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New Behavioral Paradigms to Study Taste-Quality Generalization and Discrimination in Rats


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NEW BEHAVIORAL PARADIGM S TO STUDY TASTE-QUALITY GENERALIZATION AND DISCRIMINATION IN RATS By CONNIE LYNN GROBE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Connie L. Grobe

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This dissertation is dedicated to my brother, Robert.

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ACKNOWLEDGMENTS I thank my family, and friends. Their constant support has made it possible to achieve my goals. At the University of Florida, I have been lucky to meet and interact with some very talented people, who have each helped to shape my character along the way. I especially recognize (in chronological order) Laura Tucker, Laura Geran, Cheryl Vaughan, Shachar Amdur, Mary Clinton, Ginger Blonde, Shawn Dotson, Kathryn Saulsgiver, Anaya Mitra, and Yada Treesukosol. They have provided me with encouragement, assistance, advice, and countless other acts of kindness that I will never fully understand, but always deeply appreciate. I thank the entire faculty in the Behavioral Neuroscience area for contributing to my education. I also gratefully acknowledge the help and guidance that I received from Dr. Neil Rowland, my M.S. advisor and Dr. Alan Spector, my dissertation advisor. They have each provided me with a perspective on science that I will continue to value and can only hope to incorporate into my own future scientific approach. Finally, I cannot thank my husband, Justin Grobe, enough for his unwavering love, patience, and encouragement, but especially for his exemplary scholarship. iv

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TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................iv LIST OF TABLES .............................................................................................................ix LIST OF FIGURES ...........................................................................................................xi ABSTRACT .....................................................................................................................xiii CHAPTER 1 LITERATURE REVIEW.............................................................................................1 Introduction...................................................................................................................1 Domains of Taste..........................................................................................................3 Sensory Discriminative Domain............................................................................4 Affective Domain..................................................................................................4 Physiological Domain...........................................................................................5 Taste Quality.................................................................................................................5 Animal Models Used to Study Taste Quality...............................................................6 Discrimination Tasks.............................................................................................6 Generalization Tasks...........................................................................................10 Ideal Psychophysical Task..................................................................................13 Importance of Psychophysical Analysis in Animal Models.......................................13 Argument for the Development of Psychophysical Tasks.........................................14 2 RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO SAMPLE TASK USING ONLY TASTE STIMULI.................................................16 Background.................................................................................................................16 Method........................................................................................................................17 Animals................................................................................................................17 Apparatus.............................................................................................................17 Stimuli.................................................................................................................18 Surgery................................................................................................................19 Training and Testing Phases................................................................................20 Spout training...............................................................................................20 Side training.................................................................................................21 Alternation....................................................................................................21 v

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Discrimination training I-II..........................................................................21 Trial structure (final parameters)..................................................................22 Testing.................................................................................................................23 Adjustments to Testing Parameters.....................................................................23 Statistical Analyses..............................................................................................24 Results.........................................................................................................................24 Overall Performance............................................................................................24 Performance on Same Trials...............................................................................25 Performance on Different Trials..........................................................................25 Performance on Same Trials versus Different Trials..........................................25 Discussion...................................................................................................................26 3 A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION IN RATS.....................................................................................................................35 Introduction.................................................................................................................35 Experiment I...............................................................................................................38 Method.................................................................................................................38 Subjects........................................................................................................38 Training Stimuli...........................................................................................38 Procedure......................................................................................................38 Data Analysis.......................................................................................................40 Results.................................................................................................................41 Discussion............................................................................................................42 Experiment II..............................................................................................................43 Method.................................................................................................................44 Subjects........................................................................................................44 Apparatus.....................................................................................................44 Task overview..............................................................................................44 Stimuli..........................................................................................................45 Groups..........................................................................................................45 Trial structure...............................................................................................45 Training........................................................................................................46 Test compounds............................................................................................48 Retraining water as a comparison stimulus..................................................49 Negative control test.....................................................................................49 Data analysis................................................................................................49 Generalization score.....................................................................................50 Results.................................................................................................................51 Novel concentrations: NaCl.........................................................................52 Novel concentrations: Sucrose.....................................................................53 Novel concentrations: Quinine.....................................................................53 Novel concentrations: Citric acid.................................................................54 Mixtures between NaCl and sucrose: 1.07 M NaCl + 0.421 M sucrose......55 Mixtures between NaCl and Sucrose: 1.07 M NaCl + 0.077 M Sucrose.....55 Mixtures between NaCl and Sucrose: 0.376 M NaCl + 0.421 M Sucrose...56 Novel test compound: Water........................................................................56 vi

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Retraining water as a comparison stimulus..................................................57 Negative control session...............................................................................58 Discussion............................................................................................................58 4 APPLICATION OF A NEW BEHAVIORAL PARADIGM TO ASSESS TASTE QUALITY GENERALIZATION...............................................................................76 Introduction.................................................................................................................76 Method........................................................................................................................76 Subjects................................................................................................................76 Apparatus.............................................................................................................77 Task Overview.....................................................................................................77 Stimuli.................................................................................................................77 Trial Structure......................................................................................................78 Training...............................................................................................................78 Spout training...............................................................................................79 Side training.................................................................................................79 Alternation....................................................................................................79 Discrimination training I-III.........................................................................80 Test Compounds..................................................................................................81 Data Analysis.......................................................................................................81 Results.........................................................................................................................82 Test Stimulus: Sodium Gluconate.......................................................................82 0.376 M sodium gluconate...........................................................................82 0.668 M sodium gluconate...........................................................................83 Test Stimulus: Denatonium.................................................................................83 0.131 mM denatonium.................................................................................83 0.360 mM denatonium.................................................................................84 Test Stimulus: Maltose........................................................................................84 0.077 M maltose...........................................................................................84 0.148 M maltose...........................................................................................85 Test Stimulus: Potassium Chloride (KCl)...........................................................86 0.376 M KCl.................................................................................................86 0.668 M KCl.................................................................................................86 Test Stimulus: Monosodium Glutamate..............................................................87 0.077 M MSG...............................................................................................87 0.148 M MSG...............................................................................................87 Test Stimulus: Fructose.......................................................................................88 0.077 M fructose..........................................................................................88 0.148 M fructose..........................................................................................89 Performance of Water Group..............................................................................89 Discussion...................................................................................................................91 Sodium Gluconate...............................................................................................91 Denatonium.........................................................................................................92 Maltose................................................................................................................93 Potassium Chloride..............................................................................................94 Monosodium Glutamate......................................................................................95 vii

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Fructose...............................................................................................................97 5 GENERAL DISCUSSION.......................................................................................115 Introduction...............................................................................................................115 Delayed Match/Non-Match to Sample.....................................................................115 Novel Taste Quality Generalization.........................................................................118 Future Validation of the Procedure...................................................................122 Potential Uses of the New Generalization Procedure........................................124 Neurobiological applications......................................................................124 Behavioral data support analytic processing rather than synthetic............128 Perspectives..............................................................................................................129 LIST OF REFERENCES.................................................................................................130 BIOGRAPHICAL SKETCH...........................................................................................139 viii

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LIST OF TABLES Table page 3-1. Training compounds selected from Experiment I.....................................................62 3-2. Experimental groups..................................................................................................62 3-3. Results from one-sample t-tests for a novel concentration of NaCl..........................62 3-4. Performance to training stimuli during novel NaCl testing.......................................62 3-5. Results from one-sample t-tests for a novel concentration of sucrose......................63 3-6. Performance to training stimuli during novel sucrose testing...................................63 3-7. Results from one-sample t-tests for a novel concentration of quinine.......................63 3-8. Performance to training stimuli during novel quinine testing...................................64 3-9. Results from one-sample t-tests for a novel concentration of citric acid..................64 3-10. Performance to training stimuli during novel citric acid testing.............................64 3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose....................65 3-12. Performance to training stimuli during high NaCl + high sucrose testing..............65 3-13. Results from one-sample t-tests for 1.07 M NaCl + 0.077 M sucrose....................65 3-14. Performance to training stimuli during high NaCl + low sucrose testing...............66 3-15. Results from one-sample t-tests for 0.376 M NaCl + 0.421 M sucrose..................66 3-16. Performance to training stimuli during low NaCl + high sucrose testing...............67 3-17. Results from separate one-sample t-tests for water.................................................67 3-18. Performance to training stimuli during water testing..............................................67 4-1. Overview of experimental groups.............................................................................99 4-2. Training schedule for N, S, Q, and C groups............................................................99 ix

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4-3. Training parameters for W group............................................................................100 4-4. Results from one-sample t-tests for 0.376 M NaGluconate....................................101 4-5. Results from one-sample t-tests for 0.668 M NaGluconate....................................101 4-6. Performance to training stimuli during sodium gluconate testing...........................101 4-7. Results from one-sample t-tests for 0.131 mM denatonium...................................102 4-8. Results from one-sample t-tests for 0.360 mM denatonium...................................102 4-9. Performance to training stimuli during denatonium testing....................................102 4-10. Results from one-sample t-tests for 0.077 M maltose...........................................103 4-11. Results from one-sample t-tests for 0.148 M maltose...........................................103 4-12. Performance to training stimuli during maltose testing.........................................103 4-13. Table of t-test statistics for 0.376 M KCl..............................................................104 4-14. Table of t-test statistics for 0.668 M KCl..............................................................104 4-15. Performance to training stimuli during KCl testing..............................................104 4-16. Table of t-test statistics for 0.077 M MSG............................................................105 4-17. Table of t-test statistics for 0.148 M MSG............................................................105 4-18. Performance to training stimuli during MSG testing............................................105 4-19. Table of t-test statistics for 0.077 M fructose........................................................106 4-20. Table of t-test statistics for 0.148 M fructose........................................................106 4-21. Performance to training stimuli during fructose testing........................................106 4-22. Performance to training stimuli for W group during dt3-5 through dt3-8.............107 x

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LIST OF FIGURES Figure page 2-1. Trial structure for DMTS/DNMTS (same/different) task.........................................30 2-2. The mean overall performance to all trial types is shown.........................................31 2-3. Mean performance to same trials...............................................................................32 2-4. Mean overall performance to different trials.............................................................33 2-5. Mean performance on same versus different trials....................................................34 3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test.............................68 3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test..........................68 3-3. Mean (n=8) unconditioned licking to quinine in a brief access test..........................69 3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test......................69 3-5. An overview of the trial structure..............................................................................70 3-6. The generalization profile obtained when 0.847 M NaCl was used as a test compound.................................................................................................................71 3-7. The generalization profile obtained when 0.068 M sucrose was used as a test compound.................................................................................................................71 3-8. The generalization profile obtained when 0.546 mM quinine was used as a test compound.................................................................................................................72 3-9. The generalization profile obtained when 42.56 mM citric acid was used as a test compound.................................................................................................................72 3-10. The generalization profile obtained when 1.07 M NaCl + 0.421 M sucrose was used as a test stimulus..............................................................................................73 3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose was used as a test stimulus..............................................................................................73 xi

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3-12. The generalization profile obtained when 0.376 M NaCl + 0.421 M sucrose was used as a test stimulus..............................................................................................74 3-13. The generalization profile obtained when water was used as a test stimulus..........74 4-1. Profile for 0.376 M NaGluconate............................................................................108 4-2. Profile for 0.668 M NaGluconate............................................................................108 4-3. Profile for 0.131 mM denatonium...........................................................................109 4-4. Profile for 0.360 mM denatonium...........................................................................109 4-5. Profile for 0.077 M maltose.....................................................................................110 4-6. Profile for 0.148 M maltose.....................................................................................110 4-7. Profile for 0.376 M KCl..........................................................................................111 4-8. Profile for 0.668 M KCl..........................................................................................111 4-9. Profile for 0.077 M MSG........................................................................................112 4-10. Profile for 0.148 M MSG......................................................................................112 4-11. Profile for 0.077 M fructose..................................................................................113 4-12. Profile for 0.148 M fructose..................................................................................113 4-13. Summary of performance for W group during training with water and quinine...114 4-14. Diagram outlining two possibilities for the level (peripheral or central) at which convergence of taste signal processing leading to the same behavioral output might occur.............................................................................................................114 xii

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy NEW BEHAVIORAL PARADIGMS TO STUDY TASTE-QUALITY GENERALIZATION AND DISCRIMINATION IN RATS By Connie Lynn Grobe August 2006 Chair: Alan C. Spector Major Department: Psychology Questions regarding the nature of perceivable taste qualities remain: Is taste quality perception analytic or synthetic? Specifically, are tastes comprised of mixtures of a discrete number of basic qualities? Currently, there are no appropriate animal models that allow repeated assessments of the qualitative features of taste stimuli. Because it is not possible to directly measure taste perception in animals, such sensory experiences must be inferred on the basis of results from specially designed behavioral tasks. Here, an operant-conditioning based behavioral paradigm was used to train rats to taste two samples within a trial and then to make one response if presentations of the taste stimuli (NaCl or sucrose) matched and another response if they did not match. Rats performed similarly on matching and non-matching trials. Overall performance reached an asymptote at ~74%. This approach could provide a means of testing discrimination and generalization as well as exploring the temporal capacities of short term memory in the taste system. xiii

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Another study used operant techniques to train four groups of rats to distinguish the taste quality of a single (standard) compound representing one of the putative four basic tastes (salty, sweet, sour, bitter) from compounds representing the three other taste qualities (comparisons). Prototypical stimuli were used to represent basic tastes (NaCl, sucrose, citric acid, quinine). This task was then used to quantify how animals in each group generalized their responses when presented with novel taste stimuli, providing a way to assess how NaCl-like, sucrose-like, citric acid-like and quinine-like the quality of the solution was. Stimulus control of training compounds was maintained at high levels, and behavioral responses to test stimuli generalized in predictable ways, providing a non-invasive method for repeatedly assessing taste quality in the same animals. Interestingly, the profile of monosodium glutamate is both NaCl-like and sucrose-like. Overall, results suggest that taste processing is analytic. Additionally, these paradigms could provide a functional context to interpret the outcomes of anatomical, pharmacological, and genetic manipulations of the gustatory system. They are also compatible with existing techniques that are crucial for linking neural activity with behavior, which is essential for understanding gustatory processing. xiv

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CHAPTER 1 LITERATURE REVIEW Introduction Many questions remain concerning the organization of the gustatory system and the neural mechanisms underlying taste function. How are tastes detected in the mouth and appropriate signals sent to the brain? Specifically, how are the relevant features of a chemical stimulus coded by the nervous system? What portions of the gustatory pathway are necessary for the maintenance of particular functions, like taste intensity discrimination or taste quality detection and/or discrimination? Before one can approach these questions, it is important to resolve fundamental concepts concerning the perceptual characteristics of taste stimuli in the animal models chosen to study issues pertaining to taste. For example, it is not fully known whether rats, a commonly used animal model, perceive taste stimuli as categorical or falling along a continuum of possible qualities. These two possibilities represent theoretically opposing viewpoints of how gustatory processing occurs: The analytic view and the synthetic view, respectively. Erickson (1968) stated that color vision is a synthetic system whereas audition is an analytic system. The difference being that the synthetic system appears to involve the same set of neurons and the analytic system appears to involve different sets (Erickson, 1968). Erickson (1968) further pointed out that this key difference might be at the heart of the debate about whether signals regarding taste quality are processed through devoted labeled-lines or in an across-fiber pattern. 1

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2 Therefore, the purpose of the current experiments was for the development and application of psychophysical tasks that may yield an answer to the question of whether there might be a few taste primaries or an indefinite number of them. At issue is whether a few discrete categories of taste quality are sufficient to encompass all taste experiences in our animal model (Sprague-Dawley rat) or whether there is a continuum of possible taste perceptions. The aim of the first experiment was to design a versatile task that would provide insight into the ability of rats to discriminate differences between 2 stimuli, whether of the same compound (intensity discrimination) or between different compounds (quality discrimination). A second goal of the first experiment was to determine if the same protocol could be used to investigate the temporal properties of short-term memory for taste solutions. The overall goal of the second and third experiments was to examine whether rats can reliably discriminate taste compounds thought to fall into different qualitative perceptual classes, and whether they will reliably categorize novel stimuli as possessing characteristics similar to the training stimuli. The existence of such a paradigm would offer researchers the opportunity to observe the effects that manipulations made to the gustatory system have on performance in a behavioral task that was specifically aimed at measuring taste quality identification. In addition, the task could be used to gain insight into the gustatory perceptual experience of the animal generated by novel taste compounds. In order to conduct these experiments, it was assumed that rats treat taste stimuli as being composed of (at minimum) the same 4 basic qualitative classes that humans report

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3 perceptually: salty, sweet, sour, and bitter. Nowlis, Frank, and Pfaffman (1980) found evidence supporting this assumption using a behavioral approach. Moreover, work examining the peripheral transduction mechanisms in rodents to prototypical compounds, those identified by humans as representing the four basic tastes, suggest that animals may have receptors devoted to the four taste qualities (Chandrashekar et al., 2000; Gilbertson & Boughter, 2003; Scott & Giza, 1990; Zhang et al., 2003; Zhao et al., 2003). A controversial fifth taste quality, referred to as umami (Yamaguchi, 1991), has been identified in the literature and is described as the taste quality associated with a savory or delicious sensation in humans. Support for the existence of umami taste in rodents is mixed; some sources indicate that the taste of monosodium glutamate (MSG) (the prototypical compound for the umami taste) generalizes to sucrose and NaCl, sweet and salty, respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data suggest that rats can nonetheless discriminate MSG from sucrose even when the contribution of the sodium ion is reduced (Heyer, Taylor-Burds, Mitzelfelt & Delay, 2004). The strategy of using representative compounds from the classic 4 basic tastes does not detract from the possibility that there could be more taste qualities; in fact, it could even provide support for such a notion. How does one measure taste function in non-human animals? To appreciate this, it is important to understand each of the identified taste domains and how they may be measured in animals (including humans). Domains of Taste The functional aspects of taste can be classified into at least three broad domains: sensory-discriminative, affective, and physiological (see Spector, 2003b, for review). The discriminative domain deals with identification of a stimulus, and the affective

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4 domain refers to the hedonic aspects of a compound, whereas the physiological domain consists of the physiological reflexes that a stimulus elicits. Each of the three domains describes a different facet of taste function, and possibly represents different aspects related to ingestive behavior. Sensory Discriminative Domain Briefly, sensory-discriminative function, the identification of a stimulus, can be dissociated from the affective/hedonic domain by use of several different operant and classical conditioning procedures aimed at measuring both detection thresholds and quality discriminations in animals (Spector, 2003b). These procedures do not rely on the hedonic aspects of the taste solution to drive responses because the taste serves as a signal for other reinforcing or punishing events. Consequently, the inherent motivational properties of the stimulus are irrelevant in the animals identification of the stimulus. Affective Domain Briefly, the affective domain refers to the hedonic attributes of taste stimuli (i.e., the palatability of a compound). The most commonly used methods to describe the affective responses of animals regarding taste compounds include operant tasks aimed at assessing appetitive/avoidance behavior and those aimed at measuring consummatory responses, which are the reflex-like behavior stimulated by a tastant contacting its sensory receptors (Spector, 2003a). The two-bottle intake test, a variation of it termed the brief-access task, and various operant response measurements (e.g., progressive ratio breakpoints, rates of responding) have been used to quantify the reinforcement efficacy of a taste stimulus (Clark & Bernstein, 2006; Guttman, 1953; Hodos, 1961; Reilly, 1999; Sclafani, 2006; Sclafani & Ackroff, 2003; Starr & Rowland, 2006). Consummatory responses, on the other hand, have been measured through use of the taste reactivity

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5 paradigm (Grill & Berridge, 1985; Grill et al., 1987), which is a procedure that involves the quantification of oromotor reflexes elicited by taste stimuli infused directly into the oral cavity. Physiological Domain The physiological domain, often referred to as cephalic phase responses (e.g., Berthoud, et al., 1981; Grill, Berridge, & Ganster, 1984; Mattes, 1997; Pavlov, 1902; Powley, 1977; Spector 2000), consists mainly of salivation and other predigestive responses that are elicited by taste stimuli. The increased salivation to food/fluids and other physiological reflexes related to contact with taste receptors are proposed to be adaptive as they likely contribute both to digestion/assimilation of food and protection of the oral epithelium (e.g., salivation) (Spector, 2000). Taste Quality The quality of a taste falls under the rubric of sensory-discriminative function. According to Bartoshuk (1978), Aristotle was first to suggest that the taste of all foods and fluids was a combination of only a few discrete perceptual qualities. He suggested that there were 7 basic tastes: sweet, bitter, sour, salty, astringent, pungent, and harsh (Bartoshuk, 1978). It was not until 1927, however, that Hans Henning formally asserted that the four basic tastes (salty, sweet, sour, and bitter) can be conceived as representing the corners of a tetrahedron with combinations of two qualities along the edges, and combinations of three on the face (Bartoshuk, 1978). This idea has been commonly accepted despite occasional evidence suggesting additional qualities exist; the most notable is the claim of a fifth quality, the umami taste which is said to arise from glutamate salts and is described as savory by humans (Galindo-Cuspinera, & Breslin, 2006; Schiffman, 2000; Yamaguchi, 1991). Support for the existence of umami taste

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6 in rodents, however, is mixed with some sources indicating that the taste of MSG (the prototypical compound for the umami taste) generalizes to sucrose and NaCl, sweet and salty, respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data suggest that rodents can nonetheless discriminate MSG from sucrose (Ninomiya & Funakoshi, 1989a) even when the contribution of the sodium ion is reduced (Heyer, Taylor-Burds, Mitzelfelt & Delay, 2004). Animal Models Used to Study Taste Quality Many researchers assume that the same basic taste qualities that are identified by humans also extend to other animals. Support for this statement is based on the fact that animals respond to prototypical compounds putatively representing the 4 basic tastes as would be expected. That is, animals ingest and avoid taste solutions in a manner that appears similar to human descriptions of pleasantness and aversion. Suppression of intake of a solution, however, does not necessarily indicate qualitative similarity to other avoided compounds in sensory-discriminative terms. In other words, when an animal avoids two compounds equally, there is no way of knowing whether the animal also perceives them as possessing the same taste quality. For example, an animal might avoid drinking very concentrated NaCl to the same extent as it avoids drinking a quinine solution, but data in animals and humans suggest that the two compounds are qualitatively dissimilar. Accordingly, other methods are necessary for inferences on taste quality in nonhuman animals to be established. Indeed, that is a primary theme of this dissertation. Discrimination Tasks Operant discrimination procedures have been useful for determining whether two compounds are perceptually distinct. If an animal cannot discriminate between two

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7 different solutions, then it is plausible that both give rise to a perceptually identical experience (Spector, 2003a). Alternatively, if an animal can reliably discriminate two compounds from one another, then there must be some identifiable cue (e.g., differential neural signals generated by the two stimuli) that can be used by the animal to guide its behavior accordingly. It is critical in these experimental designs that intensity cues be minimized so that discriminative responding comes under the explicit control of taste quality. For example, it is known that a rat can discriminate a relatively lower concentration of NaCl from a higher concentration of NaCl (Colbert, Garcea, & Spector, 2004), but this does not necessarily imply that the taste quality of the sensation between strong and weak NaCl solutions is different. Therefore, when conducting studies of quality discrimination, it is important to use a range of concentrations of the respective training stimuli so as to render intensity a relatively irrelevant cue (Spector, 2003a; Spector & Grill, 1992; Spector et al., 1996, 1997; St. John et al., 1995, 1997, 1998). Spector (2003b) has identified important assumptions associated with this strategy: the selected range of concentrations must have overlapping intensities and the relevant taste quality of each compound delivered is assumed to remain constant across the concentration range tested. If the first assumption were not met and two compounds were of the same quality but all of the concentrations of one compound were perceived as weaker than all of the concentrations of the other compound, then the animals would likely be able to discriminate between the two compounds based on the differences in intensity. One could identify the basis for such a discrimination, for example, if rats performed well when lower concentrations of the weak compound were added to the test stimulus array, but performed poorly to additional low concentrations of the strong

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8 compound. Conversely, the opposite would be true. That is, if greater concentrations were included in the discrimination task for both compounds, then as the weaker-tasting one became more salient, performance would decrease because the rats would incorrectly respond as if it were the stronger-tasting compound. In contrast, performance would be expected to improve for the stronger-tasting compound because a greater concentration would only serve to distinguish it more from the weaker-tasting compound. A typical stimulus discrimination paradigm involves training an animal to make one response after tasting one compound and to make a different response after tasting a different compound. As stated earlier, it is best when the concentration of each compound is varied to render intensity an irrelevant cue, which should make taste quality the salient signal. Typically, the animal is water deprived (< 24 h) to encourage sampling, and correct responses are reinforced with brief access to water. At least two studies have been published suggesting the occurrence of perceptual identity as evidenced by rats being unable to discriminate between two different taste stimuli. In one study, Spector and Kopka (2002) found that rats could not discriminate quinine hydrochloride (a prototypical bitter compound) from denatonium benzoate (a substance that rats also avoid consuming and that humans report as bitter). The same rats were able, however, to discriminate quinine from KCl (judged to be a complex bitter-sour-salt by humans), and NaCl from KCl. Interestingly, the rats appeared to be able to substitute denatonium for quinine after being trained to discriminate quinine from KCl, signifying the two compounds were similar. These results support the claim that quinine and denatonium likely generate a unitary qualitative percept in rats.

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9 The other study which demonstrates perceptual identity between taste compounds in rodent models is provided by Spector, Guagliardo, and St. John (1996). In that study, amiloride, an epithelial sodium channel blocker, was used to remove the specific NaCl taste cues necessary to discriminate NaCl and KCl. With application of 100 M amiloride, the remaining gustatory cues were not sufficient for rats to distinguish between the two salts and they performed at chance levels in a discrimination task. Moreover, an analysis of the errors in responding showed that mistakes primarily occurred on NaCl + amiloride trials. This observation suggests that the rats responded as if NaCl + amiloride was perceptually similar to KCl. Adding support to the hypothesis that amiloride changes the perceptual taste qualities of NaCl to be more similar to KCl are data from Hill, Formaker, & White (1990), showing that when NaCl, adulterated with amiloride, was used as a conditioned stimulus (see below for definition) in a conditioned taste aversion paradigm, rats generalized their aversion to non-sodium salts (specifically the halogenated salts tested) including KCl. Because there are many factors which might potentially serve as cues in a discrimination task, results from studies using this approach are more compelling when rats cannot discriminate between two compounds, provided that learning and intensity effects can be rule-out. For example, the rise and decay time of the signal may differ between two compounds that share a similar quality. Such temporal cues alone may be sufficient to allow an animal to distinguish between the stimuli in a discrimination task. Another possible signal, as mentioned earlier, may be the relative intensity of the tastants selected. If the experimenter does not know the relevant concentration ranges to include

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10 in the test stimulus panel and includes some that do not overlap in intensity, the animal may be able to use those cues to guide performance. Generalization Tasks Guttman & Kalish, (1956) are credited with associating the concept of discriminability with generalization gradients. A typical study in which a generalization gradient is obtained consists of a scenario in which appropriate responses are reinforced, when a specific training stimulus is present. Once stimulus-contingent responding is established, a generalization test is presented during which no responses are reinforced. The stimulus is varied on some physical dimension and the rate of responding is recorded. These experiments generally produce response gradients that decrease as a function of the difference between the training and test stimuli (Guttman & Kalish, 1956). This concept has been adapted for use to study similarities between taste compounds in the conditioned taste aversion (CTA) paradigm. Tapper & Halpern (1968) innovatively applied the CTA procedure to make inferences on how animals classify taste stimuli. They exposed experimental animals to radiation (2.5 min exposure of 80 r/min) 20 minutes before a scheduled session in which the rats normally consumed their daily supply of water; after the radiation, however, a novel taste compound (the conditioned stimulus; CS) was presented in place of water. This procedure resulted in a robust avoidance to the CS, evidenced by the fact that after the pairing occurred, rats consumed less of the CS upon subsequent re-exposure to the tastant. In the procedure, Tapper & Halpern (1968) assumed: i) the [CS] becomes the quality standard against which the animals compare other solutions; ii) the test solutions will be aversive, that is, associated with the [CS], as long as their taste to the animal is qualitatively similar to the [CS], iii)

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11 the magnitude of rejection indicates the degree of similarity in taste between the test solution and the [CS]. By means of multiple cross-generalization pairings, they could construct functions of aversion with which to compare compounds. Their rationale for inferring that two compounds were of the same quality was based on the assumption that similar generalization profiles would emerge for the respective stimuli (Tapper & Halpern, 1968). This approach was comprehensively extended later by Nowlis, Pfaffmann, and Frank (1980). They conditioned aversions to a large number of compounds in hamsters and rats and then measured intake to the four prototypical taste compounds, NaCl, sucrose, HCl, and quinine, which served as test stimuli. As such, like Tapper and Halpern (1968), it was assumed that the response profiles obtained related to the qualitative properties of the prototypical taste compounds, thus allowing them to make conclusions about the degree to which, a compound was sucrose-like, NaCl-like, HCl-like, and quinine-like. With some exceptions, these data became the basis for many to consider that rodents likely share the same perceptual taste experience as humans do. Later, the same technique was applied to the study of mixtures (Frank, Formaker, & Hettinger, 2003; Smith & Theodore, 1984), and researchers showed that rats could identify the CS in a mixture in a concentration-dependent manner. There are some limitations associated with this approach, which include effects of stimulus familiarity, extinction, concentration, and stimulus hedonics. First, the CTA procedure is only successful if the CS is novel to the animal, thus limiting the choice of CSs to unfamiliar compounds. Additionally, typically only one CS is used per experimental group, which means that each concentration of the compound included in

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12 the study also requires a devoted set of animals. It follows that this substantially increases the number of animals required for a comprehensive study of taste quality generalization. In addition, an inclusive design would require the researcher to also test for cross-generalization of each of the concentrations selected, and therefore, it is necessary to use a large number of animals. A second limitation of the CTA approach relates to the strength of conditioning. Testing occurs in extinction, meaning that the animal does not experience the unconditioned-stimulus-induced consequences previously paired with intake, and so the effects of learning can diminish over time. Often the strength of the conditioning is reassessed periodically, and the avoidance to the CS indeed lessens. This complicates interpretation of results and limits the number of potential test stimuli (Nowlis, Frank, and Pfaffmann, 1980). A third limitation of the CTA procedure is associated with stimulus intensity dynamism, which must be carefully considered in the interpretation of generalization profiles. Intensity dynamism refers to the observation that conditioning to a CS will generalize similarly to all higher concentrations of that compound, rather than as an inverted-V gradient, peaking at the CS, as might be expected (see Guttman & Kalish, 1968; Hull, 1949). In other words, there is a steep gradient beginning at some concentration below the CS, but the behavioral profile obtained reveals that increasing the intensity of the compound results in a greater or at least similar conditioned response. For example, if conditioning occurred to 0.05 M NaCl it would generalize to higher concentrations, including 0.5 M NaCl but conditioning to 0.5 M NaCl might not

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13 generalize to the lower concentration. That is, cross-generalization does not necessarily occur between high and low concentrations of the same compound. Finally, the inherent affective properties of a taste stimulus can influence the interpretation of CTA results. If a solution is unconditionally aversive, an animal will not readily consume much, if any, of the compound. This could compromise both the effectiveness of the pairing and the ability to measure behavior (i.e., floor effect). Therefore, some compounds, which are preferably ingested, better lend themselves to a procedure like CTA. In fact, this is a key problem facing those who study taste classification. Some compounds, especially bitter and sour solutions are not readily consumed by rats and attempts to incorporate them into CTA designs can result in problems associated with floor effects. That is, it is difficult to quantify changes in intake for an experimental versus control group when intakes of the taste stimuli for both groups are low. Ideal Psychophysical Task An ideal psychophysical task would have the following characteristics: it would 1) be compatible with assessing discriminability and generalization within the same animals, 2) allow for repeated test (probe) trials within the same animals, 3) yield clear, interpretable results, 4) be highly replicable within and between animals (i.e., have little variance in responses), and 5) circumvent the potential confounding of stimulus intensity. Importance of Psychophysical Analysis in Animal Models Advances in our understanding of taste function can be optimally achieved through a combination of experimental approaches. Arguably, it is the innovation of rigorous behavioral techniques that facilitates the confirmation or refutation of predictions about gustatory function that are based on more reduced levels of analysis (i.e.,

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14 electrophysiology, molecular biology, etc.). In addition, carefully executed psychophysical experiments produce results that generate new hypotheses regarding how the gustatory system is organized. Psychophysical tasks, though time consuming, provide invaluable data on the sensory capacities of both humans and non-human animals. Psychophysical analysis of non-verbal subjects is challenging but can be achieved through the use of operant and classical conditioning procedures. Chief among the benefits of using a psychophysical approach with non-human animals is that invasive procedures, in which the gustatory system can be manipulated, are possible. Taste function is complex; therefore, the design and application of a variety of psychophysical measures is necessary to obtain a comprehensive assessment of function. The failure to develop appropriate tasks can lead to misguided conclusions. For example, the two-bottle preference test has been, and continues to be, the most common behavioral measure of taste responsiveness in animals. This measure, however, only assesses the motivational characteristics of a taste stimulus. Moreover, postingestive events can influence the behavior. Certainly, the use of this procedure masked for many years the understanding of the contribution of gustatory nerves in the processing of taste input (e.g., Pfaffmann, 1952; Richter, 1939; see Spector, 2003a for discussion). Argument for the Development of Psychophysical Tasks The use of appropriate behavioral procedures directed at measuring taste function in animal models has been indispensable in the analysis of the neural organization of the gustatory system (e.g., Flynn, Grill, Schulkin, & Norgren, 1991; Flynn, Grill, Schwartz, & Norgren, 1991; Kopka & Spector, 2001; Kopka, Geran, & Spector, 2000; Slotnick, Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990; Spector & Grill,

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15 1992; St. John, Markison, & Spector, 1995; Shimura, Grigson, & Norgren, 1997). It follows, therefore, that the development of new behavioral paradigms that are aimed at yet unexplored aspects of gustatory function promise to lead to further important discoveries.

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CHAPTER 2 RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO SAMPLE TASK USING ONLY TASTE STIMULI Background A major goal of this project was to develop a novel behavioral task that would address whether rats can accurately assess when two samples, tasted in sequence, differ or whether they are the same. The paradigm combines two procedures, a match to sample and non-match to sample task. Potentially, such a task could be used to assess the degree of qualitative discriminability between two taste stimuli. Another possible benefit of this paradigm is that once the animal has sufficient training in the contingencies of the task, various compounds or concentrations could be added for testing. Such a procedure was introduced by Konorski, in 1959, who apparently suggested it could be used with olfactory or auditory stimuli because they both were sensory modalities which were incompatible with simultaneous delivery of test stimuli (in Shimp & Moffit, 1977). The taste system is also incompatible with simultaneous delivery of two comparison stimuli. This inherent delay between samples, as a consequence of the rat sequentially sampling two separate stimuli within a single trial, provides the opportunity to allow one to assess the properties of short-term memory processes involving taste stimuli a phenomenon that has not been previously approached. To date, only long-term memory has been studied in the taste system via conditioned taste aversion (CTA), which is not optimally designed for multiple trial analyses. 16

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17 Method Animals Nine adult male Sprague-Dawley rats weighing 555 +/20 g at the start of training were used as subjects. Two of the animals were euthanized within the first two weeks of training: one demonstrated a response bias within the first few discrimination training (see below) sessions and was removed from the study to allow for an increase in session length for the other rats, and the other rat removed his surgically implanted intraoral cannulae (see below) and thus required immediate euthanization. Therefore, seven rats served as subjects in the experiment. The rats came from Charles River (Wilmington, SC) and were maintained on Purina (5001) laboratory rat chow ad libitum (except during experimental test sessions) in a vivarium that had the lights and temperature automatically controlled. Lights were programmed to be on a 12:12 hour light:dark cycle with lights on at 0700 h, but due to an undiscovered timer malfunction the animals were in constant light during the first 110 days of training and testing. A contingency was in place so that rats would receive supplemental water if body weight decreased to 85% of the ad libitum weight calculated each week; this contingency was only necessary for one of the animals on three separate occasions. All procedures were approved by the University of Florida Institutional Animal Care and Use Committee. Apparatus In the present experiment a gustometer, which is a specially designed stimulus delivery and response measurement device, was modified from an earlier version described in detail elsewhere (Spector, Andrews-Labenski, and Letterio, 1990), and was used in training and testing. Briefly, the test chamber had two response spouts which flanked either side of a central slot through which the animal could access a sample spout

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18 controlled by a stepping motor. There were two cue lights positioned 4.2 cm above the response spouts which could be activated at the appropriate time in the trial. The response spouts served as the source for water reinforcement when the animal performed the appropriate behavior (licked the appropriate response spout after tasting a specific combination of solutions). Fluid stimuli and the water reinforcer were contained in 11 pressurized reservoirs connected to solenoid valves to regulate the amount of fluid deposited into the spout. Background masking noise was present during each session, and the test cage was enclosed in a sound-attenuating chamber housed within a dimly lit room to minimize possible extraneous cues related to stimulus delivery. A Polyethylene (PE)-100 tube, covered by a spring, was connected via a swivel to a solenoid valve which was, in turn, connected to a water reservoir. This tube was inserted through a small hole in the ceiling of the sound attenuation chamber where it was connected to an intraoral cannula implanted in the rat. This was used to provide water rinses between stimuli as described below. Stimuli All solutions were prepared daily with purified water (Elix 10; Millipore, Billerica, MA) and reagent grade chemicals, and were presented at room temperature. Initially, we attempted to use 0.1 M NaCl and 0.5 M NaCl as training stimuli, but the overall performance of the rats remained at chance. Consequently, the rats never progressed out of the training phase and it was deemed necessary to change the training stimuli after two months (35 sessions). Two solutions were used in the second phase of training: 0.1 M NaCl and 0.1 M sucrose. We reasoned that a discrimination between two compounds that are of different qualities might be easier to learn. Although we know that rats can discriminate NaCl on the basis of concentration (Colbert, Garcea, & Spector, 2004), we

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19 believed it might reduce the acquisition time if the two stimuli differed chemically and were putatively members of different perceptually qualitative classes so as to render them more distinct from each other. The initial training results with 0.1 M and 0.5 M NaCl will be ignored for the remainder of this chapter. Taste stimuli were prepared fresh daily from reagent grade chemicals (NaCl and sucrose: Fisher Scientific, Atlanta) and purified water (Elix 10; Millipore, Billerica, MA); they were presented at room temperature Surgery Rats were anesthetized with a mixture of 125 mg/kg body wt ketamine, 5 mg/kg body WT xylazine (injection given intramuscularly) and two intraoral (IO) cannulae were surgically implanted so that water could be infused directly into the mouth. The rats were placed in a surgical head holder and an incision was made along the midline of the scalp. The fascia was cleared and four small machine screws were inserted into holes drilled into the skull. The rat was then removed from the head holder and placed in a supine position. The blunt end of a 19g needle shaft was attached to the opposite end of heat-flared PE-100 tubing. A small Teflon washer was slipped onto the cannulae and placed against the heat-flared end. The beveled end of the needle was then placed between the cheek and gum, anterolateral to the first upper molar on either side of the mouth, and the needle was pushed through the tissue in a trajectory that passed beneath the zygomatic arch close to the skull until the Teflon washer and heat-flared end of PE tubing rested against the roof of the mouth lateral to the maxillary molars. The needle was separated from the PE tubing, the excess was trimmed, and a blunt piece (~10 mm) of 19 gauge stainless steel tubing, with a bead of solder attached, was securely fitted into the PE tubing. Both cannulae were placed in the same manner. Once in place, dental acrylic

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20 was added so that it created a mound over the screws and secured the cannulae (PE tubing + 19 G stainless steel tubing with bead of solder for extra anchoring) firmly in place. All rats were injected with a prophylactic dose of penicillin G Procaine suspension (30,000 units, s.c.) and the analgesic ketorolac tromethamine (2 mg/kg body mass, s.c.) immediately before surgery and on the following 3 days. At least three months passed before animals began training. The intraoral cannulae were cleaned out every day by passing a smaller diameter (polyethylene-10) tubing through the cannulae until it exited into the oral cavity. The intra-oral cannulae were implanted so that water could be infused into the oral cavity between taste samples in order to reduce the potential for adaptation to occur to the first stimulus in the pair. Training and Testing Phases Training and testing sessions took place Monday through Friday of each week during the regularly scheduled lights-on phase. Rats were water restricted beginning Sunday night and received all daily fluid within the session. At the end of the last session on Friday, water bottles were returned to the home cages until the following Sunday. Spout training The rats had access to only one spout (either the sample spout, the left response spout, or the right response spout) and each spout was connected to a reservoir that contained water. The purpose of this phase was to train the rats to approach and gain familiarity with getting fluid from each of the spouts. Eventually, the sample spout would contain a taste stimulus and only the response spouts would contain water. The rats had to learn to lick from the sample spout and then select one of the response spouts

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21 by licking it. There were a total of 6 days of spout training so that the rats experienced two sessions with each spout. Side training Only one trial type was presented within a given session during side training. If the rats were trained with same trials then, the trials within the session alternated between 1) 0.1 M NaCl followed by 0.1 M NaCl, and 2) 0.1 M sucrose followed by 0.1 M sucrose. During the next session, the rats received only different trials in which the first sample differed from the second (0.1 M NaCl followed by 0.1 M sucrose or 0.1 M sucrose followed by 0.1 M NaCl). After sampling, rats had 180 s (limited hold period) during which they were required to respond. If they made the correct response, they had limited access to water (20 licks or 10 s, whichever occurred first). Side training lasted a total of 4 days. Alternation During alternation training, the rats started out with a single trial type (either same or different). Upon completion of a set criterion of correct responses, the program automatically switched to delivery of the opposite trial type. The correct responses did not have to be consecutive. The limited hold was changed from 180 s to 15 s. Additionally, if the rat failed to initiate the second sample within 15 s of the spout becoming available, the trial terminated and punishment (timeout) was delivered. During the decision phase, if a rat failed to make any response, or made the incorrect response, a 10-s timeout was presented. Discrimination training I-II Trials were delivered in a block with a random pattern selected by the computer program. Therefore, the rats had no indication from the prior trial, which solutions would

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22 be offered on the current trial. The block size was 8; consequently, every trial was repeated twice within the block before a new block of randomized presentations occurred. Reinforcement licks were changed from 20 to 25, and timeout increased to 20 s during this phase. Session length was increased to 50 min. Trial structure (final parameters) During the 65-min test session, each rat was allowed to complete as many trials as possible within the time allotted. Each trial (see Figure 2-1) consisted of six different phases: sample 1, inter-stimulus interval, sample 2, decision, consequence, and inter-trial interval. The sample phase began when the rat made contact with the dry sample spout and initiated licking. The rat was required to lick the dry drinking spout twice within 250 ms, upon which the shaft of the drinking spout was filled with the stimulus and each subsequent lick resulted in an additional deposit of 5 l into the fluid column. The rat was allowed 3 s access to the stimulus or five additional licks, whichever came first. A 6-s interstimulus delay followed the first sample during which 30l of fluid was infused into the mouth through the left intraoral cannula. Additionally, during this inter-stimulus interval, the sample spout was rotated over a funnel, rinsed with purified water, and air-dried in preparation for the second sample, which followed the same initiation requirements as stated above. If the rat failed to initiate the second sample within 2 s of the spout becoming available, the spout rotated away from the access port and the trial moved immediately into the consequence phase during which the rat received a timeout. In a trial in which the rat properly initiated both samples, the houselights in the gustometer were turned off and the cue lights above each lever were illuminated, signaling the start of the decision phase. Concurrently, the sample spout was rotated out of position so that it could no longer be accessed. During the decision phase, rats were

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23 allowed a prescribed period of time (5 s during the testing phase, referred to as the limited hold) to respond by licking the correct response spout. If the correct spout was licked, the houselights were reactivated and the rat had the opportunity to receive 10 s or 40 licks access to water, whichever came first. If the incorrect spout was selected or no response was made within the limited hold period, the cue lights were extinguished and the rat was given a 40-s timeout, during which fluid was unavailable. The trial terminated with a 48-s intertrial interval, during which all lights were off until the next trial began. Testing Testing began 46 sessions after the very first spout training day. The parameters were the same as those used at the end of Discrimination Training II. Adjustments to Testing Parameters Initially, the trial parameters were set during the Discrimination Training II phase. There were, however, some adjustments made to the trial parameters, during the 21-week testing phase, in an attempt to increase performance in the rats. In the fifth week, the magnitude of the reinforcer was increased from 25 licks to 40 licks. In the sixth week, the timeout was increased from 20 s to 40 s. In the twelfth week, the inter-trial interval was increased from 10 s to 48 s. In the sixteenth week, the session length was increased from 60 min to 65 min. During the seventeenth week, session length was increased from 65 min to 70 min, but was reduced again because the rats stopped responding near the end of the session. Finally, beginning in the nineteenth week, the intraoral rinses were discontinued because of problems with intraoral cannulae coming loose. Consequently, two of the rats had to be euthanized because of this problem during the nineteenth and twentieth weeks of testing.

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24 Statistical Analyses For data analyses, repeated measures analysis of variance (ANOVA), one-sample t-tests, and paired t-tests were used; Bonferroni adjustment was applied where appropriate. The mean weekly performance score for each trial type for every animal during the first eighteen weeks of testing were used in the analyses. This time period was chosen for analysis because it spanned the testing period in which all rats had intraoral rinses and it also included the weeks for which data were available from all subjects. For each of the weeks tested, every animal had six performance scores: the two same trial types, the two different trial types, and an integrated score for both same and different trials. Results Overall Performance Results are shown in Figures 2-2 through 2-5. As shown in Figure 2-2, the mean performance on the task did not exceed 75%. A repeated measures ANOVA of overall performance across testing weeks revealed that the rats performed significantly better over the 18 testing weeks analyzed (F(17,102) = 10.070, p < 0.001). Multiple one-sample t-test comparisons (null hypothesis is 50%) of performance during each week, revealed that performance was better than chance levels initially (t(6) = 2.680, p = 0.037), but a Bonferroni adjustment eliminated the statistical significance of the comparison (p = 0.658). Beginning at the third week of testing, however, both the p-value and the Bonferroni adjusted p-value revealed significant differences (all ps < 0.035), which remained so for the duration of testing (all Bonferroni adjusted ps < 0.03). Of note is a drop in performance at week 14, which was attenuated by recalibration of the apparatus to deliver the appropriate volume per lick.

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25 Performance on Same Trials A graph representing performance on same trials when the trial was NaCl-NaCl or sucrose-sucrose is shown in Figure 2-3. A repeated measures ANOVA was used to analyze performance to both types of same trials. There was a main effect of time (F(17, 102) = 4.52, p < 0.001), but no main effect of trial type and no interaction was present (both p-values > 0.2). Therefore, these data could be used to support the claim that rats may have learned to respond to the trial type regardless of what the chemical compound was. Performance on Different Trials Figure 2-4 depicts the performance to different trial types. A repeated measures ANOVA was used to compare performance on NaCl-sucrose trials to sucrose-NaCl trials. There was a main effect of time (F(17,102) = 3.290, p < 0.001), but no evidence of a main effect of trial type (p > 0.34) nor an interaction (p > 0.80). Therefore, when both compounds are presented within a trial, it does not appear to matter whether the first sample is NaCl or sucrose. Performance on Same Trials versus Different Trials Figure 2-5 shows the performance of same trials collapsed across compounds versus performance of different trials also combined together. It would appear (Figure 2-5) that rats perform better on different trials, especially initially, but a statistical analysis of the performance between same and different trials does not support such a claim. A repeated measures ANOVA comparing the 18 weeks of testing revealed a main effect of time (F(17,102) = 3.303, p < 0.001), but no effect of trial type and no interaction was present (both p-values > 0.50). Additionally, a paired t-test examining the first week of

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26 testing did not provide evidence that performance on the two trial types differed (t(6) = -2.153, p = 0.084). Discussion Results from the present study indicate that rats are able to reliably respond to two taste stimuli, separated by a 6-s delay, and sampled within a single trial, on the basis of whether they are the same or different. This is the first known report of its kind involving the taste modality. Below, the performance of the rats in this taste behavioral paradigm is placed in context with other sensory modalities. Steckler, Drinkenburg, Sahgal, and Aggleton (1998) published a series of three articles outlining the ability of rodents at, what they termed, recognition memory tasks and the underlying neuroanatomical substrates mediating such performance. Overall, they claimed that rodents can acquire these tasks, but do not typically perform at high levels. Their work, however, focuses on particular tasks using objects or spatial stimuli. It is interesting that the rats in this experiment did not perform better on the different trials. Wright and Delius (2005) reported that pigeons performing a matchingand oddity-to-sample task acquire the oddity-to-sample most rapidly. In fact, there are published data that suggest a preference for stimuli that do not match (the oddity-preference effect) (Ginsburg, 1957). There is also a previous account in which matching performance begins at or below chance (50%) and non-matching performance begins higher than chance, though these studies used pigeons and differed procedurally from the task presented here (Zentall Edwards, Moore, & Hogan, 1981). An experiment by Wallace, Steinart, Scobie, and Spear (1980) might also provide information worth considering regarding the difficulty the rats had performing at high levels in this task. In their study, rats performed better in a delayed matching task on

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27 trials that contained auditory sample stimuli rather than visual (an illuminated light) stimuli. The differences in performance between the two modalities disappeared when the delay was 0 s, but emerged when delays were longer. Perhaps taste stimuli are not as salient as stimuli from other modalities. Interestingly, Slotnick and colleagues (1993) reported that rats can learn an odor matching task and perform at very high levels (>90%) even with a delay of 10 s and presentation of a masking odor between samples. The reason for the disparity in performance between their rats and those in the present task are unknown, but there are procedural differences that may explain some of them. They used a conditional go/no-go discrimination task, which allowed many more trials and far fewer reinforcers to be delivered; that difference may have helped acquisition of the task in their case. Additionally, they used a learning set of stimuli, consisting of several different scents; thus, it is possible that experience with a variety of training stimuli would improve acquisition of the task. If such an approach was adopted with taste stimuli, it remains possible that higher levels of performance would be seen. Finally, one reason that the mean performance did not surpass 75% might be related to the ratio between the interstimulus delay and the intertrial interval. One published study, using pigeons in a visual discrimination paradigm, showed that the overall correct responding changed when the experimenter varied the ratio of interstimulus delay to intertrial interval (Roberts & Kraemer, 1982). Specifically, they tested ratios of 0.5, 2, 8, 16, 32, and 64 and reported that when the delay between trials in their experiment was the greatest, the highest levels of performance occurred (Roberts & Kraemer, 1982). In the present study, design limitations of the gustometer restricted the

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28 minimum interstimulus interval to 6 s. According to Roberts & Kraemers 1982 study, with a delay this long, it would have been optimal to use an intertrial delay of 386 s. This was not practical because either the number of trials or the number of sessions possible per day would have been dramatically reduced. In light of these findings, one might even conclude that the rats in the present experiment performed as well as would be expected; this statement is based on the fact that the subjects in Roberts & Kraemers (1982) task performed at 77% when the ISI/ITI ratio was 8, as it was in the present study. Therefore, reducing the delay or lengthening the intertrial interval would be predicted to improve performance. Perhaps in contrast to that statement, however, is evidence from Sargisson and White (2001), who showed that delay appears to become part of the training stimulus and shares a portion of discriminative control, thus lowering the delay in testing might actually decrease performance if the animal acquired the task at a higher interstimulus delay. These are potentially addressable issues empirically. It might have been insightful to include different test compounds at the end of the testing period to establish if the rats would be able to apply the concept of sameness or difference. It is possible that the performance in this test was contingent on prior training with these compounds, and the learning would not generalize to novel compounds. Thus, it would have been informative to discern whether such a transfer would have occurred. If rats acquired high levels of performance to the new set of stimuli more quickly than with the first set, then it might support the claim that rats could learn to perform the conceptual task of sameness and/or difference. We felt that the current level of performance was not sufficiently high to pursue this question. Nevertheless, in the future, especially if optimal testing parameters can be achieved to increase overall

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29 performance levels, adding a variety of test compounds should be included in the experimental design. Perhaps using multiple training compounds would actually help to establish higher levels of performance (see Slotnick et al., 1993). Overall, the results of the present study were encouraging that such a procedure could be used to study rodent discrimination ability. It certainly seems reasonable that lowering the delay between stimuli would increase the overall task performance and allow more options for discrimination (e.g., solutions that vary in intensity). Additionally, this approach also shows promise for the investigation of short-term memory in the gustatory neuraxis, which might ultimately provide information about the properties of the system, the structures involved, and how taste short-term memory compares with other forms of taste memory and memory processes involving other sensory modalities. Further development of this task could reveal properties of neurobiological mechanisms underlying certain forms of behavior. Unfortunately, because the performance of the rats on the task was not optimal for continuing in the same research direction, an alternative avenue to assess taste quality in rats was required. This, however, does not detract from the potential success of the task outlined above, but because the technical limitations could not be overcome at present, it was decided to move ahead in a different direction.

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30 Figure 2-1. Trial structure for DMTS/DNMTS (same/different) task.

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31 TOTAL PERFORMANCE40506070809010001234567891011121314151617181920212223Time (Weeks)Performance (% Correct) Figure 2-2. The mean overall performance to all trial types is shown. Performance on the task increased over the course of the experiment and became significantly different from chance during the 3 rd week of testing.

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32 NaCl-NaCl vs.Sucrose-Sucrose 40506070809010001234567891011121314151617181920212223Time (Weeks)Performance (% Correct) NaCl Same Sucrose Same Increased reincks out off IncreasengthIncreasength Figure 2-3. Mean performance to same trials. The performance of the rats did not differ depending on the stimulus that was included in the same trials. The rats improved over the course of the experiment. forcer to 40 liIncreased timeTwo weeksd session led session le

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33 NaCl-Sucrosevs.Sucrose-NaCl40506070809010001234567891011121314151617181920212223Time (Weeks)Performance (% Correct) NaCl Sucrose Sucrose NaCl Increased reinforcer to 40 licksIncreased time outTwo weeks off Increased session lengthIncreased session length Figure 2-4. Mean overall performance to different trials. Rats did not perform significantly differently on trials containing both compounds regardless of the order that the stimuli were sampled.

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34 SAME vs. DIFFERENT40506070809010001234567891011121314151617181920212223Time (Weeks)Performance (% Correct) Same Different Increased rein forcer to 40 liIncreased timeTwo weeksd session led session le cks out off IncreasengthIncreasength Figure 2-5. Mean performance on same versus different trials. There was no statistical evidence that the performance on different trials was better than performance on same trials.

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CHAPTER 3 A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION IN RATS Introduction With some exceptions, the most common method used to assess taste quality in rodent models is the conditioned taste aversion (CTA) generalization paradigm. In this procedure an animal is presented with a taste solution, which serves as the conditioned stimulus (CS), followed by induction of visceral malaise. After such a conditioning trial, animals will avoid ingesting the CS as well as compounds that are thought to possess a similar taste quality. Although this procedure has provided useful information to researchers interested in taste processing, it has some interpretive and methodological limitations. One constraint is that a novel CS must be used with each group. Thus, a large number of animals are required to comprehensively assess taste quality generalization. Another key problem is that some stimuli (e.g., quinine or HCl) are inherently avoided by rats, hence making it difficult to differentiate conditioned from unconditioned suppression of intake (e.g., floor effect). Additionally, as described in Chapter 1, stimulus intensity dynamism presents another caveat for data interpretation that must be considered. Because an animal will show an increased conditioned response to concentrations higher than the CS, it becomes important to know what the relative intensity differences elicited by different compounds might be. Finally, given that-testing occurs in extinction, the number of test stimuli and test sessions possible is restricted. For the reasons outlined above, a major goal of this experiment was to develop a 35

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36 procedure that circumvents the interpretive and methodological limitations associated with the CTA approach. Morrison (1967) introduced a unique behavioral procedure that examined taste generalization in a different manner. He trained a group of rats to press one lever if the compound sampled was 0.1 M NaCl (the standard), and another lever if the sample was 0.1 M sucrose. He trained another group of rats to discriminate that same concentration of NaCl from 0.01 M HCl. Finally, he trained a third group of rats to discriminate the 0.1 M NaCl from 0.5 mM quinine. Next, he was able to determine which response each group made when given a novel test salt. Profiles, based on whether they responded on the standard (NaCl) lever or the comparison lever, were derived. This design included all four prototypical taste compounds split across the three groups, so by placing the proportion of responses made on the comparison lever together on the same graph, it represented how sucrose-like, quinine-like, and hydrochloric acid-like the test salt was. If the profile was not any of the three, then the compound was assumed to be entirely NaCl-like. Though this approach is clever, it still has some limitations. First, within a single group, it is not intuitively obvious how to interpret a compound that is similar to neither of the two compounds. If the basic tastes are indeed different from one another, presenting a compound from a separate taste quality would not be expected to fall exclusively on either one of the training stimuli for a given group, yet a score of 0.5 would indicate that the test compound shared similarities with both. Morrison does not address this possibility (Morrison, 1967). Perhaps a better paradigm would involve training the rats to discriminate a taste compound putatively representing one quality

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37 from taste stimuli thought to represent all other proposed qualities. In this approach, the rat might learn to focus solely on one taste in order to separate the features of that compound from all others. If that occurred, then when a rat responded to a novel compound as if it were the standard, it would indicate that the test compound was similar to the standard. Secondly, Morrison (1967) used only a single concentration of each prototypical stimulus. In that study intensity was not varied to make it an irrelevant cue. Therefore, it is unknown whether the rats in Morrisons (1967) experiment were responding on the basis of intensity differences or quality differences. A better approach would be to include several concentrations of each training stimulus to decrease the relevance of intensity making taste quality the only reliable cue. The present study was undertaken to expand upon Morrisons (1967) design and to incorporate improvements to overcome his experimental shortcomings. Namely, the differences include an attempt to train the rats to focus on discriminating a single prototypical compound, representing the putative four basic taste qualities, from the remaining three. Additionally, inclusion of a broader array of concentrations of the standard stimulus is intended to circumvent problems that might occur with generalizations based on intensity features. In order to choose a broad range of concentrations that represent the prototypical stimuli and include overlapping intensities, a brief-access taste test was conducted with one prototypical representative from each of the putative 4 basic taste qualities. The goal of Experiment I was to identify concentrations of NaCl, sucrose, quinine, and citric acid that span the dynamic range of intensity, which would be used in Experiment II.

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38 Experiment I Method Subjects Eight nave, adult, male Sprague-Dawley (Charles River Breeders; Wilmington, MA) rats were used. The rats were housed individually in polycarbonate shoe-box style cages in a room where temperature, humidity, and light cycle (lights on 7am 7pm) were controlled automatically. All manipulations were performed during the light phase. The rats had ad libitum access to Purina Rat Chow (5001) in the home cage. Purified (Elix 10; Millipore, Billerica, MA) water was also available ad libitum except where indicated. All procedures were approved by the University of Florida Institutional Animal Care and Use Committee. Training Stimuli All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica, MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli consisted of six concentrations of sucrose (0.01, 0.03, 0.06, 0.1, 0.3, and 1.0 M; Fisher Scientific, Atlanta, GA), NaCl (0.03, 0.1, 0.2, 0.3, 0.5, and 1.0 M; Fisher Scientific, Atlanta, GA), citric acid (0.3, 1, 3, 10, 30, and 100 mM; Fisher Scientific, Atlanta, GA), quinine (0.01, 0.03, 0.1, 0.3, 1.0, and 3.0 mM; Sigma-Aldrich, St Louis, MO) and purified water. Procedure A brief-access procedure similar to that described by others (e.g., Glendinning, Gresack, and Spector, 2002; St. John, Garcea, and Spector, 1994; Spector, Redman, and Garcea, 1996) was used. Testing took place in the gustometer, which was described in Chapter 2. The sample phase began when the rat made contact with the dry sample spout

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39 and initiated licking. The rat was required to lick the drinking spout twice within 250 ms, upon which the shaft of the drinking spout was filled with the stimulus and each subsequent lick resulted in an additional deposit of 5 l into the fluid column. During the session, the rat was allowed access to a single concentration for a brief period of time (5 s) and then after a 6-s inter-presentation interval during which the sample spout was rotated over a funnel and rinsed with clean water, a different solution was offered. The stimulus array for each compound tested included the six different concentrations detailed above and purified water. A given trial started after the first lick. Trials were presented in randomized (without replacement) blocks so that every concentration of a stimulus and water was presented exactly once before the initiation of the subsequent block. Unconditioned licking responses were recorded for later analysis. Sessions were 30 min in duration during which rats could initiate as many trials as possible. The animals were first trained to lick a stationary spout delivering water for 30 min in the gustometer after being placed on 23.5-h restricted water access schedule. For sucrose testing, animals then received 2 days of testing with six stimulus concentrations and purified water while maintained on the water-restriction schedule. During this period of training, the sample spout rotated away from the access slot between trials. The two days of sucrose training under a water-restriction schedule was done to familiarize the animals to approaching and licking the spout. Water bottles were then returned to the home cages for three days, following which, the rats were tested for three days under conditions of non-deprivation. After the last sucrose session, water bottles were again returned to the home cages for a rehydration period before the next-testing week. When the test compound was not sucrose, rats were placed on a water restriction schedule on a Sunday

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40 night, placed into the gustometer for two days of testing with water from a spout which rotated between trials, and then tested for three days under water-restriction. During the three 3-day test sessions with NaCl, citric acid, and quinine, respectively, water rinses were presented between each taste stimulus. A rehydration period always occurred between test compounds. Data Analysis A Tastant/Water Lick Ratio was calculated for the data that were collected during sessions with water-restricted rats. This ratio was computed by taking the average number of licks per trial for each concentration and dividing it by the average number of licks per trial when water was delivered as a taste stimulus. This ratio standardizes the data to control for individual differences in lick rates. In the non-deprived condition, the average number of licks per trial for each concentration was divided by that animals estimated maximal lick rate (licks/5 s) yielding a Standardized Lick Ratio. The maximal lick rate was calculated using the reciprocal of the mean of the inter-lick interval (ILI) distribution (in s) that was measured during training (only inter-lick intervals >50 ms and <200 ms were used) and multiplying this value by 5. Standardizing the licking response in this fashion controls for individual differences in lick rates. These data were used to select concentrations of NaCl, quinine, and citric acid which elicit similar lick suppression relative to water. The mean lick data for each concentration were plotted and then a three-parameter logistic equation was used to fit a curve to the data: f(x) = a/(1+10 b(x-c) ), where a is the asymptote (note, for NaCl, quinine, and citric acid, a was a constant set at 1), b is the slope and c is the point of inflection. The resulting curve was used to guide the choice of concentrations for Experiment II.

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41 Results Results from the brief-access test are shown in Figures 3-1 3-4. Table 3-1 lists the concentrations selected to represent training stimuli for each prototypical compound. Unfortunately, the incorrect lowest concentration of quinine was included in the proposed training array through a typographical error. Instead of using the intended concentration of 0.0827 mM of quinine, 0.027 mM was recorded. Consequently, that low concentration became incorporated into training array of Experiment II. The lowest training concentration of quinine, 0.027 mM is only about twice the most conservative measure of detection threshold for quinine. (0.012 mM Koh & Teitelbaum, 1961; 0.005 mM, Thaw & Smith, 1994; 0.003 mM Shaber, Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St. John, & Spector). In order to determine which concentrations resulted in a reduction in licking at a specific level (i.e., 20%, 40%, and 60% suppression), the equation was rewritten to solve for x, such that x = (log 10 ((a/y)-1)/b)+c). Following the selection of concentrations associated with 20%, 40%, and 60% suppression rates, a concentration that was one order of magnitude (i.e., 1 log 10 unit) below the highest concentration of NaCl (which was associated with a 60% reduction in licking as compared with water) was identified. For sucrose, the opposite strategy was taken and concentrations that were 40%, 60% and 80% of their maximal licking rate to water were used along with the concentration that was approximately 1 log 10 unit above the lowest concentration selected. The lowest concentration for citric acid was selected to be ~1.5 log 10 units below the concentration associated with a 60% reduction in licking because otherwise, there would have been little difference in behavioral responding for the concentration associated with a 40% reduction of licking and the intended one that was 1 log unit below the highest

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42 concentration. For quinine, it was our intention to choose a concentration that was 1.0 log 10 unit lower than highest concentration selected (i.e., 0.0827 mM), but an erroneous value was selected (0.027 mM) that was actually ~1.5 log 10 units lower. Regardless, all concentrations spanned at least 1 log 10 unit and incorporated the dynamic range of responsiveness measured in this task. Discussion The selection of training stimuli suitable for Experiment II was based on the three isoresponsive concentrations and the additional concentration for each compound that allowed for the range of concentrations to span at least 1 log 10 unit. For the aversive stimuli (NaCl, quinine, and citric acid), intensities at which rats reduced their licking to the same benchmark level of performance were selected. The three compounds are referred to as aversive because the rats decreased their licking monotonically as concentration was raised. For the appetitive stimulus, sucrose, the concentrations that resulted in alterations in licking were similarly selected except that the changes in concentration resulted in increased levels of licking rather than suppression. Thus, we attempted to match the three highest concentrations of aversive compounds with the three lowest concentrations of sucrose, with respect to the effect that increasing concentration has on behavior. Although this procedure likely does not result in exactly matching intensities between compounds, we assume that it is a good approximation and importantly provides some confidence that the concentrations chosen at least are overlapping. Here, the same rats were used to determine the dynamic range of concentrations for which licking is modulated across four compounds representing the basic taste qualities.

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43 It is plausible that there were order effects associated with the curves obtained for each compound, considering that sucrose was the first stimulus to be tested, which was followed by NaCl, citric acid, and then quinine. The nature of the prior experience with sucrose may have trained the animal to accept stronger concentrations of the taste stimuli, thus inflating the range of concentrations selected. Perhaps using a nave set of rats, or randomizing the order of presentation between the rats, for each of the four compounds would have yielded different results. An examination of the literature revealed that comparison of the midpoint of the concentration-dependent curve for quinine obtained here (approximately 0.4 mM) with those from two published studies examining brief-access using quinine (approximately 0.3 and 0.2 mM) suggests that these rats did perhaps accept higher concentrations than nave rats do (Spector and Kopka, 2002; St. John, Garcea, and Spector, 1994). Nevertheless, potential parametric influences aside, the experiment provided some basis upon which to choose a broad range of concentrations for each stimulus that at the very least overlap in intensities. Experiment II The following experiment attempted to adapt Morrisons (1967) procedure, described above, but incorporated a broader array of training concentrations and comparison stimuli in order to test the following two hypotheses: 1) rats can learn to discriminate prototypical compounds, characteristic of the putative basic taste qualities, when a variety of concentrations are used to represent each compound, and 2) rats will generalize the responses learned with training stimuli to novel untrained test stimuli.

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44 Method Subjects Forty-eight nave adult male Sprague-Dawley (Charles River Breeders; Wilmington, MA) rats served as subjects. The rats were housed individually in polycarbonate shoe-box style cages in a room where temperature, humidity, and light cycle (lights on 7am 7pm) were controlled automatically. All manipulations were performed during the light phase. The rats had ad libitum access to Purina Rat Chow (5001) in the home cage. Purified (Elix 10; Millipore, Billerica, MA) water was also available, but was removed approximately 16 hours before (~4:00 pm the night before) the first behavioral session of the week and was replaced at the completion of the last session of the week. A contingency was in place that would allow rats to receive supplemental water if body weight decreased to 85% of the ad libitum weight calculated each week, but no rat dropped below that criterion in this experiment. One of the animals was removed before side training (see below) began because it exhibited self-injurious behavior. All procedures were approved by the Institutional Animal Care and Use Committee at the University of Florida. Apparatus The apparatus was the same as that described in Chapter 2. There was, however, no cannula lead entering the chamber from the port in the ceiling of the sound attenuation chamber. Task overview The prototypical taste compounds NaCl, sucrose, quinine HCl, and citric acid were used to represent the putative 4 basic tastes, salty, sweet, bitter, and sour, respectively. Four groups of rats were trained to respond by licking one response spout after sampling

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45 any of the 4 training concentrations of a particular standard, which for each group was one of the prototypical compounds, and they were trained to lick a different response spout after sampling any of the comparison stimuli (the remaining three compounds). Stimuli All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica, MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli consisted of four concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M; Fisher Scientific, Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M; Fisher Scientific, Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM; Fisher Scientific, Atlanta, GA), quinine (0.027 mM, 0.131 mM, 0.360 mM, and 0.827 mM; Sigma-Aldrich, St Louis, MO) and purified water. Groups For overview of the four groups (N, S, Q, and C) and their associated standard and comparison stimuli, see Table 3-2. Each of the groups was named for their standard stimulus and was trained to discriminate four concentrations of that compound from four concentrations each of the comparison stimuli (those from the remaining three prototypical compounds). Trial structure On any given trial (see Figure 3-5), rats were trained to lick a centrally positioned stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two times with an inter-lick interval < 250 ms, then the predetermined solution filled the shaft of the spout, after which the rat could receive up to 5 licks (~5l was deposited into the fluid column upon each lick) before the spout was rotated out of position. Next, a decision phase was initiated, during which the rat was required to lick one response spout

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46 after sampling the standard stimulus or the other response spout after sampling a comparison stimulus. During the consequence phase, if the rat responded correctly to the stimulus, water reinforcement was delivered directly through the response spout (20 licks @ ~5l per lick or a total of 10 s access, whichever occurred first). If the rat failed to respond, or responded on the incorrect response spout, then the rat was punished with a 20-s timeout. After either consequence of the decision phase, the trial moved into an intertrial interval that lasted 6 s. Training Spout training. The rats had access to only one spout (either the sample spout, the left response spout, or the right response spout) and each spout was connected to a reservoir that contained water. The purpose was to train the rats to approach and gain familiarity with obtaining fluid from each of the spouts. Eventually, the sample spout would contain a taste stimulus and only the response spouts would contain water. Side training. Only one trial type was presented within a given session during side training so that the solutions available alternated with each session. That is, if the rats were trained with their standard compound in the first session, then during the next session, the rats received only comparison compounds. After sampling, rats had 180 s (limited hold period) during which they were required to respond. Side training lasted a total of 4 days. Only the third highest concentration of each stimulus was presented. The rats were required to lick the sample spout to obtain a small volume of the stimulus and then select one of the response spouts by licking it. If the rat responded correctly, then water reinforcement was available (10 s access or 20 licks, whichever came first). The intertrial interval during this phase was 6 s.

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47 Alternation. During alternation training, the rats started out with either a standard or one of the comparison stimuli. Upon completion of a set criterion of correct responses, the program switched to the opposite trial type. Each time the rat completed the criterion of correct responses, the program automatically switched to delivery of the other trial type. When the trial type consisted of comparison stimuli, the computer randomly selected (without replacement) the solution to deliver. The correct responses did not have to be consecutive. The limited hold was changed from 180 s to 15 s. During the decision phase, if a rat failed to make any response, or made an incorrect response, a 10-s timeout was initiated. Discrimination training I-II. Stimuli were delivered in a block with a random pattern selected by the computer program. Therefore, the rats had no indication from the prior trial, which solutions would be offered on the current trial. All four training concentrations were used in this phase, but because the gustometer had a limited number of fluid reservoirs, only two concentrations (always one of the highest two and one of the lowest two) of each prototypical compound were included per session. The block size was 12; consequently, every standard concentration for a given session was repeated three times within the block so that the number of standard stimuli matched the number of comparison stimuli available (which were each only presented once per block). The timeout period was increased to 20 s during this phase. After 12 days of discrimination training, a partial schedule of reinforcement was introduced. During the session, two trials (one standard and one comparison) from each block of 12 trials were randomly selected to have neither reinforcement nor punishment delivered contingent on the animals response. That is, the animal did not receive reinforcement if it made the

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48 correct response and it did not receive punishment if it made the incorrect response on those selected trials. There was, however, a punishment contingency in place if the rat failed to make a response. The partial schedule of reinforcement was introduced in anticipation of the eventual inclusion of test stimuli, which would make up approximately 16% of the total trials in a session. The limited hold period (the time the animal was allowed to make a response after sampling) was 5 s for this phase. Test compounds There was no correct response associated with a test stimulus, so the animal would not receive reinforcement, but it also did not receive punishment for a response, unless it failed to make the response before the limited hold period expired. In order to validate whether rats would generalize untrained test stimuli to the standard compound, novel concentrations of the training stimuli were presented. The following novel concentrations of the training compounds and mixtures of NaCl and sucrose compounds served as test stimuli: 0.847 M NaCl 0.068 M Sucrose 0.546 mM Quinine 42.56 Mm Citric acid 1.07 M (high) NaCl + 0.421 M (high) Sucrose 1.07 M (high) NaCl + 0.077 M (low) Sucrose 0.376 M (low) NaCl + 0.421 M (high) Sucrose Water

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49 Retraining water as a comparison stimulus Water was selected as a test compound because it has an interesting history in the literature. Conceptually, water should represent the absence of a taste. The literature, however, reveals that some humans (Anderson, 1959) report water as having a bitter taste and animals respond to water as if it were quinine-like (Bartoshuk, 1977; Morrison, 1967). Here, the profile for water as a test stimulus showed that water appeared to generalize to quinine (see Results). It was not clear whether this was a result of the erroneous inclusion of the very weak concentration of quinine in the training array, or if water indeed has a quinine-like taste (note, these are not mutually exclusive). Consequently, we attempted to train the rats to identify the difference between water and quinine by adding water to the comparison group. Negative control test A water control session was included at the end of the experiment, in which all of the reservoirs were filled with water. Two reservoirs were arbitrarily assigned to the standard spout, and another six were designated as the comparison spout. This was done to examine whether the rats were using non-chemical cues to guide their behavior. Data analysis A 1-way analysis of variance (ANOVA) was conducted for each test stimulus to determine the presence of differences among groups followed by more detailed Bonferroni-adjusted paired comparisons. Separate one-sample t-tests against both of the null hypotheses 1.0 (i.e., the test compound was similar to the standard stimuli) and 0 (i.e., the test compound was similar to the comparison stimuli) were performed. The conventional p 0.05 value was used as the statistical rejection criteria.

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50 Data for the negative control test were analyzed using a one-sample Binomial analysis with null hypothesis = 0.5, which corresponds with the chance level of performance. Generalization score A Generalization Score was calculated for each animal, which essentially quantified the degree to which the test compound was similar to the standard stimulus. The following equation was used to calculate the Generalization Score: [P(T)-P(C)] / [P(S)-P(C)]; where, P(T) = proportion of times the rats responded on the standard response spout when presented with a test stimulus; P(C) = proportion of times the rat responded on the standard response spout when presented with a comparison stimulus; and P(S) = proportion of times the rat responded on the standard response spout when presented with a standard stimulus. Performance (reported as errors) to the comparison stimuli was included in the equation in an attempt to account for response bias that may have developed for individual animals, thus the Generalization Score serves to standardize performance scores for each animal. The data are presented as Generalization Scores for each group. Each vertical bar, represents a different group and shows the degree to which the test compound was behaviorally treated like the standard. A Generalization Score of 0 indicates that the rat responded to the test compound as if it were a comparison stimulus. A score of 1.0 indicates that the rat responded to the test compound as if it were a standard stimulus. A Generalization Score of 0.5 indicates that the compound was no more like the standard than it was the comparison. A score of 0.5, therefore, could indicate that the test compound shares some similarities with both the standard and one (or more) of the comparison compounds. Alternatively, a score of 0.5 could indicate that the test

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51 compound is completely unlike any of the trained stimuli (standard and comparison) and the score is obtained because the rat is randomly placing its behavior between the trained responses. Results The generalization profiles for each test compound are shown in Figures 3-6 through 3-13. These figures are used to reveal the proportion of responding to the test stimulus as compared with the standard stimulus. This format is similar to that used by Morrison (1967), except that the Generalization Score is plotted on the ordinate instead of proportion of responses to the standard; the group names are listed along the horizontal axis. Tables 3-4, 3-6, 3-8, 3-10, 3-12, 3-14, 3-16, and 3-18 list the performance for each group to individual concentrations of the training stimuli for each test compound. Data reported in these tables can be used to support the conclusion that rats in this experiment were reliably able to discriminate between training compounds and that stimulus control was maintained during the testing period. Each table reflects the data for those particular sessions that contained the test stimulus. It is noteworthy that these scores were generally high and the variance was low. Interestingly, the scores for the lowest concentration of citric acid in the quinine group for many of the test stimuli were lower than the other concentrations, which implies that the group had more trouble discriminating that concentration of citric acid from their standard (quinine concentrations). Indeed, results from studies using electrophysiological and CTA approaches suggest that the signals for bitter and sour stimuli may overlap to some extent (e.g., Frank, Contreras, and Hettinger, 1983; Lemon and Smith, 2005; Nowlis, Frank, and Pfaffmann, 1980), but clearly the generally high levels of behavioral performance seen here would argue against that. Besides, similarly poor performance to the lowest sucrose concentration can be seen

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52 during some of the same test weeks, which might suggest a problem with an overall ability to maintain stimulus control for the weakest solutions in that group. Novel concentrations: NaCl Figure 3-6 depicts the untrained responses to the novel concentration of NaCl, 0.847 M. An ANOVA comparing performance in the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 2607.5, p < 0.01). Subsequent post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: N> S > Q > C. Separate one-sample t-test tests (see Table 3-3) showed that the Generalization Scores for the N group were actually greater than 1.0, indicating that novel NaCl is more standard-like than the standard concentrations used to maintain stimulus control, but the actual value was indeed very close to unity. Conversely, the Generalization Scores from the S and C groups were significantly less than 0, indicating that those groups treated the novel NaCl as more comparison-like than their actual comparison stimuli. Both of these types of findings can probably be explained as statistical artifacts. In general, it is fair to say that the N group responded as if novel NaCl was standard-like and rats in the S, Q, and C groups treated the test compound as if it were comparison-like; this was expected given that NaCl is one of the comparison compounds for each of these latter three groups. The overall performance on training stimuli, which were used to maintain stimulus control during testing sessions, is listed in table 3-4; the performance values during the sessions with the test compound present are shown.

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53 Novel concentrations: Sucrose Figure 3-7 depicts the untrained responses to the novel concentration of sucrose, 0.068 M. An ANOVA comparing Generalization Scores obtained for the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 1587.9, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: S> N > Q > C. Separate one-sample t-test analyses of the Generalization Scores (see Table 3-5) revealed that in the S group, novel sucrose was not different from the standard (sucrose) training stimuli and that the N and Q groups were statistically not different from comparison training stimuli. The C group did, however, treat the novel sucrose as more comparison-like than their comparison training compounds. Again, this can likely be explained by statistical artifact. Overall, there is statistical evidence to support the claim that the novel concentration of sucrose generalizes to sucrose in the S group, and not at all to the standards for the N, Q, and C groups. Table 3-6 includes the performance data for all of the animals during this phase of testing. Novel concentrations: Quinine Figure 3-8 describes the untrained responses to the novel concentration of quinine, 0.546 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 2329.181, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: Q> C > N > S. Of interest, separate one-sample t-test tests of the Generalization Scores (see Table 3-7) showed that the Generalization Scores to novel quinine in the Q group were not statistically different from their standard stimulus and that performance in the N

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54 and S groups was not different from comparison stimuli. Analysis of the C group revealed that Generalization Scores were statistically greater than 0, but this difference did not survive a Bonferroni correction and it was minor in magnitude. Therefore, performance to the novel concentration of quinine appears to generalize completely to the trained concentrations of quinine in the Q group and all other groups respond as if the stimulus were comparison-like. Table 3-8 lists the performance data for all of the animals during this phase of testing. Novel concentrations: Citric acid Figure 3-9 shows the untrained responses to a novel concentration of citric acid, 42.56 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 2734.3, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: C> N > Q > S. Of interest, separate one-sample t-tests of the Generalization Scores (see Table 3-9) showed that the novel citric acid test stimulus was statistically more standard-like for the C group than the training concentrations used, though that effect disappeared with Bonferroni correction. Also of note is that the S and Q groups responded as if the novel concentration of citric acid was more comparison-like than the training compounds, though Bonferroni adjustment resulted in the Q group failing to reach significance. The N group responded as if the test stimulus was not different from the comparison training stimuli. Overall, the rats in the C group responded as if the novel concentration of citric acid were similar to the training concentrations, while the rats in the other groups responded as if it were a comparison stimulus. Table 3-10 contains the performance to all concentrations of training stimulus for all of the rats.

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55 Mixtures between NaCl and sucrose: 1.07 M NaCl + 0.421 M sucrose Figure 3-10 shows the untrained responses to a mixture of 1.07 M NaCl and 0.421 M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 89.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: N = S > Q > C. Of interest, separate one-sample t-tests of the Generalization Scores (see Table 3-11) showed that all of the groups differ statistically from 1.0 (the test compound is standard-like), and only the C group responds as if the test stimulus is not statistically different than the comparison stimuli. Consequently, the N and S groups report that the mixture is also not comparison-like, while the Q group responded as if the mixture was more comparison-like than the training compounds. The performance in the N and S groups showed similar levels of responding (ANOVA post hoc between N and S p = 1.000), which suggests that both qualities (NaCl-like and sucrose-like) contributed to the overall experience of the solution. Table 3-12 contains performance data for the training stimuli. Mixtures between NaCl and Sucrose: 1.07 M NaCl + 0.077 M Sucrose Figure 3-11 shows the untrained responses to a mixture of 1.07 M NaCl and 0.077 M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 1122.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: N > = C = S =Q. Of interest, separate one-sample t-test analyses of the Generalization Scores (see Table 3-13) showed that all groups were statistically different from 1.0 (i.e.,

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56 the test stimulus was standard-like) and only the N group differed significantly from 0 (the test stimulus is comparison-like). Taken together, these data indicate that a NaCl-like taste appears to be the only quality present in the mixture. It would seem that the relatively strong concentration of NaCl overshadows the relatively weak concentration of sucrose. Table 3-14 contains data for performance to training stimuli. Mixtures between NaCl and Sucrose: 0.376 M NaCl + 0.421 M Sucrose Figure 3-12 shows the untrained responses to a mixture of 0.376 M NaCl and 0.421 M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 78.0, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: S > N > C > Q. Of interest, separate one-sample t-test analyses of the Generalization Scores (see Table 3-15) showed that all groups differed significantly from 1.0 (i.e., that the test stimulus was standard-like) and that the S and N groups also differed significantly from 0 (the test stimulus is comparison-like). Both the Q and C groups responded as if the test compound was comparison-like. The post hoc test of the ANOVA indicated that the S component was statistically greater than the N component. This suggests that rats can distribute their behavior according to the relative contribution of each compound that is present in a mixture. The overall performance to training stimuli, which were used to maintain stimulus control during testing sessions, is listed in Table 3-16. Novel test compound: Water Figure 3-13 shows the untrained responses to water as a test compound. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 43) = 386.4, p <

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57 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: Q > C > S > N. Of interest, separate one-sample t-tests of the Generalization Scores (see Table 3-17) showed that all groups were statistically different from 1.0 (the test compound is standard-like) and only the N group was not statistically different from 0 (the test stimulus is comparison-like). Taken together, these results show that, under these testing conditions, there is a strong quinine-like component, followed by citric acid-like and sucrose-like components to water. The significance of this will be addressed in the discussion section, but briefly it may have occurred because the lowest training concentration of quinine was only about twice the most conservative measure of detection threshold reported and also because water might actually possess a weak quinine taste. Performance to all stimulus control concentrations are shown in Table 3-18. Retraining water as a comparison stimulus The results of the phase in which we attempted to retrain the rats include water with the comparison stimuli are shown in figure 3-14. The results for the overall performance when all training compounds were present were poor for the water stimulus in the Q group (data not shown). Because the percentage of the trials in the session that were water was very low when all training stimuli were presented, we reasoned that to increase the ability of the rats to specifically learn the discrimination, it was necessary to limit the types of training stimuli encountered to only water and quinine. Consequently, to increase the animals overall experience with discriminating water from quinine, only water and 0.360 mM & 0.827 mM quinine were present in training sessions shown in Figure 3-14; for reference, the performance during the first day of retraining is included. Clearly, the rats were unable to perform this discrimination well. Although it can be seen

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58 that their performance to water improved over the course of training, the ability of the rats to correctly detect the presence of quinine worsened, indicating that the stimuli were unable to maintain the high levels of stimulus control previously seen with the other training compounds. Negative control session The results for the negative (water) control session are shown in Figure 3-15. When all of the testing reservoirs which were normally filled with chemical stimuli were filled instead with water, performance dropped to chance levels for most of the animals. There were 8 rats that performed significantly worse than chance. If a Bonferroni correction is applied to control for multiple tests, then the same rats fail to reach significance. These data support the claim that rats used only chemical cues to guide their behavior. Discussion The rats in this experiment readily learned to discriminate several concentrations of one prototypical compound representing one of the putative basic taste qualities from various concentrations of prototypical compounds representing the three remaining taste qualities. Moreover, the results from test stimuli support the claim that responses to training stimuli generalized to novel compounds that likely shared similar taste qualities with the training compounds. The fact that these trials were presented without consequence allows the assertion that the behavior generalized on the basis of the training history of the animal. Additionally, other evidence to support that claim is based on the performance to the novel concentrations of training compounds; the rats performed as if the novel concentrations were similar to the training compounds.

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59 The novel concentrations of training stimuli were taken from the midpoint of each of the curves obtained during the brief-access experiment. Because the concentrations included were within the range of training stimuli, it remains possible that more intense compounds would not have generalized as well, but given what is known about stimulus intensity dynamism, it is likely that higher concentrations would be identified appropriately. Nevertheless, it remains an empirical question which could be addressed by further experiments. The data on the mixtures of NaCl and sucrose were insightful. These data showed that rats do not fully generalize to their standard concentration just because it is present within the mixture. When the standard compound is included in a solution with another compound to which the rat has been trained to make a competing response, a Generalization Score of 0.5 may result. Therefore, depending on the relative concentrations of the standard and comparison solutions used, the behavior of the rats seems to reflect which compound(s) is/are dominant in the solution. It suggests, then, that profiles of this type would be helpful in the identification of the components of complex stimuli (either naturally complex, or through mixtures). Overall, the data from this novel paradigm suggest that this testing method has the potential to provide information similar to that obtainable using the conditioned taste aversion approach with respect to the way rats categorize taste stimuli, presumably on the basis of their qualitative feature. The most obvious benefit of this procedure over the CTA approach, however, is that the same test animals can be used repeatedly to report on essentially an unlimited number of test compounds. The initial training that is required

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60 could be described by some as rather lengthy, but the potential for information return is quite large, and arguably worthwhile. This paradigm could serve as a more efficient method of obtaining information about the taste quality of several compounds. It is fair to state that the procedure has promise as an alternative or complementary testing protocol to the study of taste quality in animal models. Clearly, more test compounds should be used to extend previous findings and to identify similarities between this method and other existing procedures. While it is feasible that this paradigm would yield different findings (e.g., because of different-testing parameters), it is also possible that this method would provide converging lines of evidence for results obtained using the conditioned taste aversion approach and taste discrimination tasks. Such an outcome would increase the confidence that these different approaches are tapping into similar principles. Even if this paradigm, however, resulted in conflicting findings from those generated with other procedures such as CTA, it still does not undermine the information that this method could potentially provide. As long as the results are reproducible some aspect of taste behavior is being measured. Perhaps the unique strengths of this procedure will be realized with further development. One possible avenue which sets this approach apart from the CTA method is that extinction of learning is not a factor. Theoretically, the same compound could be tested weeks apart and the animal would respond to it in the same manner, provided the training stimuli still maintained stimulus control. The usefulness of this aspect of the task is that it is compatible with manipulations of the gustatory system in which subsequent re-testing in the same animal

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61 subjects is required by design. This feature (i.e., the strength of within subject designs for data interpretation) is a benefit that the CTA approach does not offer. When water served as a test compound, the profile generated was unexpected. In the planning stages of the experiment, the wrong concentration of quinine was included in the proposed training array through an unfortunate typographical error. Because the lowest training concentration of quinine, 0.027 mM is only about twice the most conservative measure of detection threshold for quinine reported in the literature (0.012 mM Koh & Teitelbaum, 1961; 0.005 mM Thaw & Smith, 1994; 0.003 mM Shaber, Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St. John, & Spector), it remains possible that animals generalized water responses to quinine because, of all the stimuli, quinine had the weakest of the low concentrations. It is also possible that water might have a quinine-like taste as has been reported for both humans (Anderson, 1959), and rats (Bartoshuk, 1977; Morrison, 1967). These two possibilities are not mutually exclusive, but as the next experiment will suggest, however, the latter explanation seems to have some merit.

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62 Table 3-1. Training compounds selected from Experiment I. Compound 1 2 3 4 NaCl (M) 0.107 0.376 0.668 1.07 Sucrose (M) 0.042 0.077 0.148 0.421 Quinine (mM) 0.027 0.131 0.360 0.827 CitricAcid (mM) 2.04 10.4 28.2 64.3 Table 3-2. Experimental groups Group Standard Comparison Solutions 1) N NaCl Sucrose, Quinine, Citric Acid 2) S Sucrose NaCl, Quinine, Citric Acid 3) Q Quinine NaCl, Sucrose, Citric Acid 4) C Citric Acid NaCl, Sucrose, Quinine Table 3-3. Results from one-sample t-tests for a novel concentration of NaCl. Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 5.0 < 0.01 < 0.01 228.62 < 0.01 < 0.01 S 10 -148.2 < 0.01 < 0.01 -4.85 < 0.01 < 0.01 Q 11 -67.3 < 0.01 < 0.01 -1.77 0.11 0.84 C 11 -93.5 < 0.01 < 0.01 -7.12 < 0.01 < 0.01 Table 3-4. Performance to training stimuli during novel NaCl testin g Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 0.107 92.1 1.4 96.0 1.6 88.9 1.9 97.5 0.376 0.376 97.8 0.7 96.7 1.3 96.0 2.2 98.9 0.668 0.668 97.9 0.6 97.0 0.9 97.9 1.1 97.6 NaCl (M) 1.07 1.07 97.9 0.6 97.6 1.3 98.1 1.6 97.4 0.042 0.042 98.7 0.9 90.5 2.8 87.6 3.3 94.7 0.077 0.077 98.0 1.1 96.3 1.1 93.5 1.7 98.9 0.148 0.148 100.0 0.0 96.8 0.7 96.7 2.1 98.2 Sucrose (M) 0.421 0.421 99.0 0.5 98.8 0.8 96.8 1.4 97.6 0.027 0.027 96.1 1.3 87.4 3.3 94.8 1.0 79.3 0.131 0.131 97.9 0.9 92.4 1.5 94.3 1.2 80.7 0.360 0.360 96.2 0.7 94.4 0.9 94.7 1.0 86.2 Quinine (mM) 0.827 0.827 98.4 0.7 93.1 1.3 95.3 0.6 85.6 2.04 2.04 98.6 0.8 96.1 1.4 79.8 3.6 89.2 10.4 10.4 96.3 1.2 97.9 0.8 90.9 1.6 94.4 28.2 28.2 97.3 1.1 99.1 0.9 96.9 1.2 97.7 Citric Acid (mM) 64.3 64.3 98.2 1.0 99.0 0.7 95.6 2.5 99.1

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63 Table 3-5. Results from one-sample t-tests for a novel concentration of sucrose. Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 -112.36 < 0.01 < 0.01 0.19 0.85 1.00 S 10 -0.25 0.81 1.00 77.92 < 0.01 < 0.01 Q 11 -54.94 < 0.01 < 0.01 0.51 0.62 1.00 C 11 -146.99 < 0.01 < 0.01 -7.88 < 0.01 < 0.01 Table 3-6. Performance to training stimuli during novel sucrose testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6 0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6 NaCl (M) 1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9 0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1 Sucrose (M) 0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7 0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1 Quinine (mM) 0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3 Citric Acid (mM) 64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9 Table 3-7. Results from one-sample t-tests for a novel concentration of quinine Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 -153.10 < 0.01 < 0.01 -0.62 0.55 1.00 S 10 -122.15 < 0.01 < 0.01 -1.80 0.10 0.82 Q 11 2.66 0.02 0.16 109.65 < 0.01 < 0.01 C 11 -61.17 < 0.01 < 0.01 3.11 0.01 0.08

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64 Table 3-8. Performance to training stimuli during novel quinine testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 92.1 1.4 96.0 1.6 88.9 1.9 97.5 1.1 0.376 97.8 0.7 96.7 1.3 96.0 2.2 98.9 0.6 0.668 97.9 0.6 97.0 0.9 97.9 1.1 97.6 1.0 NaCl (M) 1.07 97.9 0.6 97.6 1.3 98.1 1.6 97.4 1.0 0.042 98.7 0.9 90.5 2.8 87.6 3.3 94.7 1.6 0.077 98.0 1.1 96.3 1.1 93.5 1.7 98.9 0.6 0.148 100.0 0.0 96.8 0.7 96.7 2.1 98.2 1.0 Sucrose (M) 0.421 99.0 0.5 98.8 0.8 96.8 1.4 97.6 0.8 0.027 96.1 1.3 87.4 3.3 94.8 1.0 79.3 3.6 0.131 97.9 0.9 92.4 1.5 94.3 1.2 80.7 3.2 0.360 96.2 0.7 94.4 0.9 94.7 1.0 86.2 2.9 Quinine (mM) 0.827 98.4 0.7 93.1 1.3 95.3 0.6 85.6 2.6 2.04 98.6 0.8 96.1 1.4 79.8 3.6 89.2 1.3 10.4 96.3 1.2 97.9 0.8 90.9 1.6 94.4 1.1 28.2 97.3 1.1 99.1 0.9 96.9 1.2 97.7 0.4 Citric Acid (mM) 64.3 98.2 1.0 99.0 0.7 95.6 2.5 99.1 0.2 Table 3-9. Results from one-sample t-tests for a novel concentration of citric acid Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 -178.99 < 0.01 < 0.01 -0.21 0.84 1.00 S 10 -145.84 < 0.01 < 0.01 -6.11 < 0.01 < 0.01 Q 11 -68.01 < 0.01 < 0.01 -32.85 0.01 0.08 C 11 3.50 0.01 0.08 109.77 < 0.01 < 0.01 Table 3-10. Performance to training stimuli during novel citric acid testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6 0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6 NaCl (M) 1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9 0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1 Sucrose (M) 0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7 0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1 Quinine (mM) 0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3 Citric Acid (mM) 64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9

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65 Table 3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 -6.89 < 0.01 < 0.01 12.88 < 0.01 < 0.01 S 10 -5.72 < 0.01 < 0.01 9.47 < 0.01 < 0.01 Q 11 -104.61 < 0.01 < 0.01 -6.29 < 0.01 < 0.01 C 11 -62.61 < 0.01 < 0.01 -0.56 0.59 1.00 Table 3-12. Performance to training stimuli during high NaCl + high sucrose testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 89.9 2.2 94.6 1.4 78.2 3.2 96.1 1.3 0.376 96.6 1.4 95.8 1.2 93.6 1.6 95.9 2.6 0.668 97.0 1.3 96.1 1.1 96.2 2.7 88.2 8.1 NaCl (M) 1.07 98.8 0.4 100.0 0.0 98.0 1.0 95.2 1.6 0.042 98.7 0.5 86.9 1.2 80.0 3.0 89.6 2.7 0.077 99.6 0.4 94.7 1.8 91.3 2.6 97.7 0.9 0.148 99.3 0.5 98.4 0.4 97.2 1.2 97.2 1.4 Sucrose (M) 0.421 97.5 1.2 99.0 0.5 98.0 1.2 97.0 1.5 0.027 98.0 0.9 88.1 2.5 94.9 1.0 81.5 1.9 0.131 99.0 0.5 94.7 1.6 95.2 1.0 87.7 2.4 0.360 98.5 0.6 95.7 1.6 97.1 0.8 89.7 2.3 Quinine (mM) 0.827 97.5 1.0 99.0 0.5 97.0 0.9 89.3 3.8 2.04 99.0 0.7 97.3 1.3 80.2 2.5 87.0 1.4 10.4 99.2 0.5 97.8 0.8 88.7 2.6 94.0 1.1 28.2 99.2 0.5 98.6 0.8 96.4 0.8 98.4 0.6 Citric Acid (mM) 64.3 98.6 1.1 97.8 1.1 97.9 0.6 99.5 0.3 Table 3-13. Results from one-sample t-tests for 1.07 M NaCl + 0.077 M sucrose Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 -4.91 < 0.01 < 0.01 98.04 < 0.01 < 0.01 S 10 -110.81 < 0.01 < 0.01 0.48 0.63 1.00 Q 11 -63.78 < 0.01 < 0.01 -0.68 0.51 1.00 C 11 -51.85 < 0.01 < 0.01 -0.95 0.36 1.00

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66 Table 3-14. Performance to training stimuli during high NaCl + low sucrose testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 94.6 1.6 95.9 1.3 89.0 2.6 95.2 1.9 0.376 97.4 1.0 98.4 0.7 98.1 1.0 98.6 0.5 0.668 98.6 0.4 98.3 0.9 99.7 0.3 98.8 0.7 NaCl (M) 1.07 98.6 0.3 98.2 0.8 97.7 0.6 98.0 0.8 0.042 99.7 0.3 92.2 1.9 87.2 2.3 96.5 2.0 0.077 100.0 0.0 97.9 0.8 95.1 1.6 98.1 0.7 0.148 98.6 0.8 97.7 1.3 99.1 0.5 97.4 1.1 Sucrose (M) 0.421 99.4 0.4 98.5 1.0 97.3 1.1 96.4 1.2 0.027 99.0 0.5 87.5 3.9 93.7 1.3 79.7 4.0 0.131 99.1 0.5 94.2 1.0 96.8 0.5 84.8 3.3 0.360 98.9 0.6 94.3 1.4 97.5 0.5 90.1 2.5 Quinine (mM) 0.827 98.0 0.8 96.0 2.1 96.1 0.8 92.3 1.8 2.04 99.7 0.3 94.3 1.7 71.9 2.6 88.9 1.6 10.4 99.6 0.4 96.8 1.2 89.8 1.8 94.3 0.9 28.2 99.0 0.7 98.3 1.0 97.2 1.4 98.3 0.5 Citric Acid (mM) 64.3 99.0 0.5 99.7 0.3 98.8 0.7 99.1 0.3 Table 3-15. Results from one-sample t-tests for 0.376 M NaCl + 0.421 M sucrose Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 -17.58 < 0.01 < 0.01 15.07 < 0.01 < 0.01 S 10 -4.43 < 0.01 < 0.01 14.87 < 0.01 < 0.01 Q 11 -51.13 < 0.01 < 0.01 -1.17 0.27 1.00 C 11 -18.29 < 0.01 < 0.01 3.18 0.01 0.08

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67 Table 3-16. Performance to training stimuli during low NaCl + high sucrose testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 92.2 1.5 95.0 1.5 86.2 2.2 98.1 0.6 NaCl 0.376 99.2 0.3 98.5 0.6 98.6 0.6 98.7 0.5 (M) 0.668 98.9 0.5 99.3 0.5 98.1 1.1 96.7 1.6 1.07 98.7 0.4 99.2 0.5 90.6 8.3 97.6 0.7 0.042 98.1 0.6 88.9 1.9 81.7 3.4 91.2 2.3 Sucrose 0.077 99.7 0.3 93.0 1.7 87.0 2.2 93.3 3.1 (M) 0.148 99.6 0.4 98.7 0.7 98.0 1.0 97.6 1.0 0.421 99.6 0.4 98.9 0.5 97.8 0.8 98.9 0.6 0.027 97.5 1.2 87.5 2.0 93.1 1.0 78.8 3.4 Quinine 0.131 97.5 1.2 95.1 1.6 95.1 1.0 85.0 3.5 (mM) 0.360 98.9 0.6 96.8 1.2 88.4 8.0 91.4 1.9 0.827 98.3 0.6 98.4 0.7 96.3 0.8 90.1 2.3 2.04 98.3 0.8 93.9 1.4 57.3 4.2 79.3 2.5 Citric Acid (mM) 10.4 98.9 0.8 98.7 0.7 81.4 7.2 91.8 1.4 28.2 99.7 0.3 99.5 0.5 99.3 0.7 99.0 0.4 64.3 97.7 1.0 98.5 0.9 97.1 1.0 99.4 0.3 Table 3-17. Results from separate one-sample t-tests for water Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 11 -130.33 < 0.01 < 0.01 3.01 0.01 0.08 S 10 -30.60 < 0.01 < 0.01 7.19 < 0.01 < 0.01 Q 11 -4.73 < 0.01 < 0.01 90.67 < 0.01 < 0.01 C 11 -24.67 < 0.01 < 0.01 7.880 < 0.01 < 0.01 Table 3-18. Performance to training stimuli during water testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 97.3 0.6 99.2 0.8 94.5 1.0 97.9 0.8 NaCl 0.376 98.3 0.5 98.9 0.6 98.6 0.6 98.4 0.8 (M) 0.668 98.7 0.4 96.7 0.9 99.2 0.5 98.5 0.6 1.07 99.0 0.3 98.3 0.9 99.3 0.5 99.7 0.3 0.042 96.7 1.0 90.7 2.4 87.3 2.5 94.2 1.9 0.077 98.3 1.4 95.2 1.3 96.7 1.1 98.0 1.0 0.148 99.2 0.5 98.4 0.4 98.4 0.7 97.9 1.2 Sucrose (M) 0.421 99.3 0.5 98.1 0.8 97.4 0.8 96.9 1.2 0.027 97.2 1.6 89.1 2.7 92.3 1.2 81.6 2.2 0.131 96.9 1.2 94.0 2.3 94.7 0.9 88.7 2.5 0.360 97.5 1.0 92.4 2.3 94.3 1.2 91.0 2.6 Quinine (mM) 0.827 98.0 0.7 97.1 1.1 94.0 1.2 90.1 1.6 2.04 98.3 0.7 97.2 1.1 90.9 1.6 93.4 1.0 10.4 97.4 1.1 98.8 0.8 95.8 1.1 93.9 1.6 28.2 97.2 1.1 99.2 0.5 92.8 1.6 93.4 1.2 Citric Acid (mM) 64.3 98.6 0.6 99.5 0.5 97.9 1.0 98.6 0.6

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68 NaCl Concentration (M) 0.010.11 0.00.20.40.61.0 0.8 Figure 3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test. Rats monotonically decreased licking as concentration increased. SucroseConcentration (M) 0.0010.010.1110 0.00.20.4 1.0 0.8 0.6 Figure 3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test. Rats monotonically increased licking as concentration increased.

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69 QuinineConcentration (mM) 0.0010.010.1110 0.00.20.40.60.81.0 Figure 3-3. Mean (n=8) unconditioned licking to quinine in a brief access test. Rats monotonically decreased licking as concentration increased. Citric AcidConcentration (mM) 0.010.11101001000 0.00.20.40.60.8 1.0 Figure 3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test. Rats monotonically decreased licking as concentration increased.

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70 Figure 3-5. An overview of the trial structure.

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71 0.847 M NaClGroups NSQC -0.20.00.20.40.60.8 1.0 1.2 Figure 3-6. The generalization profile obtained when 0.847 M NaCl was used as a test compound. The novel concentration generalized to the trained standard. 0.068 M SucroseGroups NSQC -0.20.0 0.2 0.40.61.2 0.81.0 Figure 3-7. The generalization profile obtained when 0.068 M sucrose was used as a test compound. The novel concentration generalized to the trained standard.

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72 0.546 mM QuinineGroups NSQC -0.20.00.20.40.60.81.01.2 Figure 3-8. The generalization profile obtained when 0.546 mM quinine was used as a test compound. The novel concentration generalized to the trained standard. 42.56 mM Citric AcidGroups NSQC -0.20.00.20.4 0.60.8 1.01.2 Figure 3-9. The generalization profile obtained when 42.56 mM citric acid was used as a test compound. The novel concentration generalized to the trained standard.

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73 1.07 M NaCl + 0.421 M SucroseGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 3-10. The generalization profile obtained when 1.07 M NaCl + 0.421 M sucrose was used as a test stimulus. The profile obtained was equally NaCland sucrose-like. 1.07 M NaCl + 0.077 M Sucrose Groups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose was used as a test stimulus. The profile obtained was NaCl-like.

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74 0.376 M NaCl + 0.421 M SucroseGroups Generalization Score NSQC -0.20.2 0.00.40.61.01.2 0.8 Figure 3-12. The generalization profile obtained when 0.376 M NaCl + 0.421 M sucrose was used as a test stimulus. The profile obtained was more sucrose-like than NaCl-like, but there was no statistical evidence for quinine-like or citric acid-like components. Water Gro ups NS QC Generalization Score -0.20.20.40.61.01.2 0.00.8 3. The generalization profile obtained when water was used as a test stimulus. The profile obtained was predominantly quinine-like, indicating that either quinine had the weakest of the low concentrations. Figure 3-1 water has a quinine-like taste quality and/or because, of all of the stimuli,

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75 Retraining 0.360 mM & 0.827 mM Concentrations of Quinine Versus Water0.40.50.60.80.91% Correc 01 00.30.71415161718192021222324252627 t .1 0.2 Quinine Wa Days ter f water Figure 3-14. Performance of the Q group during retraining for discrimination ofrom the two mid-range concentrations of quinine. Overall Performance During Negative Control Test708090100% Correct 0100123456789Rat Number 24050601011121314151617181920212223242526272930313233343536373839404142434445464748 ******** 30 Figure 3-15. Performance on water control test. Individual performance scores for all rats indicate that taste did not serve as a cue to guide behavior.

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CHAPTER 4 APPLICATION OF A NEW BEHAVIORAL PAQUALITY GENERALIZATION RADIGM TO ASSESS TASTE Introduction s, d s that were completely novel to the rats. s. The rats were housed individually in polycarbonate shoeChapter 3 explored whether rats would be able to perform a task in which they were required to discriminate one prototypical taste compound, thought to be representative of one of the putative four basic taste qualities, from the other three prototypical taste compounds. Furthermore, we wanted to determine whether we could use the untrained responses of the animals, when presented with novel taste compoundto generate profiles which would indicate how NaCl-like, sucrose-like, quinine-like, and citric acid-like each novel test compound is. A few questions and confounds were not addressed in that particular paradigm. Therefore, the following experiment was modifieby 1) increasing the lowest concentration of quinine from 0.027 mM to 0.083 mM, and 2)including a water (W) group specifically trained to discriminate the four prototypical stimuli (comparison stimuli) from their water standard in an attempt to overcome the pitfalls encountered in Experiment II of Chapter 3. Additionally, the current experiment was also designed to extend the findings of the previous chapter by including test compound Method Subjects Thirty nave adult male Sprague-Dawley (Charles River Breeders; Wilmington, MA) rats served as subject 76

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77 box style cages i n a room where temperature, humidity, and light cycle (lights on 7am 7pm)e HCl, and citric acid, present the putative 4 basic tastes, salty, sweet, bitter, and sour, respe out g ere were controlled automatically. All manipulations were performed during the light phase. The rats had ad libitum access to Purina Rat Chow (5001) in the home cage. Purified (Elix 10; Millipore, Billerica, MA) water was also available, but was removed approximately 16 hours before (4:00 pm the day before) the first behavioral session of the week and was replaced at the completion of the last session of the week. Apparatus The apparatus was the same as that described in Experiment II of Chapter 3. Task Overview The prototypical taste compounds, NaCl, sucrose, quinin were used to re ctively. Five groups of rats were trained in a manner similar to that in Chapter 3.Briefly, they were trained to respond by licking one response spout after sampling any ofthe 4 training concentrations of a particular standard, which for each group was one of theprototypical compounds or water, and they were trained to lick a different response spafter tasting any of the comparison stimuli, which included the remaining compounds (see Table 4-1). Stimuli The same concentrations of each of the prototypical compounds were used durintraining and were based on the results of Experiment I in Chapter 3. All solutions wprepared daily with purified water (Elix 10, Millipore, Billerica, MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli consisted of four concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M; Fisher Scientific, Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M; Fisher Scientific,

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78 Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM; Fisher ScienAtlanta, GA), quinine (0.083 mM, 0.131 mM, 0.360 mM, and 0.827 mM; Sigma-ASt Louis, MO) and purified water. Note the originally intended lowest tific, ldrich, concentration of d in this experiment. Trialt parison s, If the rat failed to respond, or respoout al he r quinine, 0.083 mM, was include Structure On any given trial (see flow chart), rats were trained to lick a centrally positioned stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two times with an interlick interval < 250 ms, then the shaft of the spout was filled with the stimulus solution, after which the rat could sample up to 5 licks (~5l was deposited into the fluid column upon each lick) before the spout was rotated out of position. Next, a decision phase was initiated, during which the rat was required to lick one response spouafter tasting the standard stimulus or the other response spout after tasting a comstimulus. During the consequence phase, if the rat responded correctly to the stimuluwater reinforcement was delivered directly through the response spout (20 licks @ ~5l per lick or a total of 10 s access, whichever occurred first). nded on the incorrect response spout, then the rat was punished with a 20-s timeAfter either consequence of the decision phase, the trial moved into an inter-trial intervthat lasted 6 s. See Figure 3-5 in Chapter 3 for an overview of the trial structure. Training Table 4-2 contains the training parameters associated with this experiment. Tinclusion of water as a comparison had to be abandoned in order to proceed with trainingbut the water group was maintained, albeit on a different training schedule than the othe4 groups (see Table 4-3).

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79 Spout training This phase was the same as that described in Experiment II Chapter 3. The rats ha d pout (either the sample spout, the left response spout, or the right respoty with getting fluid m each of the spouts. Eventually, the sample spout would contain a taste stimulus and rentain water. The rats had to learn to lick from the mpponse spouts by licking it. If the rat responded ment was available (10 s access or 20 licks, whichever meial interval during this phase was 6 s. s the same as that described in Experiment II Chapter 3. Briefly, ly oas presented within a given session during side training. If the rats r standard in the first session, then during the next session, the rats ison trials. After sampling, rats had 180 s (limited hold period) rinrequired to respond. Side training lasted a total of 4 days. Only thration of each stimulus was presented. access to only one s nse spout) and each spout was connected to a reservoir that contained water. The point of this phase was to train the rats to approach and gain familiari fro thesponse spouts would only co sale spout and then select one of the res correctly, then water reinforce ca first). The inter-tr Side training This phase wa onne trial type w were trained with thei received only compar dug which they were theird highest concent Alternation This phase was the same as that described in Experiment II Chapter 3. Briefly, during alternation training, the rats started out with either a standard or one of the comparison stimuli. Upon completion of a set criterion of correct responses, the programswitched to the opposite trial type. The criterion was set at 6 the first day, 4 the second day, and 2 the third day of alternation training. Each time the rat completed the criterion of correct responses, the program automatically switched to delivery of the other trial

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80 type. The correct responses did not have to be consecutive. The limited hold was changed from 180 s to 15 s. During the decision phase, if a rat failed to make any respoere delivered in a block with a random pattern selected by the computer program. Therefore, the rats had no indim the prior trial, which solutions would be off reservoirs, only two concentrations e of the lowest two) of each prototypical compound he block size was 16 to accommodate the water standard; conseshment e t owever, a punishment contingency if the rat failed to make a response. There was no correct response associated with a test stimulus, nse, or made the incorrect response, a 10-s timeout was initiated. Discrimination training I-III Trials w cation fro ered on the current trial. All 4 training concentrations were used in this phase, butbecause the gustometer had a limited number of fluid (always one of the highest two and on were included per session. T quently, every standard concentration for a given session was repeated three times within the block so that the number of standard stimuli matched the number of comparison stimuli available (which were each only presented once per block). The timeout period was increased to 20 s during this phase. The training schedules differed for the W group and the N, S, Q, & C groups at this point. Once performance reached an asymptote for all animals in the N, S, Q, and C groups (85% or better two consecutive days), a partial schedule of reinforcement was introduced. During the session, 2 trials (one standard and one comparison) from each block of 12 trials were randomly selected to have neither reinforcement nor punidelivered contingent on the animals response. That is, the animal did not receivreinforcement if it made the correct response but it also did not receive punishment if imade the incorrect response. There was, h

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81 so the animal would not recei ve reinforcement, but it also did not receive punishment for a resp onse, unless it failed to make the response before the limited hold (5 s) timed out. Test Compounds In order to extend the results from the last experiment, only novel taste compounds were tested. The following novel compounds served as test stimuli: 0.376 M sodium gluconate 0.668 M sodium gluconate 0.131 mM denatonium 0.360 mM denatonium 0.077 M maltose 0.148 M maltose 0.376 M KCl 0.668 M KCl 0.077 M MSG 0.148 M MSG 0.077 M fructose 0.148 M fructose Data Analysis The same calculation and interpretation for the Generalization Score was used as described in Experiment II of Chapter 3. One-way analyses of variance (ANOVAs) were conducted for each test stimulus to determine the presence of differences among groups followed by detailed Bonferroni-adjusted paired comparisons. Separate one-sample t-tests testing group means against both of the null hypotheses 1.0 (the test compound was similar to the standard stimuli) and 0 (the test compound was similar to the comparison

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82 stimuli) were performed. The conventional p < 0.05 value was used as the statistical rejection criteria. Data for the negative control test were analyzed using a one-sample Binomial analysis with null hypothesis = 0.5, which corresponds with the chance level of performance. Results S, Q, and C for the two concentrations of each of the 6 test comp C. standard stimulus, the profile was still In addition, Bonferroni post hoc comparisons of the re revealed that S, Q, and C groups did not differ from each other, while Results for groups N ounds can be seen in Figures 4-1 through 4-10. Test Stimulus: Sodium Gluconate 0.376 M sodium gluconate Figure 4-1 shows the behavioral profile obtained for 0.376 M sodium gluconate. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed thatthere was a significant difference between one or more of the groups (F(3, 20) = 68.7, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: N > S = Q =Separate one-sample t-test analyses of the Generalization Scores (see Table 4-4) showed that all groups were statistically different than 1.0. In addition, the N and S groups werestatistically different than 0. These results show that although the N group did not treat 0.376 M sodium gluconate exactly like a predominantly NaCl-like. Generalization Sco the N group differed from all three.

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83 0.668 M sodium gluconate Figure 4-2 shows the behavioral profile obtained for 0.668 M sodium gluconate. An ANOVA comparing Generalization Scores obtained from the 4 groups revealedthere was a significant difference between one or more of the groups (F(3, 20) = 44.4, p0.01). A post-hoc analysis with Bonferroni adjustment indicated that the Generalization Scores for the different groups could be ordered in the following way: N > Q = S = C. Separate one-sample t-test analyses of the Generalization Scores (see Table 4-5) showedthat all groups were statistically different than 1.0, although with a Bonferroni adjustmefor multiple t-tests, the N group failed to reach statistical significance. Thus, statistical evidence exists to support the claim th that < nt at the N group was standard-like. After Bonferroni lied to the results from the t-test aimed at discerning which groups differsis there Separate one-sample t-tests of the Generalization Scores (see Table 4-7) determined that correction was app ed statistically from 0 (that the test stimulus was comparison-like), the analyrevealed that only the N and Q groups differed from 0. Thus, there is a predominant NaCl-like component in 0.668 M sodium gluconate and possibly also a slight quininelike component. Performance to the training stimuli used during testing for 0.376 M and0.668 M sodium gluconate is shown in Table 4-6. Test Stimulus: Denatonium 0.131 mM denatonium Figure 4-3 shows the behavioral profile obtained for 0.131 mM denatonium. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed thatwas a significant difference between one or more of the groups (F(3, 20) = 508.9, p < 0.01). A post-hoc analysis with Bonferroni adjustment showed that the Generalization Scores for the different groups could be ordered in the following way: Q > S > C = N.

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84 all groups except the Q group differed significantly from 1.0 (the test compound was standard-like). Thus, denatonium is statistically not different than the training the t-test comparing the Generalization ealed that both the Q and S groups were statistically greater than 0, indication S. led different than 0, indicating that the N, S, and C groups had performance that n-like. This test compound, 0.360 mM denatonium, was clearly quinine-like. there concentrations of quinine. On the other hand Scores to 0 rev ting that 0.131 mM denatonium is treated behaviorally as predominantly quinine-like, and very slightly sucrose-like. 0.360 mM denatonium Figure 4-4 shows the behavioral profile obtained for 0.360 mM denatonium. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 20) = 258.1, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the GeneralizaScores for the different groups could be ordered in the following way: Q > C = N =Separate one-sample t-test analyses of the Generalization Scores (see Table 4-8) reveathat the Q group is not statistically different than 1.0 (i.e., the test compound is standard-like), while all of the other groups are different. Furthermore, only the Q group isstatistically was compariso Performance to the stimulus control concentrations for both 0.131 mM and 0.360 mM denatonium is shown in Table 4-9. Test Stimulus: Maltose 0.077 M maltose Figure 4-5 shows the behavioral profile obtained for 0.077 M maltose. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed thatwas a significant difference between one or more of the groups (F(3, 20) = 25.7, p <

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85 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: Q > S > C = N. Separate one-sample t-test analyses of the Generalization Scores (see Table 4-10) revealed that all of the groups differed significantly from 1.0 but that only the S and Q groups differed from 0. This indicates that there was both a sucrose-like and quinine-likecomponent to the maltose. Since the post hoc analyses of the ANOVA revealed that Q > r Q component to the compound than an S hould be stated again that the Generalization Score does not reflect the intensere N. that the post hoc analysis of the ANOVA showed no differences d Q group means. These results, taken together, indicate that there is an equaln S, it can be concluded that there is a stronge component. It s ity of the taste quality, but it is an indicator of how similar the test compound is to the standard stimulus concentrations. 0.148 M maltose Figure 4-6 shows the behavioral profile obtained for 0.077 M maltose. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that thwas a significant difference between one or more of the groups (F(3, 20) = 28.9, p <0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for the different groups could be ordered in the following way: S = Q > C > Separate one-sample t-test analyses of the Generalization Scores (see Table 4-11) revealed that all of groups differed statistically from a hypothesized mean of 1.0. Additionally, the S and Q groups also differed significantly from a hypothesized mean of 0. It is interesting between the S an sucrose-like and quinine-like component arising from 0.148 M maltose. These data might reveal the basis of taste cues which allow discrimination of maltose and sucrose i

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86 rats. Performance to the training stimuli for both concentrations of maltose can be seen in Table 4-12. Test Stimulus: Potassium Chloride (KCl) 0.376 M KCl Figure 4-7 shows the behavioral profile obtained for 0.376 M KCl. An ANOVcomparing Generalization Scores obtained from the 4 groups revealed that there was asignificant difference between one or more of the groups (F(3, 20) = 6.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores forthe different groups could be ordered in the following way: Q A > C = N = S (also Q>N=S)Separate one-sample t-test analyses of the Generalization Scores (see Table 4-13) show that all groups are statistically different than 1.0 (standard-like). After Bonferroni adjustment for multiple comparisons, only the performance of the Q and N groups differed from 0 (comparison-like). Collectively, these data indicate that while KCl is pred ominantly quinine-like there is also a NaCl-like component. The profile is that of a alities contributing at least some portion to the overall 0.668a r complex taste, with two qu experience M KCl Figure 4-8 shows the behavioral profile obtained for 0.668 M KCl. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was significant difference between one or more of the groups (F(3, 20) = 5.4, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores fothe different groups could be ordered in the following way: Q > C = N = S. Separaone-sample t-test analyses of the Generalization Scores (see Table 4-14) show that performance in all groups was statistically different than it was for their respective te

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87 standards. Performance for each of the groups, N, Q, and C, was significantly different than 0, indicating that portions of those three qualities contributed to the taste of KCl. The predominant aspects are quinine and citric acid followed by NaCl, which coincidnicely with data from previous studies, suggesting KCl has a bitter, sour, salty(Morrison, 1967). Data concerning the performance to the training stimuli are shown i es taste n Test A es for te y s result o a pect. A Table 4-14. Stimulus: Monosodium Glutamate 0.077 M MSG Figure 4-9 shows the behavioral profile obtained for 0.077 M MSG. An ANOVcomparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 20) = 33.5, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scorthe different groups could be ordered in the following way: S > N > Q = C. Separaone-sample t-test analyses of the Generalization Scores (see Table 4-16) show that performance for all groups differed from the standard (hypothesized mean of 1.0). Onlthe S and N groups differed from 0, indicating that there was both a NaCl-like and sucrose-like component to the 0.077 M MSG. The post hoc analysis of the ANOVA indicated that there was a greater sucrose-like component than NaCl-like. Thisuggests that MSG, at this concentration, is predominantly sucrose-like but there is alsNaCl-like as 0.148 M MSG Figure 4-10 shows the behavioral profile obtained for 0.148 M MSG. An ANOVcomparing Generalization Scores obtained from the 4 groups revealed that there was asignificant difference between one or more of the groups (F(3, 20) = 32.2, p < 0.01). A

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88 post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scorthe different groups could be ordered in the following way: N > S > C = Q. Separateone-sample t-test analyses of the Generalization Scores (see Table 4-17) show that performance for all groups differed from the standard (hypothesized mean of 1.0). Othe S and N groups differed from 0, indicating that there was both a NaCl-like and sucrose-like component to the 0.077 M MSG. The post hoc analysis of the ANOVA indicated that there was a greater NaCl-like component than sucrose-like. This ressuggests that MSG, at this concentration, is predominantly NaCl-like but there is also a sucrose-like aspect. Taken together, both profiles for MSG would suggest there is good evidence to postulate that the taste of MSG is more sucrose-like or NaCl-like, depenon the concentration, than anything else, but that it is definitely a combination of the two compounds and there is little evidence to support the claim that MSG is representative of a fifth taste quality in rats. The performance to the training stimuli is shown in Table 4-18. Test Stimulus: Fructose es for nly ult ding 0.077ion the S, Q, and C groups differed from 0. This indicates that there was a sucrose-like, M fructose Figure 4-11 shows the behavioral profile obtained for 0.077 M fructose. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there was a significant difference between one or more of the groups (F(3, 20) = 30.1, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the GeneralizatScores for the different groups could be ordered in the following way: Q > S > C = N. Separate one-sample t-test analyses of the Generalization Scores (see Table 4-19) revealed that all of the groups differed significantly from 1.0 except the Q group, but that

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89 quinine-like, and citric acid-like component to the fructose. Since the post hoc analysesof the ANOVA revealed that Q > S > C, it can be concluded that ther e is a stronger Q component to the compound than an S It should be stated again that the Gene. re alization from a hypothesized mean that the post hoc analysis of the ANOVA showed no differences betwes an e component. ralization Score does not reflect the intensity of the taste quality, but it is an indicator of how similar the test compound is to the standard stimulus concentrations0.148 M fructose Figure 4-12 shows the behavioral profile obtained for 0.077 M fructose. An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that thewas a significant difference between one or more of the groups (F(3, 20) = 36.8, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the GenerScores for the different groups could be ordered in the following way: S = Q > C = N. Separate one-sample t-test analyses of the Generalization Scores (see Table 4-20) revealed that all of groups differed statistically from a hypothesized mean of 1.0. Additionally, the S, Q and C groups also differed significantly of 0. It is interesting en the S and Q group means. These results, taken together, indicate that there iequal sucrose-like and quinine-like component arising from 0.148 M fructose. Thesdata might indicate that fructose and sucrose would be discriminable to rats. Performance to the training stimuli for both concentrations of fructose can be seen in Table 4-21. Performance of Water Group The water (W) group was on a different schedule than the other groups because discrimination of water from quinine proved a difficult task. Results for the different phases of training are shown in Figure 4-13. First, the W group was performing poorly

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90 overall when only the third-highest concentrations of all compounds were used intraining. When comparing their performance to all compounds, it was clear that the problem primarily occurred between water and quinine (data not shown). Only the highest concentration of quinine and water were next used to help rats discriminatbetween the compounds. See summary of training schedule (Table 4-3) for number of days at each manipulation. Next, the number of sample licks and reinforcement licks was increased and appeared to improve the performance slightly (see Figure 4-13). A different water source (Publix purified water) was used, and that appeared also to help thrats learn the discrimination (see Figure 4-13), but we cannot rule-out that this could habeen based on potential chemical cues arising from the storage container (Song, Al-Taher, & Sadler, 2003). In fact, high levels of discriminability remained when the source was switched back to the in-house Millipore-purified water. A drop in the nu e e ve water mber of samee e g that ows that there was iability in the group (Figure 4-13). The overall performance to training comp ple licks and reinforcement licks resulted in a decrease in performance levels (spoint dt1-27 on Figure 4-13). Finally, returning to the 10 sample licks and 40 reinforcement licks appeared to increase levels of performance. Next, one of the two highest concentrations of each prototypical compound was used to retrain the rats to discriminate water from all 4 prototypical compounds; thconcentration present depended on which concentrations the other groups were usinsession. Finally, all of the concentrations were rotated through the training sessions and overall performance was better than chance, though the graph sh substantial var ounds is listed in Table 4-22; from the table, it can be seen that the rats did not perform as well as the other rats on many of the compounds, suggesting that maintaining

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91 stimulus control under these conditions is difficult. The training history of the anicannot be ruled out as a contributing factor to the poor performance. Discussion The results from the test compounds reveal that the rats can generalize the behavilearned from the prototypical training compounds to completely novel compounds. Presumably, if the behavior generalizes to untrained (i.e., no consequence was delivered for responses on these trials) compounds, then it supports the conclusion that the animais responding on the basis of a shared feature between the standard stimuli and the test stimulus, most likely related to taste quality. The fact that performance to the trastimuli remained high and the profiles obtained were distinct for the different compounds, suggests the animals were not just arbitrarily responding to test compounds. Some of the profiles were complex, which further mals or l ining demonstrates the strength of this paradach size of ate anion size limiting passage of sodium through tight junctions; NaCl, with the reons igm to capture not only pure taste qualities, but also compounds which appear to possess two or more distinct taste qualities (e.g., MSG, KCl). Sodium Gluconate The profiles for both concentrations of sodium gluconate were similar to eother, although there was more of a quinine-like component present for the highest concentration tested. These data may help to explain differences found between NaCl and sodium gluconate in other behavioral data in the literature. Sodium gluconate and NaCl are thought to activate different salt transduction pathways due to the large the glucon latively smaller anion is thought to be capable of passing through the tight junctiresulting in activation of paracellular receptor sites (Elliot & Simon, 1990; Formaker &Hill, 1988; Ye et al., 1991, 1993, 1994). Therefore, it was assumed that when amiloride,

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92 an epithelial sodium channel (ENaC) blocker, was applied to the oral cavity, the transduction of both salts would be impaired differentially because sodium gluconattheoretically would not have a pathway to activate. Geran and Spector (2000), howeshowed that although amiloride treatment significantly shifted sodium gluconate detection thresholds more than it did for NaCl thresholds, rats were still able to pe e ver, rform aboveter m l., e and high concentrations of sodium gluconate, that is responsible for onent present in the higher concentration. Dena. chance at higher concentrations (0.1, 0.2 and 0.4 M) of the organic salt. They laconcluded that it did not seem likely that the higher levels of performance to sodiugluconate was related to Na + reaching the transcellular receptors (see Geran & Spector, 2000, 2004 for further discussion). Their conclusion leaves open the possibility that a non-sodium cue was being detected at the high sodium gluconate concentrations. The data in this experiment, showing that there is a quinine-like component to sodium gluconate, however slight, might serve as a basis for the difference in performance. It would be interesting to know which gustatory receptors sodium gluconate, atvarious concentrations, activates. Perhaps it additionally activates taste receptors belonging to the T2R family, which have been shown to be involved in bitter taste transduction (Chandrashekar et al., 2000; Gilbertson & Boughter, 2003; Zhang, et a2003). If not, then perhaps there is some convergence that occurs downstream of receptor signaling, such as a shared component in the signal transduction pathway activated by quinin the quinine-like comp tonium That denatonium was treated as similar to the Q group standard was not surprisingIn fact, this was predicted based on the work of Spector and Kopka (2002) that showed Sprague-Dawley rats cannot discriminate between the two purported bitter tasting

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93 compounds. Their results were controversial in light of other published findings suggesting that rats could discriminate between the two compounds (Caicedo & Roper, 2001) because their application to taste receptor cells in situ resulted in differential calcium responses in separate subpopulations of cells, which they interpreted to imply discrimination at the cellular level. The data presented here together with the data from Spector and Kopka (2002) could argue that even if different populations of receptor cells are activated with exposure to the two ligands, the signal that is used by the animal t o guide The ped rences th e two compounds, havioral data here and in Spector and Kopka (2002). Malto of ther & behavior is apparently the same for both quinine and denatonium. Alternatively, activation of potentially separate signal processing pathways results in the same behavioral outcome. See Figure 4-14 for a diagram outlining both possibilities. designs of the current experiment, and that of Spector and Kopka (2002) are not equipto determine which of the two might be the case. These two findings could be reconciled if one considers that there were diffebetween levels of investigation. That is, the behavior of the rats represents the output of the entire gustatory system, whereas the findings from measurement of calcium responding (Caicedo & Roper, 2001) are based on the initial stages of stimulus processing, which occur in a discrete subpopulation of receptor cells. Therefore, it is possible that convergence of information somewhere in the gustatory neuraxis from bodenatonium and quinine plays a role in the perceptual similarity of th which was supported by be se The fact that maltose did not fully generalize to sucrose makes sense in lightprevious studies showing that maltose and sucrose are discriminable from one ano(Nissenbaum and Sclafani, 1987; Spector and Grill, 1988; Spector, Markison, St. John

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94 Garcea, 1997). The results from this paradigm, along with those from previous worksuggests that the discrimination of maltose from sucrose is not based merely using intensity cues, but is likely guided by other sideband tastes, described here. This finding, above all others, might demonstrate the true strength of this approach. It gives insight into how similar the test compound is to each of the prototypical stimuli. It is interesting, however, that there is a substantial quinine-like (assumed to be inherently aversive) component to the maltose profiles because maltose has beenestablished as a preferred stimulus in rats (Davis, & Smith, 1992; Richter & Campbell, 1940; Sclafani, & Clyne, 1987; and Sclafani, & Mann, 1987; Sc lafani & Nissenbaum, 1987)s r at ulus is related to the extensive use of this salt atimes in rats that NaCl is behaviorally discriminable from KCl (St. John, Markison, & It has also been shown to cross-generalize to other sugars, like sucrose, in studieemploying the conditioned taste aversion approach in the rat (Sako, Shimura, Komure, Mochizuki, Matsuo, & Yamamoto, 1994; Spector & Grill, 1988), but not in the hamste(MacKinnon, Frank, Hettinger, & Rehnberg, 1999). The issue at hand highlights an interpretive requirement concerning the profiles obtained here. It must be stressed ththe height of the bar does not imply intensity of the compound, but merely indicates the presence of the component. That is, when a test compound fully generalizes to the standard of a given group, it says nothing of the intensity of that signal, but only that the taste arising from the test compound fits into the range that was trained to define the standard stimulus. Potassium Chloride The choice to include KCl as a taste stim s a taste stimulus in other studies. Morrison (1967) showed in his study with rats that KCl produced a profile that was distinct from NaCl and it has also been shown many

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95 Spector, 1997; Kopka, Geran, & Spector, 2000; Spector, Guagliardo, & St. John, 1996St. John, Markison, Guagliardo, Hackenberg, & Spector, 1995). Potassium chloride salt that tastes salty-bitter to humans (e.g., van der Klaauw & Smith, 1995). Inemploying behavioral generalization (CTA), non-sodium salts and acids are categosimilarly by rats (Nowlis, Frank, & Pfaffmann, 1980). One of the goals here was to use the present paradigm to obtain a behavioral description of KCl in rats, which mito determine the qualitative characteristics used by rats to identify the taste of KCl.The profiles obtained for both concentrations of KCl were indicative of a complex taste. There were components of quinine-like, citric acid-like, and NaCl-like tastes, which may pr ; is a studies rized ght help ovide insight into the differential taste cues that a rat might use to te the salt from NaCl. It would be interesting to know if adulteration with amiloted hn, this discrimina ride would cause NaCl to yield a profile that looked like KCl, as would be predicfrom behavioral work showing the two compounds are treated similarly with oral amiloride application (Hill, Formaker, & White, 1990; Spector, Guagliardo, & St. Jo1996). Technically, this would be difficult because it would be important to maintain stimulus control of the NaCl training stimuli, and if everything was adulterated with amiloride (as is commonly done to assure constant exposure to the ENaC blocker), would be impossible. It might be feasible, however to present the animals with a few trials at the end of the session in which amiloride is added to NaCl. Monosodium Glutamate The inclusion of MSG as a test compound was in response to the growing acceptance for umami taste as a distinct fifth quality. As stated previously, the evidence for a separate MSG-like taste quality is mixed for rodents, but there are examples supporting the existence of this taste quality where MSG is distinguishable

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96 from sucrose and NaCl (Heyer, Taylor-Burds, Mitzelfelt, & Delay, 2004; Stapleton, Luellig, Roper, & Delay, 2002). The NaCl-like aspect of MSG is often controlled for using amiloride to suppress the taste of NaCl or adding NaCl to sucrose solutions to account for the salt taste present in MSG (e.g., Heyer, Taylor-Burds, Mitzelfelt, & Delay, 2004). Heyer, Taylor-Burds, Mitzelfelt, and Delay (2004) conclude that sweet (sucrose) and umami (MSG) afferent signaling may share a similar signaling pathway either through a common taste receptor with high affinity for both prototypical compounds, some similar downstream transduction mechanism, or possibly through cell-cell interactions (e.g., see Figure 4-14 for similar explanation). Some of those possibilities have been supported by work using the mouse model that indicates that the two transduction processes do share similar components (Zhang et al., 2003; Zhao et al., 2003). Taken together, it is not surprising that we found both a NaCl-like and sucrose-like profile for the concentrations of MSG tested. The data from the present experiment extend previous findings suggesting that, in the rat, the taste quality associated with MSG is not uniquely different than that arising from sucrose and NaCl, but is likely a combination of the two. It would be interesting to test more amino acids to uncover whether they are similarly categorized by rats to be a combination of the putative four basic tastes, or if they will yield a profile as yet unseen. Moreover, perhaps different amino acids would fall into categories, based on similarity of responding, that would match those interpreted to be sweet tasting and bitter tasting (Iwasaki, Kasahari, & Sato, 1985; Nelson et al., 2002). In summary, the findings from the present study have provided evidence for the usefulness of this paradigm to examine the perceptual taste qualities of novel compounds

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97 for which the animals have never received exp licit training. That rats will respond to novel stimuli thelevel to w stimulus (e.g., q and datonaCts that the two Additionally, the behavioral paradigms can potentially be used to indicate other taste qualities that might play a part in the overall perceptual experience genpound. This is especially rmatihemical stimuli that have company g arch to pursun the findingent study, but first the tione must belly defined. Chapter 5 provides a discussion poked nearly identical to maltose was unexpected. Again, it is interesting that there is a substantial quinine-like (assumed to be inherently aversive) component to the fructose profiles because fructose, like maltose, is a preferred stimulus and it is a component of the sucrose molecule. It has been shown to cross-generalize to other sugars, like sucrose, in studies employing the conditioned taste aversion approach in the rat (e.g., Nissenbaum & Sclafani, 1987; Nowlis, Frank, & Pfaffmann, 1980). On the other hand, Ramirez (1994) showed that rats differentially avoided consuming sucrose and fructose following aversion training of each, indicating that the two sugars differ in some aspect of quality after he attempted to control for intensity. Additionally, experimental evidence from a human psychophysical task claims that with many sugars (including fructose), the bitterness of sweeteners decreases as concentration increases (Sciffman et al., 1995). Finally, it stands repeating that the height of the bar does not imply intensity of the compound, but merely indicates at same hich they respond to the standard uinine en ium, and N l and sodium gluconate) sug ges compounds likely share similar qualitative features erated by a com info ve regarding c lex tastes. There are m excitin venues of resea e give s of the pres limita s of the procedur carefu to that int. Fructose The fact that fructose generated a profile that loo

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98 the presence of the component. That is, when a test compound fully generalizes to the of a givup, it saysthe inal, that the taising fcompound fits into the range that was trained to define the sd stimu standard en gro nothing of tensity of that sign but only ste ar rom the test tandar lus.

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99 Table 4-1. Overview of experimental groups Group N Stomparison Stimuli andard C 1) N 6 NaCl Sunine, Citric Acid, Water* crose, Qui 2) S6SucroNae, Citric Acid, Water) Q6 inine NaCl, S, Citric Aater* ) C6 ric AciNaCl,, Quinine, ) W6 ter NaCl,, Quinine, Acid ateas ultimndonedmparis se Cl, Quinin 3 Qu ucrose cid, W 4 Cit d Sucrose Water 5 Wa Sucrose Citric *W r w ately aba as a co on stimulus Table 4-2. Training schedule for N, S, Q, and C groups Sessions Phase Limited hold (s), t (s) Schedule timeou 1-6 Spoutning NN/A trai /A 7-1 0 Side trai180, tant -13 Altern15, ating 25 Discrim I 10, dom37 Only Qs 10, dom 38-40 Disc. I. (N,S,Q,C) 10, 20 Semi-random 41-47 Discrimination II 5, 20 Semi-random Limd hold ihe maximuount of time alorns alternatitedly uertain numbct responsade. Thisd wthe ssi the secon, theesmuli were pd inizeks dur semom sule ning 0 Cons 11 ation 10 Altern 14ination 20 Semi-ran 26, W grp 20 Semi-ran 48-64 Partial Reinforcement 5, 20 Semi-random 65-90 Testing 5, 20 Semi-random ite s t m amntil a c lotted fer of corre a respo e. Duringes were m on, a stimulus predetermine was presenalternation repeatedcriterion as 6 in first seing a on, 4 in dched and 2 in final s sion. St i resente random d bloc i-rand

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100 Table 4-3. Training parameters for W group. Sessi oPhase ple licks, RLimited holdtimout (s) ns Sam einf. licks (s), e Stimuli 1-6 Spout training 5,N/A Millipore wa 20 ter 710Sng 5, 2013rd conc., e wate1-1ion 5, 201 conMe water 14-3Discrimination I 5, 20 10, 20 3rd highest conc., Millipore water re water & 0.827 nine 53-61 Discrimin5, 20 rified water & 0.827 M qDition5,ore water & 0.827 mne 0-7Dition I 10, 4Me water & 0.827 nine 77-8Discrimination II 10, 40 5, 20 Me water &0.827 mM or 0.360 mM quinine re water, all trations all compounds ide traini 80, 0 highest Millipor r 1 3 Alternat 5 10 3 rd highest c., illipor 7 38-52 Discrimination I 10, 40 10, 20 MillipomM qui ation I 10, 40 20 Publix pu uinine 6 2-6 9 s crimina I 5 20 Millip M quini 7 6 scr imina 0 5, 20 illipor mM qui illipor either 1 82-90 Discrimination III 10, 40 5, 20 Millipoconcen

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101 Table 4-4. Results from one-sample t-tests for 0.376 M NaGlucon ate Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted pvalue N 5 -3.34 0.02 0.17 14.01 < 0.01 < 0.01 S 5 58.84 < 0.01 < 0.01 6.62 < 0.01 < 0.01 Q 5 15.48 < 0.01 < 0.01 0.49 0.64 1.00 C 5 -26.81 < 0.01 < 0.01 -1.06 0.34 1.00 Table 4-5. Results from one-sample t-tests for 0.668 M NaGlucona te Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted pvalue N 5 -3.06 0.03 < 0.225 10.06 < 0.01 0.01 S 5 -19.13 < 0.01 < 0.01 4.38 < 0.01 0.06 Q 5 -32.01 < 0.01 < 0.01 5.71 < 0.01 0.02 C 5 -28.43 < 0.01 < 0.01 1.53 0.19 1.00 Table 4-6. Performance to training stimuli during sodium gluconate testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 86.5 2.5 89.5 4.3 88.2 3.1 96.4 1.5 0.376 96.0 0.7 97.0 1.5 96.4 0.9 92.6 1.3 0.668 98.0 0.4 99.1 0.9 98.6 0.6 94.0 2.0 NaCl (M) 1.07 93.2 1.5 92.6 3.0 96.0 1.0 93.5 2.4 0.042 97.4 0.7 87.3 1.7 84.8 2.7 88.6 1.7 0.077 96.8 1.4 94.6 2.2 92.2 3.0 85.7 3.2 0.148 96.5 2.4 97.2 0.9 98.1 0.6 95.6 1.4 Sucrose (M) 0.421 97.8 1.0 96.0 0.9 81.3 4.8 83.0 3.5 0.027 91.5 1.6 86.1 3.0 90.6 2.1 83.8 2.5 0.131 95.9 1.2 84.0 1.3 89.2 1.0 78.2 5.1 0.360 91.6 3.0 86.3 1.9 91.6 2.1 83.0 3.3 Quinine (mM) 0.827 95.1 0.9 92.3 2.3 95.8 0.8 78.9 4.6 2.04 97.6 1.6 96.6 1.7 70.5 4.8 78.3 3.3 10.4 93.6 2.8 95.4 1.8 84.3 2.7 88.9 2.0 28.2 96.7 1.0 95.2 3.2 93.3 2.2 94.9 1.0 Citric Acid (mM) 64.3 98.8 0.8 93.5 5.4 99.3 0.5 93.5 2.4

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102 Table 4-7. Results from one-sample t-tests for 0.131 m M denatonium Test against 1.0 Test against 0 Grp df t p-value t p-value Adjusted p-value Adjusted p-value N 5 -68.14 < 0.01 < 0.01 0.26 0.80 1.00 S 5 -38.43 < 0.01 < 0.01 7.06 <<4<0.01 0.01 Q 5 -3.32 0.02 0.17 5.38 < 0.01 0.01 C 5 48.70 < 0.01 < 0.01 1.71 0.15 1.00 Table 4-8. Results from one-sample t-tests for 0.360 m M denatonium Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 5 -69.45 < 0.01 < 0.01 1.10 0.32 1.00 S 5 -34.50 < 0.01 < 0.01 0.69 0.52 1.00 Q 5 0.29 0.79 1.00 3<-< 5.38 < 0.01 0.01 C 5 21.55 < 0.01 0.01 1.70 0.15 1.00 Table 4-9. Performance to training stimuli during denatonium te sting Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 86.5 2.5 89.5 4.3 88.2 3.1 96.4 1.5 0.376 96.0 0.7 97.0 1.5 96.4 0.9 92.6 1.3 0.668 98.0 0.4 99.1 0.9 98.6 0.6 94.0 2.0 NaCl (M) 1.07 93.2 1.5 92.6 3.0 96.0 1.0 93.5 2.4 0.042 97.4 0.7 87.3 1.7 84.8 2.7 88.6 1.7 0.077 96.8 1.4 94.6 2.2 92.2 3.0 85.7 3.2 0.148 96.5 2.4 97.2 0.9 98.1 0.6 95.6 1.4 Sucrose (M) 0.421 97.8 1.0 96.0 0.9 81.3 4.8 83.0 3.5 0.027 91.5 1.6 86.1 3.0 90.6 2.1 83.8 2.5 0.131 95.9 1.2 84.0 1.3 89.2 1.0 78.2 5.1 0.360 91.6 3.0 86.3 1.9 91.6 2.1 83.0 3.3 Quinine (mM) 0.827 95.1 0.9 92.3 2.3 95.8 0.8 78.9 4.6 2.04 97.6 1.6 96.6 1.7 70.5 4.8 78.3 3.3 10.4 93.6 2.8 95.4 1.8 84.3 2.7 88.9 2.0 28.2 96.7 1.0 95.2 3.2 93.3 2.2 94.9 1.0 Citric Acid (mM) 64.3 98.8 0.8 93.5 5.4 99.3 0.5 93.5 2.4

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103 Table 4-10. Results from one-sample t-tests for 0.077 M maltose T Test against 1.0 est against 0 Grp df t p-value ApAv djusted p-value t -value djusted p-alue N 5 -42.68 < 0.01 < 0.01 0.5<11< 0.< 0.20.0. 158 0.88 1.00 S 5 -9.89 < 0.01 < 0.01 .79 0.01 0.02 Q 5 -6.98 < 0.01 < 0.01 .37 01 01 C 5 -14.34 < 0.01 < 0.01 .98 03 25 Table 4-11. Results from one-sample t-tests for 0.148 M maltose T Test against 1.0 est against 0 Grp df t p-value ApAv djusted p-value t -value djusted p-alue N 5 -<01<<6< 0.< 0.4< 0.0. 56.27 < 0.01 0.01 .69 0.52 1.00 S 5 -11.23 < 0.01 < 0.01 3.75 0.01 0.01 Q 5 -10.01 < 0.01 < 0.01 .93 01 01 C 5 -16.59 < 0.01 < 0.01 .21 01 07 Table 4-12. Performance to training stimuli during maltose testin g Group NaCl Sucrose Quinine Citric Acid Solution Conc. 1110.4 98.7 0.9 98.2 1.2 97.2 1.0 95.0 1.1 28.2 99.0 0.7 96.9 1.5 97.7 1.1 96.5 0.6 64.3 98.0 0.8 98.4 1.0 97.8 1.5 98.3 0.7 Mean SE Mean SE Mean SE Mean SE 0.107 92.5 1.4 96.3 2.3 91.6 2.2 91.9 1.7 0.376 96.6 1.4 97.4 0.8 94.9 1.1 94.5 1.9 0.668 98.8 0.6 98.9 1.1 99.3 0.5 96.5 1.6 NaCl (M) 1.07 94.4 2.3 96.6 1.1 93.6 1.8 92.8 1.6 0.042 94.6 1.9 89.7 2.7 89.7 3.0 91.9 3.3 0.077 93.3 1.2 94.6 1.8 95.4 0.8 95.3 2.6 0.148 94.9 1.5 97.9 0.8 00.0 0.0 00.0 0.0 Sucrose (M) 0.421 94.4 1.3 96.8 1.0 96.5 2.5 94.4 1.1 0.027 92.5 3.8 93.4 2.3 89.0 2.0 90.7 1.5 0.131 92.8 0.9 93.6 3.3 94.3 0.7 89.2 3.0 0.360 91.7 2.8 95.1 1.7 97.1 0.5 86.9 3.5 Quinine (mM) 0.827 93.3 1.8 96.1 2.0 93.2 2.2 77.2 3.2 2.04 99.2 0.5 97.5 1.1 82.5 1.6 90.4 1.5 Citric Acid (mM)

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104 Table 4-13. Table of t-test statistics for 0.376 M KCl Test against 1.0 Test against 0 Grp df t p-value adjusted p-value t p-value Adjusted p-value N 5 -24.52 < 0.01 < 0.01 8.70 < 0.01 < 0.01 S 5 -14.24 < 0.01 < 0.01 2.58 0.05 0.40 Q 5 -4.99 < 0.01 0.03 6.06 < 0.01 0.01 C 5 -9.56 < 0.01 < 0.01 4.20 < 0.01 0.07 Table 4-14. Table of t-test statistics for 0.668 M KCl Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 5 -18.08 < 0.01 < 0.01 6.38 < 0.01 0.01 S 5 -13.37 < 0.01 < 0.01 2.98 0.03 0.25 Q 5 -4.88 < 0.01 0.04 6.35 < 0.01 0.01 C 5 -6.37 < 0.01 0.01 4.67 < 0.01 0.04 Table 4-15. Performance to training stimuli during KCl testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 93.5 1.3 95.2 1.8 87.2 1.6 97.5 1.2 0.376 95.9 1.3 96.5 1.8 99.4 0.4 97.6 0.8 0.668 99.7 0.2 93.7 3.5 96.7 1.3 94.7 1.3 NaCl (M) 1.07 96.8 1.2 97.3 1.8 91.3 4.5 92.8 2.0 0.042 95.4 1.8 87.4 2.2 93.0 1.4 95.8 1.2 0.077 88.5 1.7 93.6 1.6 95.5 2.5 98.2 0.8 0.148 94.7 1.6 96.9 1.4 96.0 1.5 99.0 0.7 Sucrose (M) 0.421 94.0 1.7 93.6 3.2 90.7 3.6 94.7 1.1 0.027 98.1 0.8 90.8 3.1 93.1 1.7 86.2 2.4 0.131 96.5 0.6 87.7 2.6 93.4 0.7 84.0 4.7 0.360 98.9 0.7 92.6 4.2 93.8 1.5 89.2 1.9 Quinine (mM) 0.827 95.2 1.0 92.8 3.0 95.8 0.5 86.3 1.6 2.04 97.2 0.8 95.2 2.2 87.0 1.3 84.7 2.2 10.4 94.3 1.3 87.8 6.1 87.5 4.6 92.6 2.0 28.2 97.8 0.9 91.6 7.7 99.0 0.7 99.4 0.4 Citric Acid (mM) 64.3 98.1 0.8 96.9 2.3 98.7 0.6 95.4 2.0

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105 Table 4-16. Table of t-test statistics for 0.077 M MSG Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 5 -25.21 < 0.01 < 0.01 9.31 < 0.01 < 0.01 S 5 -6.85 < 0.01 < 0.01 8.91 < 0.01 < 0.01 Q 5 -28.00 < 0.01 < 0.01 0.86 0.43 1.00 C 5 -22.09 < 0.01 < 0.01 0.16 0.88 1.00 Table 4-17. Table of t-test statistics for 0.148 M MSG Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 5 -5.00 < 0.01 0.03 7.84 < 0.01 < 0.01 S 5 -18.56 < 0.01 < 0.01 12.66 < 0.01 < 0.01 Q 5 -38.35 < 0.01 < 0.01 0.87 0.43 1.00 C 5 -21.62 < 0.01 < 0.01 1.93 0.11 0.89 Table 4-18. Performance to training stimuli during MSG testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 87.9 2.5 91.3 3.5 90.9 1.7 92.7 1.9 0.376 94.5 2.2 95.8 1.8 96.3 0.9 91.9 1.4 0.668 98.6 0.5 94.9 5.1 100.0 0.0 90.7 2.6 NaCl (M) 1.07 95.7 1.0 97.0 3.0 95.7 1.1 96.7 1.2 0.042 94.1 1.7 85.6 1.9 83.9 3.0 87.9 2.3 0.077 93.7 1.2 95.0 0.8 86.7 2.4 94.4 1.4 0.148 98.1 0.8 96.8 1.1 95.4 1.2 92.2 2.1 Sucrose (M) 0.421 98.7 0.9 97.6 1.2 95.2 1.8 96.6 1.5 0.027 95.3 1.5 92.9 2.5 94.1 1.2 80.0 2.5 0.131 93.4 1.4 92.7 2.2 93.6 1.5 85.7 2.9 0.360 100.0 0.0 95.3 1.8 96.5 1.2 82.5 1.2 Quinine (mM) 0.827 96.1 1.6 94.4 3.5 96.0 1.0 82.8 2.7 2.04 91.4 2.4 99.3 0.7 69.1 5.8 83.3 1.7 10.4 97.0 0.9 95.5 1.7 81.1 2.5 90.2 1.7 28.2 97.6 1.6 98.2 1.1 91.0 3.6 93.9 1.9 Citric Acid (mM) 64.3 94.3 1.9 98.3 1.7 98.2 1.2 97.7 0.5

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106 Table 4-19. Table of t-test statistics for 0.077 M fructose Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 5 -68.25 > 0.01 > 0.01 1.28 0.26 1.00 S 5 -15.90 > 0.01 > 0.01 9.59 > 0.01 > 0.01 Q 5 -4.00 0.01 0.08 7.52 > 0.01 > 0.01 C 5 -30.18 > 0.01 > 0.01 6.08 > 0.01 0.01 Table 4-20. Table of t-test statistics for 0.148 M fructose Test against 1.0 Test against 0 Grp df t p-value Adjusted p-value t p-value Adjusted p-value N 5 -44.44 > 0.01 > 0.01 0.01 0.99 1.00 S 5 -11.08 > 0.01 > 0.01 17.41 > 0.01 > 0.01 Q 5 -7.88 > 0.01 > 0.01 6.85 > 0.01 > 0.01 C 5 -17.51 > 0.01 > 0.01 2.58 0.05 0.40 Table 4-21. Performance to training stimuli during fructose testing Group NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE 0.107 92.8 1.3 97.0 1.0 89.9 2.4 95.9 1.2 0.376 95.4 1.8 97.4 1.2 97.7 0.7 94.3 2.2 0.668 95.5 1.9 97.7 1.0 99.2 0.5 94.2 2.3 NaCl (M) 1.07 97.4 0.5 98.3 1.1 98.6 1.0 93.3 1.4 0.042 96.9 0.6 90.3 2.2 80.9 2.3 92.0 3.2 0.077 92.7 1.6 94.0 2.0 93.3 2.0 95.2 1.2 0.148 96.1 1.8 98.1 1.2 96.5 1.9 95.2 2.0 Sucrose (M) 0.421 96.2 1.1 87.6 4.5 89.2 5.5 98.9 0.7 0.027 92.3 2.4 92.0 2.4 94.2 0.8 86.4 4.2 0.131 96.0 2.2 90.8 3.2 92.2 1.2 82.8 2.2 0.360 93.8 1.4 82.5 6.2 93.5 1.2 78.3 2.6 Quinine (mM) 0.827 96.0 2.7 100.0 0.0 96.0 0.8 93.6 1.3 2.04 97.5 1.7 77.9 7.8 28.3 2.7 60.0 4.4 10.4 97.1 1.9 98.5 0.9 94.1 1.9 90.5 2.6 28.2 100.0 0.0 100.0 0.0 95.3 2.8 99.0 0.3 Citric Acid (mM) 64.3 98.8 0.8 100.0 0.0 99.1 0.7 99.0 0.5

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107 Table 4-22. Performance to training stimuli for W group during dt3-5 through dt3-8. Group Water Solution Conc. Mean SE 0.107 69.3 5.9 0.376 82.1 2.8 0.668 96.6 1.4 NaCl (M) 1.07 85.8 6.2 0.042 97.4 0.7 0.077 77.4 5.1 0.148 92.9 2.1 Sucrose (M) 0.421 86.1 7.6 0.083 53.4 6.0 0.131 70.1 4.4 0.360 73.5 6.2 Quinine (mM) 0.827 100.0 0.0 2.04 89.0 3.6 10.4 80.7 6.0 28.2 87.1 5.7 Citric Acid (mM) 64.3 98.0 1.3 Water Water 86.1 2.0

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108 0.376 M NaGLUCONATEGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-1. Profile for 0.376 M NaGluconate. Mean Generalization Scores for each group are plotted. The novel concentration of NaCl generalized to NaCl training concentrations 0.668 M NaGLUCONATEGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-2. Profile for 0.668 M NaGluconate. Mean Generalization Scores for each group are plotted. The novel concentration of NaCl generalized to NaCl training concentrations

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109 0.131 mM DENATONIUMGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-3. Profile for 0.131 mM denatonium. Mean Generalization Scores for each group are plotted. The novel concentration of denatonium generalized to quinine training concentrations. 0.360 mM DENATONIUMGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-4. Profile for 0.360 mM denatonium. Mean Generalization Scores for each group are plotted. The novel concentration of denatonium generalized to quinine training concentrations.

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110 0.077 M MALTOSEGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-5. Profile for 0.077 M maltose. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to sucrose and quinine training concentrations. 0.148 M MALTOSEGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-6. Profile for 0.148 M maltose. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to sucrose and quinine training concentrations.

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111 0.376 M KClGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-7. Profile for 0.376 M KCl. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, quinine, and citric acid training concentrations. 0.668 M KClGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-8. Profile for 0.668 M KCl. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, quinine, and citric acid training concentrations.

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112 0.077 M MSGGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-9. Profile for 0.077 M MSG. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, and sucrose training concentrations. 0.148 M MSGGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-10. Profile for 0.148 M MSG. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, and sucrose training concentrations

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113 0.077 M FRUCTOSEGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-11. Profile for 0.077 M fructose. Mean Generalization Scores for each group are plotted. The novel concentration of fructose generalized to sucrose training concentrations. 0.148 M FRUCTOSEGroups NSQC Generalization Score -0.20.00.20.40.60.81.01.2 Figure 4-12. Profile for 0.148 M fructose. Mean Generalization Scores for each group are plotted. The novel concentration of fructose generalized to sucrose training concentrations.

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114 Water Group Performance00.10.20.30.40.50.60.70.80.91dt1-1dt1-3dt1-5dt1-7dt1-9dt1-11dt1-13dt1-15dt1-17dt1-19dt1-21dt1-r1dt1-r3dt1-r5dt1-r7dt1-r9dt1-r11dt1-r13dt1-r15dt1-r17dt1-r19dt1-r21dt1-r23dt1-r25dt1-r27dt1-r29dt1-r31dt1-r32dt1-r34dt2-2dt2-4dt3-1dt3-3dt3-5dt3-7Training Days% Correct QUININE WATER 0.827 mM Quinine0.827 mM Quinine & Water / 10 sample licks(Week off)(week off)/Publix Wt Week off/Calibration checkedMillipore vs 0.827 mM QuinineMillipore vs Highest concentrations of all stimuliMillipore vs all stimuli 5 licks10 licks Figure 4-13. Summary of performance for W group during training with water and quinine. Figure 4-14. Diagram outlining two possibilities for the level (peripheral or central) at which convergence of taste signal processing leading to the same behavioral output might occur.

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CHAPTER 5 GENERAL DISCUSSION Introduction The findings presented in the current collection of studies are novel to the field of taste quality research. Chapters 2 and 3 presented data from two experimental procedures that have not been previously used with taste stimuli, and the results suggest that they could be applied as an alternative method to examine taste quality discrimination and generalization. These paradigms could provide a functional context to interpret the outcomes of anatomical, pharmacological, and genetic manipulations of the gustatory system. Additionally, they may afford a means of testing hypotheses proposing how signals generated from taste stimuli give rise through some central process to the perception of taste quality. Moreover, these paradigms extend existing techniques that are crucial for linking neural activity with behavior, which is essential for understanding gustatory processing. Perhaps of most theoretical interest, however, is that the data presented in Chapters 3 and 4 provide evidence that the perception of taste quality is analytic. In other words, the behavior of the rats in this task was unambiguously categorical, and at least one of the four putative taste qualities, respectively represented by four prototypical stimuli, or a combination of them, was sufficient to describe each of the novel test compounds, including MSG. Delayed Match/Non-Match to Sample The experiment employing the delayed matching and non-matching (DMTS/DNMTS) approaches (Chapter 2) was successful in that rats indeed learned to 115

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116 respond to all types of trials presented at above chance levels. Although performance of the rats was statistically better than chance, applying the paradigm, as it currently stands, to address questions such as the temporal capacity of taste memory in the same/different discrimination would be complicated by the fact that the range between asymptotic performance and chance levels of responding is too limited to adequately measure changes in behavior. A strategy to lower the interstimulus delay would be the most plausible solution. Reliably higher levels of correct responding would be necessary to pursue any aims focused on attributing changes in performance to specific gustatory components. As mentioned in Chapter 2, asymptotic performance could potentially be improved by decreasing the delay period between the two sample stimuli. There have been accounts showing that animals acquire a similar task at a faster rate when the delay between the sample and a comparison stimulus is shorter, with a 0 s delay often yielding the best performance (e.g., Sargisson & White, 2001). Currently, the design of the gustometer prevents a delay shorter than 6 s. A substantial modification would be necessary to enable the delivery of two taste compounds in a shorter time period. In recent weeks, an adapter was constructed offsite to allow two sample spouts to be controlled by the same stepping motor; consequently, a reduced delay between samples would be achievable. There are still some technical aspects to overcome in order to use such an adaptor, however, and therefore empirical testing is not yet possible. Further development of this paradigm is almost certain to provide results indicating higher levels of performance.

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117 Another change that could be made to the design of the experiment, as it was presented, would be to increase the number of trials the rats were able to experience in a given session. Reducing both the interstimulus interval and the intertrial interval would help to achieve this; with shorter delays rats would be able to initiate more trials in a session. Another potential problem that may have decreased the performance of the rats in the present experiment was the likely loss of motivation through satiation. A water-restriction schedule was used to potentiate the reinforcer efficacy of water, but as animals sampled fluid in the trials, received fluid during intraoral rinses, and had access to water after correct responses, they became sated. Perhaps introducing a partial schedule of reinforcement might help improve performance while reducing the amount of fluid received during each trial. One problem with this suggestion, however, is that it would interfere with a benefit of the design. Typically, the water obtained during the reinforcement phase serves to rinse the oral cavity between trials so that adaptation to a particular compound does not occur. There have been prior accounts in the literature showing that adaptation to a stimulus can affect subsequent responses to taste stimuli in both rodents and humans (e.g., Bartoshuk, 1977; Galindo-Cuspinera et al., 2006). An alternative, though not mutually exclusive, explanation for the overall performance levels not surpassing 75% in this task might be related to the malfunction that occurred in the light timer during the first 110 days of training and testing. Published studies have used continuous exposure to lighted conditions as a chronic mild stressor (CMS) (Grippo & Johnson, 2002; Grippo, Beltz, & Johnson, 2003; Grippo, Moffitt, & Johnson, 2002), which results in various physiological changes in the animal, including an increase in circulating corticosterone and decreased behavioral responsiveness to

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118 sucrose (experimentally induced anhedonia). This might lead some research groups to speculate that hypothalamicpituitaryadrenal dysregulation following CMS may result in altered cognitive function. This remains an untested hypothesis. The development of the DMTS/DNMTS paradigm with taste stimuli has potential benefits for those interested in studying cognitive processes in animal models. For example, this task could be used to address the concepts of working memory, or short-term memory processing, as has been attempted in other sensory modalities. Evidence exists in audition and olfaction that shows that differential neuronal activity can be found during tasks requiring the animal to compare one stimulus to a second before making a response (Sakurai, 1990; Wiebe & Staubli, 2001). A process, that some have termed olfactory recognition memory, has been shown to have neural activity correlates in hippocampal theta cells (Wiebe & Staubli, 2001). Therefore, the potential for identifying similar underlying explanations and neural structures for taste behavior exists, especially using a task such as the DMTS/DNMTS task outlined here. Further development of the DMTS/DNMTS task could also facilitate efforts to assess intensity discrimination in animal models. In the same way that tasks similar to the one outlined here helped understanding of hue discrimination (e.g., see Wright, 1972), this task could help gustatory researchers realize the limits of taste quality and intensity discrimination of their animal models. Novel Taste Quality Generalization In Chapter 3, it was demonstrated that rats can learn to discriminate between stimuli thought to typify the four classic basic tastes. Further, they are capable of responding to test stimuli that have not been explicitly trained, in ways that would be predicted. When mixtures of two of the prototypical stimuli are presented, the behavioral

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119 responses of the rats can be used to generate behavioral profiles that indicate which of the compounds was present in the highest overall concentration. The fact that a rat does not respond entirely on the standard response spout when a familiar concentration of the standard has been mixed with a familiar concentration of the comparison reveals that the animal can detect that there is more than one qualitative taste component. This is a remarkable aspect of this paradigm because it gives insight into the relative features of a complex stimulus. The results from the mixtures indicate that rats will distribute their behavior according to how prevalent a taste component is within a mixture. There was also a major caveat of the paradigm that was highlighted when water was used as a test compound. Together with the findings in Chapter 4 which examined the extent that water can be discriminated from characteristic stimuli of the putative four basic tastes, we now know that the number of sample licks is likely a critical factor in the ability of the rats to make some discriminations in this task. Additionally, data from Chapter 4 support the view that this paradigm can be used to obtain behavioral profiles which may describe the qualitative features of novel taste compounds. Moreover, rats do not need to be trained explicitly with these novel compounds, the training received using the prototypical stimuli appears to generalize to new taste compounds. Presenting the data from all groups reveals the degree to which the four basic taste qualities are generated by a given test stimulus. The extent to which this holds true should (and could) be examined by varying the relative concentrations of different pairings so that mixtures for all possible combinations at various concentrations are thoroughly explored. If one wanted the animals to respond completely on their standard response spout when the taste was present within a compound, it might be possible to train the rats to

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120 identify the standard in mixtures during training. The concentration in the mixture could be varied in much the same way that the training concentrations were varied to render intensity an irrelevant cue, which should result in better discriminatory control. Conceivably, the rats would be able to perform quite well with feedback encountered during training. If the groups of rats could learn to identify, at high levels of performance, the presence of the standard in many different combinations of the four prototypical taste compounds, then it would increase the confidence that responses on the standard response spout after presentation of unknown test compounds indicate detection of a standard-like taste. This would be an alternative strategy for using behavioral profiles to describe a compounds qualitative features. Unfortunately, the current design of the gustometer delivery system prevents such a strategy due to a limited number of fluid reservoirs, but if such technical limitations could be overcome it would be useful in the future to explore the use of complex mixtures as standards. It is interesting that in the experiment where water was used as a test stimulus, a quinine-like profile was obtained. Additionally, it is remarkable that the rats in the Q group of the experiment in Chapter 3 were unable to learn to discriminate quinine from water. Studies measuring absolute detection threshold for quinine are possible, and thresholds have even been obtained using the same equipment, albeit with a methodologically different task. Additionally, Experiment I in Chapter 3 (brief-access test) showed that rats will alter their licking behavior in a concentration dependent manner to quinine, though in that experiment the rats could initiate as many licks in a 5-s period as possible (which could result in as many as 35 licks) compared to the 5 licks they were allowed during sampling in Experiment II. In fact, the average (+/SE) licks

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121 to the concentration closest to 0.027 mM quinine (0.03 mM) presented during brief-access testing was 27.93 (+/-1.18). The highest concentration used as a training stimulus in the generalization experiment (0.827 mM) falls between two of the concentrations used in the brief-access test, 1 mM and 0.3 mM. At the higher concentration, 1 mM quinine, rats in the brief access test licked an average of 9.5 (+/1.2) and at the next lowest concentration, 0.3 mM quinine, rats licked an average of 21.7 (+/1.1). Therefore, it is plausible that the number of sample licks associated with the testing parameters were too low in the original design of this task. Clearly they were sufficient to maintain high levels of performance with the other compounds, but apparently water and/or quinine are different from the other three compounds. The experiment in Chapter 4 helped to clarify the ability of rats to discriminate quinine and water under these conditions, but interpretation of those results are complicated by the different training histories encountered by animals between these two experiments. Surprisingly, the nave rats in the W and Q groups from Chapter 4 had difficulty discriminating 0.360 mM quinine and water also. After 22 days of discrimination training, the average performance was near the mid-to-low 60% range. This is in stark contrast with the rate at which the N, S, and C groups learned to discriminate their training compounds. It was decided at that point in the experiment to remove water from the comparison stimuli for the N, S, Q, and C groups. The W group received more discrimination training with the water and 0.827 mM quinine so that eventually the group might be useful for assessing whether a test compound would generate a water-like profile. It was clear, however, that when all of the training concentrations were added to the training array, there was evidence of loss of stimulus control. It is possible that with

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122 more explicit training using all of the training compounds, high levels of performance should be achievable. The explanation for the impaired performance is elusive, unless one considers that water may have a quinine-like taste in rats (Bartoshuk, 1977, Morrison, 1967). Future Validation of the Procedure For this paradigm to be useful to researchers, it should be further validated with respect to understanding the limitations of the information provided by the profiles. For example, it would be instructive to examine how the rats would respond if they were given a concentration completely unrelated to the range of the training compounds encountered. This could most easily be accomplished by using very high concentrations of the standards and comparisons. Not only would this provide information about the ability for the training to generalize to concentrations completely outside the range of training compounds, but it might also provide information about the constancy of a quality at high concentrations. Another important issue would be to understand what would happen if a compound from a new distinct taste quality was encountered during testing. One way to approach that issue would be to train three groups of rats to discriminate only three of the prototypical compounds and use the fourth as a test compound. If the generalization profile obtained did not resemble any of the standard stimuli (or it resembled all of them equally, i.e., Generalization Score = 0.5), then it would provide evidence that rats were capable of indicating when a stimulus was unlike any of the familiar training stimuli. All possible combinations of comparisons should be tested to identify which, if any, qualities might generalize most to others.

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123 It would not be surprising if the rats generalized one of the prototypical compounds to another. Morrison (1967) showed this in his study, for example, when he used quinine as a test compound so that the rats in the HCl and sucrose groups had to distinguish whether it was more NaCl-like or more like their comparison stimulus (HCl or sucrose, respectively). He showed that when rats were forced to choose between HCl and NaCl to behaviorally describe quinine taste, that the responses were more HCl-like than NaCl-like. This has been the basis for some to interpret that rats have difficulty discriminating between quinine and HCl (Lemon & Smith, 2005). It would be interesting to know what the generalization profile might reveal using this paradigm if we trained rats in the citric acid group to discriminate citric acid from sucrose and NaCl (in this example, the only two comparison stimuli) and then presented quinine as a test stimulus. It might look like the profile obtained using Morrisons (1967) procedure, or this paradigm might offer more flexibility for responding. Either way, it is an interpretably important piece of information to consider. Another recommended validation procedure would be to understand what happens to responding when the animal is made to no longer experience a specific taste quality. For example, if specific gustatory nerve transections were performed, and they resulted in the complete inability to detect one (or more) training compound(s), would responding to the other stimuli then be normal? What would the profile for that taste compound look like? Perhaps such an outcome would result in loss of stimulus control, especially if the quality is the standard. This is an interesting and important interpretive issue that is revisited below.

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124 It would also be important to extend the test stimulus array to include many examples of compounds, especially those with complex tastes. The limited number of test compounds incorporated into these studies may bias the conclusions drawn about the utility of the profiles. Each of the test stimuli were carefully chosen because of a certain expectation about what the outcome should look like as we anticipated that it would probably be most informative to first include compounds that have been tested using other behavioral methodology (e.g., the conditioned taste aversion approach) to provide an external validation. The only unanticipated results came from the maltose profile where we found evidence of a more dominant quinine-like component than a sucrose-like component for the putative sweetener. This was surprising because rats are known to prefer maltose, so one would not imagine that it would contain a dominant quinine-like quality. If the results from this paradigm and others are not in agreement, however, it would not necessarily suggest that this paradigm (or another) is flawed, but it certainly would warrant further investigation. This list of suggested means to further validate the procedure is likely not exhaustive, but it is meant to indicate that the interpretation of profiles should be done with these caveats in mind. Addressing each of the issues would only serve to strengthen any conclusions about the taste quality of a test compound determined using this procedure. Potential Uses of the New Generalization Procedure Neurobiological applications The behavioral testing paradigm presented in Chapters 3 and 4 has the potential to provide great insight into the study of the peripheral gustatory system. For example, it would be possible to employ gustatory nerve transections in order to understand the

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125 necessity and sufficiency of specific nerves to maintain gustatory function. Such studies might reveal that a particular nerve (or combination of nerves) is necessary for the transduction of specific taste qualities. Alternatively, it might be the case that a single nerve is important for all quality discrimination, indicating that the signal for taste quality is channeled through a specific pathway. Currently, such studies have not been attempted, likely because the conditioned taste aversion approach is not compatible with such a design, or at least not one that would yield such straightforward results. The peripheral gustatory system. The known presence of narrowly tuned N-fibers, in the chorda tympani (CT) nerve, which respond to sodium salts (and LiCl), suggested that the anterior tongue taste receptor cells (which are innervated by the CT) were critical in NaCl sensibility (Frank, Contreras, & Hettinger, 1983). Researchers were surprised, however, when transection of the CT did not affect NaCl intake or preference in an overnight test as compared with intact rats (Akaike, Hiji, & Yamada, 1965; Pfaffmann, 1952; Richter, 1939; Vance, 1967). It was not until later, when the application of more detailed and rigorous behavioral testing was used, that severe consequences of CT transection on salt taste perception were revealed (Slotnick, Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990). Transection of the CT increases the detection threshold for NaCl by at least 1 orders of magnitude (Kopka & Spector, 2001; Slotnick, Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990), resulting in decreased sensitivity to Na+ salts. Chorda tympani nerve transection also attenuates salt discrimination performance (Kopka, Geran, & Spector, 2000; Spector & Grill, 1992; St. John, Markison, & Spector, 1995). These results suggest that the CT is

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126 necessary to maintain normal sodium detectability and recognition even though its transection denervates only 15% of taste buds in the oral cavity. Conversely, when the glossopharyngeal (GL) nerve is severed, thereby removing input from roughly 60% of the total taste bud complement (Miller, 1977), salt discrimination and sodium recognition remain normal (Markison, St. John, & Spector, 1995; Spector, Schwartz, & Grill, 1990). Thus, it appears that the GL, which innervates taste buds on the posterior 1/3 of the tongue, is not necessary for the maintenance of these particular functions (St. John, & Spector, 1998). Given what is known about the importance of the CT in taste discrimination behavior, it would be enlightening to design an experiment in which half of the animals in each group receive bilateral transection of the CT nerve and their subsequent ability to discriminate between the training compounds is assessed. If, they were still able to perform at high levels, then it might be equally exciting to see whether the generalization profiles for test compounds (either novel and/or those experienced prior to surgery) compare to the other half of each group, which would receive sham surgery. Likewise, it would be interesting to perform the same experiment with GL transection as the surgical manipulation because although the GL has not been shown to be highly involved with any behavioral tasks assessing the discrimination of compounds, it is responsive to all four prototypical stimuli. The GL is highly responsive to quinine, responds well to acids, and also has a somewhat weaker response to salts and sugars (Oakley, 1967; Boudreau, et al., 1987; Frank, 1991; Dahl et al., 1997). Therefore, it is possible that the GL would be involved in carrying information specifically about

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127 quinine-like taste qualities, or possibly even all four putative basic tastes. It remains a conceptually interesting question that can now be addressed. As pointed out earlier, it may first be important to understand what happens to intact rats if one of their training stimuli suddenly disappears. The loss of a specific taste quality could be mimicked through providing sham licks. If the gustometer was programmed to proceed normally allowing a rat to lick the dry sample spout, but not deliver a taste sample contingent on the licks, and then otherwise treat the trial normally, it is conceivable that that condition might mimic loss of a specific taste quality. Unfortunately, it would not mimic the other sensory cues associated with sampling (e.g., somatosensory) and so is not the most ideal; although it would be a better alternative than presenting water, which has been associated with a quinine-like taste in this paradigm. Additionally, the current generalization paradigm would be well-suited for a within subject design that could assess function before, during, and after recovery from specific nerve transection. Following regeneration of the nerve after a surgical lesion, it would be interesting to know if function returns to the same levels seen prior to the insult. Such a procedure might not be possible given the likelihood of loss of stimulus control that would occur in those animals. This point could be addressed, however, by including other groups that do not receive testing during the period of time that regeneration occurs. Perhaps findings of this ilk would be useful to predicting recovery of function after human injury. Finally, another exciting avenue of study that is possible with this paradigm would be to use an inducible knockout preparation. The rationale for such a statement is that the knockout technology could be key in understanding whether specific taste receptors are

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128 necessary for a specific taste quality (or just specific compounds). For example, the experimenter could train the animal to discriminate the prototypical compounds as used here, and then they could knockout function of a specific receptor and observe the level of discrimination behavior. Next, the experimenter could restore the function of the receptor and note the effects on performance. Likely, the animal would perform poorly without the proper signal transduction machinery, but would be able to perform the task once the receptor was restored. It might be more interesting, however, to find out that the animal can compensate for non-functional receptors, suggesting redundancy in the system. Obviously the success of the proposed study depends on a lot of technical factors working properly, but theoretically, the behavioral testing paradigm opens the doors to a lot of currently unachievable inquiries. Behavioral data support analytic processing rather than synthetic Results from the novel taste quality generalization experiments suggest that taste quality signals may undergo analytic, rather than synthetic, processing in the gustatory system. The fact that rats could learn to discriminate between the four prototypical taste compounds, representing the four putative basic taste qualities, and then respond to novel test compounds in terms of how NaCl-like, sucrose-like, quinine-like, or citric acid-like they were provides support for this claim. The profiles generated from sodium gluconate and quinine looked like the profiles generated from the novel concentration of NaCl and quinine, respectively, used in Chapter 3. Although maltose and fructose did not generate a strictly sucrose-like profile, it was still consistent with an analytic viewpoint because there was also a quinine-like component to the response profiles. Even KCl, which has been argued as a complex taste which is distinct from NaCl gave rise to a profile that consisted of a combination of the 4 prototypical stimuli. Moreover, when MSG was

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129 presented, the profile generated appeared to be a combination of two of the training compounds, NaCl and sucrose, which is also consistent with the assertion that taste quality is analytic. This is especially remarkable given that some researchers argue that the taste of MSG is representative of a distinct fifth taste quality referred to as umami. If all of the novel test compounds had generated profiles indicative of a separate taste quality then it would have refuted the analytic claim. For a test compound to have generated a profile suggestive of a separate taste quality, either all of the groups Generalization Scores would have been 0.5 or they would all have been 0. Therefore, if the rats did not recognize distinct components comprising the novel test compounds but instead responded as if the test compounds were novel qualities, then it would have suggested synthetic processing (that continua of taste qualities exist rather than a few discrete qualities) (see Erickson, 1968). Perspectives The present collection of studies introduced and utilized two novel behavioral paradigms to study aspects of taste processing in rats. Each paradigm has unique strengths that attempt to circumvent shortcomings associated with the commonly used conditioned taste aversion technique. This dissertation provides significant groundwork towards the characterization of these new behavioral paradigms for assessing taste quality in rodents, and indicates future lines of investigation necessary to fully elucidate the strengths and limitations of these paradigms. The two approaches will likely prove useful in future investigations of taste quality coding in rodents, especially with respect to answering questions about whether taste coding is governed by analytic or synthetic processing. Thus far, results support analytic processing, but further testing is recommended.

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139 BIOGRAPHICAL SKETCH Connie Lynn Colbert was born in Miami, Florida, on May 5, 1977, to Robert and Debora Colbert. She has a twin brother and had an older brother (deceased), both of whom fostered a healthy co mpetitive spirit regarding sc hool. Connie always enjoyed school and knew at a very young age that she would enjoy a career in the sciences. She graduated high school in 1995 with an AA de gree from Miami Dade Community College and then attended Florida International Univers ity in Miami and receiv ed her Bachelor of Science in psychology in 1998. She spent the next year continuing her research at F.I.U., and in 1999, Connie started gr aduate school in psychobiology at the University of Florida, and received her M.S. degree in A ugust 2002. She also met her husband, Justin L. Grobe, while obtaining her Ph.D. After obtaining her Ph.D., Connie will move to Iowa for postdoctoral training with her husband.


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NEW BEHAVIORAL PARADIGMS TO STUDY TASTE-QUALITY
GENERALIZATION AND DISCRIMINATION IN RATS













By

CONNIE LYNN GROBE


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

Connie L. Grobe
































This dissertation is dedicated to my brother, Robert.















ACKNOWLEDGMENTS

I thank my family, and friends. Their constant support has made it possible to

achieve my goals. At the University of Florida, I have been lucky to meet and interact

with some very talented people, who have each helped to shape my character along the

way. I especially recognize (in chronological order) Laura Tucker, Laura Geran, Cheryl

Vaughan, Shachar Amdur, Mary Clinton, Ginger Blonde, Shawn Dotson, Kathryn

Saulsgiver, Anaya Mitra, and Yada Treesukosol. They have provided me with

encouragement, assistance, advice, and countless other acts of kindness that I will never

fully understand, but always deeply appreciate.

I thank the entire faculty in the Behavioral Neuroscience area for contributing to

my education. I also gratefully acknowledge the help and guidance that I received from

Dr. Neil Rowland, my M.S. advisor and Dr. Alan Spector, my dissertation advisor. They

have each provided me with a perspective on science that I will continue to value and can

only hope to incorporate into my own future scientific approach.

Finally, I cannot thank my husband, Justin Grobe, enough for his unwavering love,

patience, and encouragement, but especially for his exemplary scholarship.
















TABLE OF CONTENTS



A C K N O W L E D G M E N T S ......... .................................................................................... iv

LIST OF TABLES ......... ........................................... .......... .. ix

LIST OF FIGURES ......... ......................... ...... ........ ............ xi

A B S T R A C T .......................................... .................................................. x iii

CHAPTER

1 L ITER A TU R E R E V IE W ............................................................... .. .....................1

In tro d u ctio n ............................................ ............................... 1
Dom ains of Taste ................................................................. ...............
Sensory D iscrim native D om ain........................................ ........................ 4
A effective D om ain ...................................... ............................. .4
Physiological Dom ain .......................... ...... .... ................5. 5
Taste Quality...................................... ..................... ...............
Animal Models Used to Study Taste Quality ............... .....................................6
D iscrim nation T asks ............... .................... .................... ... ............ ... .6
G eneralization Tasks ................................. .. .. ...... ............ 10
Ideal P sychophysical T ask .................... ......................... ............... .. 13
Importance of Psychophysical Analysis in Animal Models.................. ............ 13
Argument for the Development of Psychophysical Tasks .....................................14

2 RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO
SAMPLE TASK USING ONLY TASTE STIMULI.............................................16

Background .......................................................16
M e th o d ...................... .. .............. .. ....................................................... 1 7
A n im a ls .......................................................................................................... 1 7
Apparatus ....................... ....................... 17
S tim u li ........................................................................................................... 1 8
S u rg e ry ..............................................................1 9
Training and Testing Phases ......................................................................20
S p o u t train in g ......................................................................................... 2 0
S id e tra in in g ........................................................................................... 2 1
A lternation............................................. 21


v









D iscrim nation training I-II ........................................ ...................... 21
Trial structure (final param eters)................................ ....... ............... 22
Testing ................. .......... ...... .......... ......................... 23
Adjustments to Testing Parameters ................................................................23
Statistical A n aly ses........... ...... .................................... .............. .. ... .... .... .. 24
Results ............................. ................................. 24
Overall Perform ance........... ... .... .... ...... ... ...... ..... .... .... ...... .. 24
Perform ance on Sam e Trials ........................................ .......... ............... 25
Perform ance on D different Trials....................................... ............... ... 25
Performance on Same Trials versus Different Trials .......................................25
Discussion ............. ........... ....... ..... ....... ......................... 26

3 A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION
IN RATS ...................................... ................. ................. .......... 35

Introduction ........................................................................................................ 35
E x p e rim e n t I ............................................................................................................... 3 8
M e th o d ..................................................................................................... 3 8
S u b je cts ................................................................3 8
T rain in g S tim u li ..................................................................................... 3 8
P ro c ed u re ............................................................................... 3 8
Data Analysis........................................ 40
R e su lts ........................................................................................................... 4 1
D isc u ssio n .....................................................................................4 2
E x p erim en t II ......................................................................................4 3
M e th o d ........................................................................................................... 4 4
S u b je cts ................................................................4 4
A p p a ratu s ............................................................................................... 4 4
T ask ov erview ...........................................................44
S tim u li ................................................................4 5
G ro u p s ................................................................4 5
T ria l stru c tu re ......................................................................................... 4 5
T ra in in g ................................................................4 6
Test compounds.............................................48
Retraining water as a comparison stimulus ................................................49
N negative control test ......... ................ ............................49
D ata analysis ................................ ............................ 49
G eneralization score ......... ......... .. ....... .............................50
Results ............................ .... ..........................51
N ovel concentrations: N aC l ....................................................... 52
Novel concentrations: Sucrose .................. .............................. 53
Novel concentrations: Quinine ...... ........................................... 53
Novel concentrations: Citric acid ......................... .. ............ ........... 54
Mixtures between NaCl and sucrose: 1.07 M NaCl + 0.421 M sucrose......55
Mixtures between NaCl and Sucrose: 1.07 M NaCl + 0.077 M Sucrose.....55
Mixtures between NaCl and Sucrose: 0.376 M NaCl + 0.421 M Sucrose...56
N ovel test compound: W ater.......................... ... ......................... 56









Retraining water as a comparison stimulus.................... .... ............... 57
N negative control session.......................................... ........................... 58
Discussion ............. ..... ......... ... ...............58

4 APPLICATION OF A NEW BEHAVIORAL PARADIGM TO ASSESS TASTE
QUALITY GENERALIZATION ....................................... ...................................76

Introdu action .............. ................. .................................................................. 76
Method ............... ....................................................76
Subjects ............... ......... ....... ................76
A p p aratu s ................................7 7............................
T ask O overview ............... .................................................................................77
S tim u li ............................................................................................................ 7 7
Trial Structure..................... ..................78
T ra in in g ............................................................................................................... 7 8
S p o u t train in g .......................................................................................... 7 9
S id e tra in in g ............................................................................................ 7 9
Alternation.......................................... 79
Discrimination training I-III ..................................... .......... 80
Test Compounds ......... ............. .............. ............... 81
D ata A n a ly sis .................................................................................................. 8 1
R e su lts ................................ ........ ........................................................................... 8 2
Test Stimulus: Sodium Gluconate .....................................82
0.376 M sodium gluconate ............. ........ .............82
0.668 M sodium gluconate ............... .................... .......... 83
Test Stim ulus: D enatonium ....................................................... 83
0.13 1 m M denatonium ....................................................... 83
0.360 m M denatonium ....................................................... 84
T est Stim u lu s: M alto se ................................................................................... 84
0.077 M maltose ............... ......... ..............84
0.148 M maltose ................................. ................ ...............85
Test Stimulus: Potassium Chloride (KC1) ........................................86
0 .3 7 6 M K C 1 ............................................................................. 8 6
0.668 M K C 1 ................................................................................ 86
Test Stimulus: Monosodium Glutamate .................. ...............87
0 .0 7 7 M M S G .......................................................................................... 8 7
0 .14 8 M M S G .......................................................................................... 8 7
T est Stim u lu s: F ru cto se .................................................................................. 88
0.077 M fructose ................................................. ............... 88
0.148 M fru ctose ................................................. ............... 89
Performance of Water Group ................................... ....... ...............89
D discussion ..................91................................................
S o d iu m G lu co n ate .......................................................................................... 9 1
D e n ato n iu m .................................................................................................... 9 2
M a lto se ..............................................................9 3
Potassium Chloride.................................................. 94
M onosodium G lutam ate .............................................. ............... 95









F ru c to se ............................................................................................................... 9 7

5 GENERAL D ISCU SSION .............. .......... .............. ............... ............... 115

Introduction ....................... ............ ......... 115
Delayed M atch/Non-M atch to Sample........... .................................... ...............115
Novel Taste Quality Generalization ...... ................. .................118
Future Validation of the Procedure ............................................122
Potential Uses of the New Generalization Procedure.................... ........ 124
Neurobiological applications................... ... ............................ 124
Behavioral data support analytic processing rather than synthetic ...........128
P e rsp e c tiv e s ..............................................................................................................12 9

L IST O F R E FE R E N C E S ....................................................................... .................... 130

BIOGRAPHICAL SKETCH ............................................................. ............... 139






































viii
















LIST OF TABLES


Table page

3-1. Training compounds selected from Experiment I. .................................................62

3-2 E xperim ental group s......................................................................... ...................62

3-3. Results from one-sample t-tests for a novel concentration of NaC ......................62

3-4. Performance to training stimuli during novel NaCl testing...................................62

3-5. Results from one-sample t-tests for a novel concentration of sucrose ....................63

3-6. Performance to training stimuli during novel sucrose testing ...............................63

3-7. Results from one-sample t-tests for a novel concentration of quinine .....................63

3-8. Performance to training stimuli during novel quinine testing .............................64

3-9. Results from one-sample t-tests for a novel concentration of citric acid .............. 64

3-10. Performance to training stimuli during novel citric acid testing ...........................64

3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose ....................65

3-12. Performance to training stimuli during high NaCl + high sucrose testing .............65

3-13. Results from one-sample t-tests for 1.07 M NaCl + 0.077 M sucrose ....................65

3-14. Performance to training stimuli during high NaCl + low sucrose testing .............66

3-15. Results from one-sample t-tests for 0.376 M NaCl + 0.421 M sucrose ..................66

3-16. Performance to training stimuli during low NaCl + high sucrose testing ...............67

3-17. Results from separate one-sample t-tests for water ...........................................67

3-18. Performance to training stimuli during water testing ....................................67

4-1. Overview of experim ental groups ........................................ ......................... 99

4-2. Training schedule for N, S, Q, and C groups .................................... ............... 99









4-3. Training parameters for W group. ............................................100

4-4. Results from one-sample t-tests for 0.376 M NaGluconate ..............................101

4-5. Results from one-sample t-tests for 0.668 M NaGluconate ..............................101

4-6. Performance to training stimuli during sodium gluconate testing .........................101

4-7. Results from one-sample t-tests for 0.131 mM denatonium ..............................102

4-8. Results from one-sample t-tests for 0.360 mM denatonium ..............................102

4-9. Performance to training stimuli during denatonium testing .................................. 102

4-10. Results from one-sample t-tests for 0.077 M maltose ................ ................103

4-11. Results from one-sample t-tests for 0.148 M maltose ................ ................ 103

4-12. Performance to training stimuli during maltose testing ...................................103

4-13. Table oft-test statistics for 0.376 M K C1 ................................... .................104

4-14. Table oft-test statistics for 0.668 M K C1 ........................................ ............... 104

4-15. Performance to training stimuli during KC1 testing ...........................................104

4-16. Table oft-test statistics for 0.077 M MSG .................................................................105

4-17. Table oft-test statistics for 0.148 M MSG ........................... 105

4-18. Performance to training stimuli during MSG testing .........................................105

4-19. Table of t-test statistics for 0.077 M fructose ........... .................1.06

4-20. Table of t-test statistics for 0.148 M fructose ........... .................1.06

4-21. Performance to training stimuli during fructose testing ............ ... ..................106

4-22. Performance to training stimuli for W group during dt3-5 through dt3-8.............07
















LIST OF FIGURES


Figure page

2-1. Trial structure for DMTS/DNMTS (same/different) task. ......................................30

2-2. The mean overall performance to all trial types is shown............... ...................31

2-3. M ean performance to same trials ................................................... .............32

2-4. M ean overall performance to different trials....................................................... 33

2-5. Mean performance on same versus different trials..............................................34

3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test ...........................68

3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test........................68

3-3. Mean (n=8) unconditioned licking to quinine in a brief access test........................69

3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test .................69

3-5. An overview of the trial structure................................................................. 70

3-6. The generalization profile obtained when 0.847 M NaCl was used as a test
com pound ............. ..... .. ......... ............. ............................7 1

3-7. The generalization profile obtained when 0.068 M sucrose was used as a test
com pound ............. ..... .. ......... ............. ............................7 1

3-8. The generalization profile obtained when 0.546 mM quinine was used as a test
com pound ............................................................ .... ..... ......... 72

3-9. The generalization profile obtained when 42.56 mM citric acid was used as a test
com pound ............................................................ .... ..... ......... 72

3-10. The generalization profile obtained when 1.07 M NaCl + 0.421 M sucrose was
used as a test stim ulus ........................................ ................. ..... .... 73

3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose was
used as a test stim ulus. ....................................... ................. ..... .... 73









3-12. The generalization profile obtained when 0.376 M NaCl + 0.421 M sucrose was
used as a test stim ulus ...................................... ................... ..... .... 74

3-13. The generalization profile obtained when water was used as a test stimulus..........74

4-1. Profile for 0.376 M N aGluconate............................................................ .......... 108

4-2. Profile for 0.668 M NaGluconate................................ ......................... ........ 108

4-3. Profile for 0.131 mM denatonium ...................................................... ............... 109

4-4. Profile for 0.360 mM denatonium ................................ ......................... ....... 109

4-5. Profile for 0.077 M m altose .................................................... .... ...........110

4-6. Profile for 0.148 M m altose .................................................... .... ...........110

4-7. Profile for 0.376 M K C1 ...................................................... ............... ............... 111

4-8. Profile for 0.668 M K C1 ...................................................... ............... ............... 111

4-9. P rofile for 0.077 M M SG .......................................................................... .... 112

4-10. Profile for 0.148 M M SG ........................................................................ 112

4-11. Profile for 0.077 M fructose ................................................... ..................113

4-12. Profile for 0.148 M fructose ............................ ............... ............... 13

4-13. Summary of performance for W group during training with water and quinine... 114

4-14. Diagram outlining two possibilities for the level (peripheral or central) at which
convergence of taste signal processing leading to the same behavioral output
m eight occur ............................. ........................................... 114















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

NEW BEHAVIORAL PARADIGMS TO STUDY TASTE-QUALITY
GENERALIZATION AND DISCRIMINATION IN RATS

By

Connie Lynn Grobe

August 2006

Chair: Alan C. Spector
Major Department: Psychology

Questions regarding the nature of perceivable taste qualities remain: Is taste quality

perception analytic or synthetic? Specifically, are tastes comprised of mixtures of a

discrete number of basic qualities? Currently, there are no appropriate animal models

that allow repeated assessments of the qualitative features of taste stimuli. Because it is

not possible to directly measure taste perception in animals, such sensory experiences

must be inferred on the basis of results from specially designed behavioral tasks.

Here, an operant-conditioning based behavioral paradigm was used to train rats to

taste two samples within a trial and then to make one response if presentations of the taste

stimuli (NaCl or sucrose) matched and another response if they did not match. Rats

performed similarly on matching and non-matching trials. Overall performance reached

an asymptote at -74%. This approach could provide a means of testing discrimination

and generalization as well as exploring the temporal capacities of short term memory in

the taste system.









Another study used operant techniques to train four groups of rats to distinguish the

taste quality of a single (standard) compound representing one of the putative four basic

tastes ("salty," "sweet," "sour," "bitter") from compounds representing the three other

taste qualities (comparisons). Prototypical stimuli were used to represent basic tastes

(NaC1, sucrose, citric acid, quinine). This task was then used to quantify how animals in

each group generalized their responses when presented with novel taste stimuli, providing

a way to assess how NaCl-like, sucrose-like, citric acid-like, and quinine-like the quality

of the solution was. Stimulus control of training compounds was maintained at high

levels, and behavioral responses to test stimuli generalized in predictable ways, providing

a non-invasive method for repeatedly assessing taste quality in the same animals.

Interestingly, the profile of monosodium glutamate is both NaCl-like and sucrose-like.

Overall, results suggest that taste processing is analytic.

Additionally, these paradigms could provide a functional context to interpret the

outcomes of anatomical, pharmacological, and genetic manipulations of the gustatory

system. They are also compatible with existing techniques that are crucial for linking

neural activity with behavior, which is essential for understanding gustatory processing.














CHAPTER 1
LITERATURE REVIEW

Introduction

Many questions remain concerning the organization of the gustatory system and the

neural mechanisms underlying taste function. How are tastes detected in the mouth and

appropriate signals sent to the brain? Specifically, how are the relevant features of a

chemical stimulus coded by the nervous system? What portions of the gustatory pathway

are necessary for the maintenance of particular functions, like taste intensity

discrimination or taste quality detection and/or discrimination?

Before one can approach these questions, it is important to resolve fundamental

concepts concerning the perceptual characteristics of taste stimuli in the animal models

chosen to study issues pertaining to taste. For example, it is not fully known whether

rats, a commonly used animal model, perceive taste stimuli as categorical or falling along

a continuum of possible qualities. These two possibilities represent theoretically

opposing viewpoints of how gustatory processing occurs: The analytic view and the

synthetic view, respectively. Erickson (1968) stated that color vision is a synthetic

system whereas audition is an analytic system. The difference being that the synthetic

system appears to involve the same set of neurons and the analytic system appears to

involve different sets (Erickson, 1968). Erickson (1968) further pointed out that this key

difference might be at the heart of the debate about whether signals regarding taste

quality are processed through devoted labeled-lines or in an across-fiber pattern.









Therefore, the purpose of the current experiments was for the development and

application of psychophysical tasks that may yield an answer to the question of whether

there might be a few taste primaries or an indefinite number of them. At issue is whether

a few discrete categories of taste quality are sufficient to encompass all taste experiences

in our animal model (Sprague-Dawley rat) or whether there is a continuum of possible

taste perceptions.

The aim of the first experiment was to design a versatile task that would provide

insight into the ability of rats to discriminate differences between 2 stimuli, whether of

the same compound (intensity discrimination) or between different compounds (quality

discrimination). A second goal of the first experiment was to determine if the same

protocol could be used to investigate the temporal properties of short-term memory for

taste solutions.

The overall goal of the second and third experiments was to examine whether rats

can reliably discriminate taste compounds thought to fall into different qualitative

perceptual classes, and whether they will reliably categorize novel stimuli as possessing

characteristics similar to the training stimuli. The existence of such a paradigm would

offer researchers the opportunity to observe the effects that manipulations made to the

gustatory system have on performance in a behavioral task that was specifically aimed at

measuring taste quality identification. In addition, the task could be used to gain insight

into the gustatory perceptual experience of the animal generated by novel taste

compounds.

In order to conduct these experiments, it was assumed that rats treat taste stimuli as

being composed of (at minimum) the same 4 basic qualitative classes that humans report









perceptually: "salty," "sweet," "sour," and "bitter." Nowlis, Frank, and Pfaffman (1980)

found evidence supporting this assumption using a behavioral approach. Moreover, work

examining the peripheral transduction mechanisms in rodents to prototypical compounds,

those identified by humans as representing the four basic tastes, suggest that animals may

have receptors devoted to the four taste qualities (Chandrashekar et al., 2000; Gilbertson

& Boughter, 2003; Scott & Giza, 1990; Zhang etal., 2003; Zhao etal., 2003). A

controversial fifth taste quality, referred to as "umami" (Yamaguchi, 1991), has been

identified in the literature and is described as the taste quality associated with a savory or

delicious sensation in humans. Support for the existence of "umami" taste in rodents is

mixed; some sources indicate that the taste of monosodium glutamate (MSG) (the

prototypical compound for the "umami" taste) generalizes to sucrose and NaC1, "sweet"

and "salty," respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data

suggest that rats can nonetheless discriminate MSG from sucrose even when the

contribution of the sodium ion is reduced (Heyer, Taylor-Burds, Mitzelfelt & Delay,

2004). The strategy of using representative compounds from the classic 4 basic tastes

does not detract from the possibility that there could be more taste qualities; in fact, it

could even provide support for such a notion.

How does one measure taste function in non-human animals? To appreciate this, it

is important to understand each of the identified taste domains and how they may be

measured in animals (including humans).

Domains of Taste

The functional aspects of taste can be classified into at least three broad domains:

sensory-discriminative, affective, and physiological (see Spector, 2003b, for review).

The discriminative domain deals with identification of a stimulus, and the affective









domain refers to the hedonic aspects of a compound, whereas the physiological domain

consists of the physiological reflexes that a stimulus elicits. Each of the three domains

describes a different facet of taste function, and possibly represents different aspects

related to ingestive behavior.

Sensory Discriminative Domain

Briefly, sensory-discriminative function, the identification of a stimulus, can be

dissociated from the affective/hedonic domain by use of several different operant and

classical conditioning procedures aimed at measuring both detection thresholds and

quality discrimination in animals (Spector, 2003b). These procedures do not rely on the

hedonic aspects of the taste solution to drive responses because the taste serves as a

signal for other reinforcing or punishing events. Consequently, the inherent motivational

properties of the stimulus are irrelevant in the animal's identification of the stimulus.

Affective Domain

Briefly, the affective domain refers to the hedonic attributes of taste stimuli (i.e.,

the palatability of a compound). The most commonly used methods to describe the

affective responses of animals regarding taste compounds include operant tasks aimed at

assessing appetitive/avoidance behavior and those aimed at measuring consummatory

responses, which are the reflex-like behavior stimulated by a tastant contacting its

sensory receptors (Spector, 2003a). The two-bottle intake test, a variation of it termed

the brief-access task, and various operant response measurements (e.g., progressive ratio

breakpoints, rates of responding) have been used to quantify the reinforcement efficacy

of a taste stimulus (Clark & Bernstein, 2006; Guttman, 1953; Hodos, 1961; Reilly, 1999;

Sclafani, 2006; Sclafani & Ackroff, 2003; Starr & Rowland, 2006). Consummatory

responses, on the other hand, have been measured through use of the taste reactivity









paradigm (Grill & Berridge, 1985; Grill et al., 1987), which is a procedure that involves

the quantification of oromotor reflexes elicited by taste stimuli infused directly into the

oral cavity.

Physiological Domain

The physiological domain, often referred to as cephalic phase responses (e.g.,

Berthoud, et al., 1981; Grill, Berridge, & Ganster, 1984; Mattes, 1997; Pavlov, 1902;

Powley, 1977; Spector 2000), consists mainly of salivation and other predigestive

responses that are elicited by taste stimuli. The increased salivation to food/fluids and

other physiological reflexes related to contact with taste receptors are proposed to be

adaptive as they likely contribute both to digestion/assimilation of food and protection of

the oral epithelium (e.g., salivation) (Spector, 2000).

Taste Quality

The quality of a taste falls under the rubric of sensory-discriminative function.

According to Bartoshuk (1978), Aristotle was first to suggest that the taste of all foods

and fluids was a combination of only a few discrete perceptual qualities. He suggested

that there were 7 basic tastes: sweet, bitter, sour, salty, astringent, pungent, and harsh

(Bartoshuk, 1978). It was not until 1927, however, that Hans Henning formally asserted

that the four basic tastes (salty, sweet, sour, and bitter) can be conceived as representing

the covers of a tetrahedron with combinations of two qualities along the edges, and

combinations of three on the face (Bartoshuk, 1978). This idea has been commonly

accepted despite occasional evidence suggesting additional qualities exist; the most

notable is the claim of a fifth quality, the umami taste which is said to arise from

glutamate salts and is described as "savory" by humans (Galindo-Cuspinera, & Breslin,

2006; Schiffman, 2000; Yamaguchi, 1991). Support for the existence of"umami" taste









in rodents, however, is mixed with some sources indicating that the taste of MSG (the

prototypical compound for the "umami" taste) generalizes to sucrose and NaC1, "sweet"

and "salty", respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data

suggest that rodents can nonetheless discriminate MSG from sucrose (Ninomiya &

Funakoshi, 1989a) even when the contribution of the sodium ion is reduced (Heyer,

Taylor-Burds, Mitzelfelt & Delay, 2004).

Animal Models Used to Study Taste Quality

Many researchers assume that the same basic taste qualities that are identified by

humans also extend to other animals. Support for this statement is based on the fact that

animals respond to prototypical compounds putatively representing the 4 basic tastes as

would be expected. That is, animals ingest and avoid taste solutions in a manner that

appears similar to human descriptions of pleasantness and aversion. Suppression of

intake of a solution, however, does not necessarily indicate qualitative similarity to other

avoided compounds in sensory-discriminative terms. In other words, when an animal

avoids two compounds equally, there is no way of knowing whether the animal also

perceives them as possessing the same taste quality. For example, an animal might avoid

drinking very concentrated NaCl to the same extent as it avoids drinking a quinine

solution, but data in animals and humans suggest that the two compounds are

qualitatively dissimilar. Accordingly, other methods are necessary for inferences on taste

quality in nonhuman animals to be established. Indeed, that is a primary theme of this

dissertation.

Discrimination Tasks

Operant discrimination procedures have been useful for determining whether two

compounds are perceptually distinct. If an animal cannot discriminate between two









different solutions, then it is plausible that both give rise to a perceptually identical

experience (Spector, 2003a). Alternatively, if an animal can reliably discriminate two

compounds from one another, then there must be some identifiable cue (e.g., differential

neural signals generated by the two stimuli) that can be used by the animal to guide its

behavior accordingly. It is critical in these experimental designs that intensity cues be

minimized so that discriminative responding comes under the explicit control of taste

quality. For example, it is known that a rat can discriminate a relatively lower

concentration of NaCl from a higher concentration of NaCl (Colbert, Garcea, & Spector,

2004), but this does not necessarily imply that the taste quality of the sensation between

strong and weak NaCl solutions is different. Therefore, when conducting studies of

quality discrimination, it is important to use a range of concentrations of the respective

training stimuli so as to render intensity a relatively irrelevant cue (Spector, 2003a;

Spector & Grill, 1992; Spector etal., 1996, 1997; St. John etal., 1995, 1997, 1998).

Spector (2003b) has identified important assumptions associated with this strategy:

the selected range of concentrations must have overlapping intensities and the relevant

taste quality of each compound delivered is assumed to remain constant across the

concentration range tested. If the first assumption were not met and two compounds

were of the same quality but all of the concentrations of one compound were perceived as

weaker than all of the concentrations of the other compound, then the animals would

likely be able to discriminate between the two compounds based on the differences in

intensity. One could identify the basis for such a discrimination, for example, if rats

performed well when lower concentrations of the "weak" compound were added to the

test stimulus array, but performed poorly to additional low concentrations of the "strong"









compound. Conversely, the opposite would be true. That is, if greater concentrations

were included in the discrimination task for both compounds, then as the 'weaker-tasting'

one became more salient, performance would decrease because the rats would incorrectly

respond as if it were the "stronger-tasting" compound. In contrast, performance would be

expected to improve for the "stronger-tasting" compound because a greater concentration

would only serve to distinguish it more from the "weaker-tasting" compound.

A typical stimulus discrimination paradigm involves training an animal to make

one response after tasting one compound and to make a different response after tasting a

different compound. As stated earlier, it is best when the concentration of each

compound is varied to render intensity an irrelevant cue, which should make taste quality

the salient signal. Typically, the animal is water deprived (< 24 h) to encourage

sampling, and correct responses are reinforced with brief access to water.

At least two studies have been published suggesting the occurrence of perceptual

identity as evidenced by rats being unable to discriminate between two different taste

stimuli. In one study, Spector and Kopka (2002) found that rats could not discriminate

quinine hydrochloride (a prototypical "bitter" compound) from denatonium benzoate (a

substance that rats also avoid consuming and that humans report as "bitter"). The same

rats were able, however, to discriminate quinine from KC1 (judged to be a complex

"bitter-sour-salt" by humans), and NaCl from KC1. Interestingly, the rats appeared to be

able to substitute denatonium for quinine after being trained to discriminate quinine from

KC1, signifying the two compounds were similar. These results support the claim that

quinine and denatonium likely generate a unitary qualitative percept in rats.









The other study which demonstrates perceptual identity between taste compounds

in rodent models is provided by Spector, Guagliardo, and St. John (1996). In that study,

amiloride, an epithelial sodium channel blocker, was used to remove the specific NaCl

taste cues necessary to discriminate NaCl and KC1. With application of 100 tM

amiloride, the remaining gustatory cues were not sufficient for rats to distinguish between

the two salts and they performed at chance levels in a discrimination task. Moreover, an

analysis of the errors in responding showed that mistakes primarily occurred on NaCl +

amiloride trials. This observation suggests that the rats responded as ifNaCl + amiloride

was perceptually similar to KC1. Adding support to the hypothesis that amiloride

changes the perceptual taste qualities of NaCl to be more similar to KC1 are data from

Hill, Formaker, & White (1990), showing that when NaC1, adulterated with amiloride,

was used as a conditioned stimulus (see below for definition) in a conditioned taste

aversion paradigm, rats generalized their aversion to non-sodium salts (specifically the

halogenated salts tested) including KC1.

Because there are many factors which might potentially serve as cues in a

discrimination task, results from studies using this approach are more compelling when

rats cannot discriminate between two compounds, provided that learning and intensity

effects can be rule-out. For example, the rise and decay time of the signal may differ

between two compounds that share a similar quality. Such temporal cues alone may be

sufficient to allow an animal to distinguish between the stimuli in a discrimination task.

Another possible signal, as mentioned earlier, may be the relative intensity of the tastants

selected. If the experimenter does not know the relevant concentration ranges to include









in the test stimulus panel and includes some that do not overlap in intensity, the animal

may be able to use those cues to guide performance.

Generalization Tasks

Guttman & Kalish, (1956) are credited with associating the concept of

discriminability with generalization gradients. A typical study in which a generalization

gradient is obtained consists of a scenario in which appropriate responses are reinforced,

when a specific training stimulus is present. Once stimulus-contingent responding is

established, a generalization test is presented during which no responses are reinforced.

The stimulus is varied on some physical dimension and the rate of responding is

recorded. These experiments generally produce response gradients that decrease as a

function of the difference between the training and test stimuli (Guttman & Kalish,

1956).

This concept has been adapted for use to study similarities between taste

compounds in the conditioned taste aversion (CTA) paradigm. Tapper & Halpern (1968)

innovatively applied the CTA procedure to make inferences on how animals classify taste

stimuli. They exposed experimental animals to radiation (2.5 min exposure of 80 r/min)

20 minutes before a scheduled session in which the rats normally consumed their daily

supply of water; after the radiation, however, a novel taste compound (the conditioned

stimulus; CS) was presented in place of water. This procedure resulted in a robust

avoidance to the CS, evidenced by the fact that after the pairing occurred, rats consumed

less of the CS upon subsequent re-exposure to the tastant. In the procedure, Tapper &

Halpern (1968) assumed: i) the [CS] becomes the quality standard against which the

animals compare other solutions; ii) the test solutions will be aversive, that is, associated

with the [CS], as long as their taste to the animal is qualitatively similar to the [CS], iii)









the magnitude of rejection indicates the degree of similarity in taste between the test

solution and the [CS]." By means of multiple cross-generalization pairings, they could

construct functions of aversion with which to compare compounds. Their rationale for

inferring that two compounds were of the same quality was based on the assumption that

similar generalization profiles would emerge for the respective stimuli (Tapper &

Halpern, 1968).

This approach was comprehensively extended later by Nowlis, Pfaffmann, and

Frank (1980). They conditioned aversions to a large number of compounds in hamsters

and rats and then measured intake to the four prototypical taste compounds, NaC1,

sucrose, HC1, and quinine, which served as test stimuli. As such, like Tapper and

Halpem (1968), it was assumed that the response profiles obtained related to the

qualitative properties of the prototypical taste compounds, thus allowing them to make

conclusions about the degree to which, a compound was sucrose-like, NaCl-like, HC1-

like, and quinine-like. With some exceptions, these data became the basis for many to

consider that rodents likely share the same perceptual taste experience as humans do.

Later, the same technique was applied to the study of mixtures (Frank, Formaker, &

Hettinger, 2003; Smith & Theodore, 1984), and researchers showed that rats could

identify the CS in a mixture in a concentration-dependent manner.

There are some limitations associated with this approach, which include effects of

stimulus familiarity, extinction, concentration, and stimulus hedonics. First, the CTA

procedure is only successful if the CS is novel to the animal, thus limiting the choice of

CSs to unfamiliar compounds. Additionally, typically only one CS is used per

experimental group, which means that each concentration of the compound included in









the study also requires a devoted set of animals. It follows that this substantially

increases the number of animals required for a comprehensive study of taste quality

generalization. In addition, an inclusive design would require the researcher to also test

for cross-generalization of each of the concentrations selected, and therefore, it is

necessary to use a large number of animals.

A second limitation of the CTA approach relates to the strength of conditioning.

Testing occurs in extinction, meaning that the animal does not experience the

unconditioned-stimulus-induced consequences previously paired with intake, and so the

effects of learning can diminish over time. Often the strength of the conditioning is

reassessed periodically, and the avoidance to the CS indeed lessens. This complicates

interpretation of results and limits the number of potential test stimuli (Nowlis, Frank,

and Pfaffmann, 1980).

A third limitation of the CTA procedure is associated with stimulus intensity

dynamism, which must be carefully considered in the interpretation of generalization

profiles. Intensity dynamism refers to the observation that conditioning to a CS will

generalize similarly to all higher concentrations of that compound, rather than as an

inverted-V gradient, peaking at the CS, as might be expected (see Guttman & Kalish,

1968; Hull, 1949). In other words, there is a steep gradient beginning at some

concentration below the CS, but the behavioral profile obtained reveals that increasing

the intensity of the compound results in a greater or at least similar conditioned response.

For example, if conditioning occurred to 0.05 M NaCl it would generalize to higher

concentrations, including 0.5 M NaCl but conditioning to 0.5 M NaCl might not









generalize to the lower concentration. That is, cross-generalization does not necessarily

occur between high and low concentrations of the same compound.

Finally, the inherent affective properties of a taste stimulus can influence the

interpretation of CTA results. If a solution is unconditionally aversive, an animal will not

readily consume much, if any, of the compound. This could compromise both the

effectiveness of the pairing and the ability to measure behavior (i.e., floor effect).

Therefore, some compounds, which are preferably ingested, better lend themselves to a

procedure like CTA. In fact, this is a key problem facing those who study taste

classification. Some compounds, especially "bitter" and "sour" solutions are not readily

consumed by rats and attempts to incorporate them into CTA designs can result in

problems associated with floor effects. That is, it is difficult to quantify changes in intake

for an experimental versus control group when intakes of the taste stimuli for both groups

are low.

Ideal Psychophysical Task

An ideal psychophysical task would have the following characteristics: it would 1)

be compatible with assessing discriminability and generalization within the same

animals, 2) allow for repeated test (probe) trials within the same animals, 3) yield clear,

interpretable results, 4) be highly replicable within and between animals (i.e., have little

variance in responses), and 5) circumvent the potential confounding of stimulus intensity.

Importance of Psychophysical Analysis in Animal Models

Advances in our understanding of taste function can be optimally achieved through

a combination of experimental approaches. Arguably, it is the innovation of rigorous

behavioral techniques that facilitates the confirmation or refutation of predictions about

gustatory function that are based on more reduced levels of analysis (i.e.,









electrophysiology, molecular biology, etc.). In addition, carefully executed

psychophysical experiments produce results that generate new hypotheses regarding how

the gustatory system is organized. Psychophysical tasks, though time consuming, provide

invaluable data on the sensory capacities of both humans and non-human animals.

Psychophysical analysis of non-verbal subjects is challenging but can be achieved

through the use of operant and classical conditioning procedures.

Chief among the benefits of using a psychophysical approach with non-human

animals is that invasive procedures, in which the gustatory system can be manipulated,

are possible. Taste function is complex; therefore, the design and application of a variety

of psychophysical measures is necessary to obtain a comprehensive assessment of

function.

The failure to develop appropriate tasks can lead to misguided conclusions. For

example, the two-bottle preference test has been, and continues to be, the most common

behavioral measure of taste responsiveness in animals. This measure, however, only

assesses the motivational characteristics of a taste stimulus. Moreover, postingestive

events can influence the behavior. Certainly, the use of this procedure masked for many

years the understanding of the contribution of gustatory nerves in the processing of taste

input (e.g., Pfaffmann, 1952; Richter, 1939; see Spector, 2003a for discussion).

Argument for the Development of Psychophysical Tasks

The use of appropriate behavioral procedures directed at measuring taste function

in animal models has been indispensable in the analysis of the neural organization of the

gustatory system (e.g., Flynn, Grill, Schulkin, & Norgren, 1991; Flynn, Grill, Schwartz,

& Norgren, 1991; Kopka & Spector, 2001; Kopka, Geran, & Spector, 2000; Slotnick,

Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990; Spector & Grill,






15


1992; St. John, Markison, & Spector, 1995; Shimura, Grigson, & Norgren, 1997). It

follows, therefore, that the development of new behavioral paradigms that are aimed at

yet unexplored aspects of gustatory function promise to lead to further important

discoveries.














CHAPTER 2
RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO SAMPLE
TASK USING ONLY TASTE STIMULI

Background

A major goal of this project was to develop a novel behavioral task that would

address whether rats can accurately assess when two samples, tasted in sequence, differ

or whether they are the same. The paradigm combines two procedures, a match to sample

and non-match to sample task. Potentially, such a task could be used to assess the degree

of qualitative discriminability between two taste stimuli. Another possible benefit of this

paradigm is that once the animal has sufficient training in the contingencies of the task,

various compounds or concentrations could be added for testing.

Such a procedure was introduced by Konorski, in 1959, who apparently suggested

it could be used with olfactory or auditory stimuli because they both were sensory

modalities which were incompatible with simultaneous delivery of test stimuli (in Shimp

& Moffit, 1977). The taste system is also incompatible with simultaneous delivery of

two comparison stimuli. This inherent delay between samples, as a consequence of the

rat sequentially sampling two separate stimuli within a single trial, provides the

opportunity to allow one to assess the properties of short-term memory processes

involving taste stimuli a phenomenon that has not been previously approached. To

date, only long-term memory has been studied in the taste system via conditioned taste

aversion (CTA), which is not optimally designed for multiple trial analyses.









Method

Animals

Nine adult male Sprague-Dawley rats weighing 555 +/- 20 g at the start of training

were used as subjects. Two of the animals were euthanized within the first two weeks of

training: one demonstrated a response bias within the first few discrimination training

(see below) sessions and was removed from the study to allow for an increase in session

length for the other rats, and the other rat removed his surgically implanted intraoral

cannulae (see below) and thus required immediate euthanization. Therefore, seven rats

served as subjects in the experiment. The rats came from Charles River (Wilmington,

SC) and were maintained on Purina (5001) laboratory rat chow ad libitum (except during

experimental test sessions) in a vivarium that had the lights and temperature

automatically controlled. Lights were programmed to be on a 12:12 hour light:dark cycle

with lights on at 0700 h, but due to an undiscovered timer malfunction the animals were

in constant light during the first 110 days of training and testing. A contingency was in

place so that rats would receive supplemental water if body weight decreased to 85% of

the ad libitum weight calculated each week; this contingency was only necessary for one

of the animals on three separate occasions. All procedures were approved by the

University of Florida Institutional Animal Care and Use Committee.

Apparatus

In the present experiment a gustometer, which is a specially designed stimulus

delivery and response measurement device, was modified from an earlier version

described in detail elsewhere (Spector, Andrews-Labenski, and Letterio, 1990), and was

used in training and testing. Briefly, the test chamber had two response spouts which

flanked either side of a central slot through which the animal could access a sample spout









controlled by a stepping motor. There were two cue lights positioned 4.2 cm above the

response spouts which could be activated at the appropriate time in the trial. The

response spouts served as the source for water reinforcement when the animal performed

the appropriate behavior (licked the appropriate response spout after tasting a specific

combination of solutions). Fluid stimuli and the water reinforcer were contained in 11

pressurized reservoirs connected to solenoid valves to regulate the amount of fluid

deposited into the spout. Background masking noise was present during each session, and

the test cage was enclosed in a sound-attenuating chamber housed within a dimly lit room

to minimize possible extraneous cues related to stimulus delivery. A Polyethylene (PE)-

100 tube, covered by a spring, was connected via a swivel to a solenoid valve which was,

in turn, connected to a water reservoir. This tube was inserted through a small hole in the

ceiling of the sound attenuation chamber where it was connected to an intraoral cannula

implanted in the rat. This was used to provide water rinses between stimuli as described

below.

Stimuli

All solutions were prepared daily with purified water (Elix 10; Millipore, Billerica,

MA) and reagent grade chemicals, and were presented at room temperature. Initially, we

attempted to use 0.1 M NaCl and 0.5 M NaCl as training stimuli, but the overall

performance of the rats remained at chance. Consequently, the rats never progressed out

of the training phase and it was deemed necessary to change the training stimuli after two

months (35 sessions). Two solutions were used in the second phase of training: 0.1 M

NaCl and 0.1 M sucrose. We reasoned that a discrimination between two compounds

that are of different qualities might be easier to learn. Although we know that rats can

discriminate NaCl on the basis of concentration (Colbert, Garcea, & Spector, 2004), we









believed it might reduce the acquisition time if the two stimuli differed chemically and

were putatively members of different perceptually qualitative classes so as to render them

more distinct from each other. The initial training results with 0.1 M and 0.5 M NaCl

will be ignored for the remainder of this chapter.

Taste stimuli were prepared fresh daily from reagent grade chemicals (NaCl and

sucrose: Fisher Scientific, Atlanta) and purified water (Elix 10; Millipore, Billerica, MA);

they were presented at room temperature

Surgery

Rats were anesthetized with a mixture of 125 mg/kg body wt ketamine, 5 mg/kg

body WT xylazine (injection given intramuscularly) and two intraoral (10) cannulae were

surgically implanted so that water could be infused directly into the mouth. The rats were

placed in a surgical head holder and an incision was made along the midline of the scalp.

The fascia was cleared and four small machine screws were inserted into holes drilled

into the skull. The rat was then removed from the head holder and placed in a supine

position. The blunt end of a 19g needle shaft was attached to the opposite end of heat-

flared PE-100 tubing. A small Teflon washer was slipped onto the cannulae and placed

against the heat-flared end. The beveled end of the needle was then placed between the

cheek and gum, anterolateral to the first upper molar on either side of the mouth, and the

needle was pushed through the tissue in a trajectory that passed beneath the zygomatic

arch close to the skull until the Teflon washer and heat-flared end of PE tubing rested

against the roof of the mouth lateral to the maxillary molars. The needle was separated

from the PE tubing, the excess was trimmed, and a blunt piece (-10 mm) of 19 gauge

stainless steel tubing, with a bead of solder attached, was securely fitted into the PE

tubing. Both cannulae were placed in the same manner. Once in place, dental acrylic









was added so that it created a mound over the screws and secured the cannulae (PE

tubing + 19 G stainless steel tubing with bead of solder for extra anchoring) firmly in

place. All rats were injected with a prophylactic dose of penicillin G Procaine suspension

(30,000 units, s.c.) and the analgesic ketorolac tromethamine (2 mg/kg body mass, s.c.)

immediately before surgery and on the following 3 days. At least three months passed

before animals began training.

The intraoral cannulae were cleaned out every day by passing a smaller diameter

(polyethylene-10) tubing through the cannulae until it exited into the oral cavity. The

intra-oral cannulae were implanted so that water could be infused into the oral cavity

between taste samples in order to reduce the potential for adaptation to occur to the first

stimulus in the pair.

Training and Testing Phases

Training and testing sessions took place Monday through Friday of each week

during the regularly scheduled lights-on phase. Rats were water restricted beginning

Sunday night and received all daily fluid within the session. At the end of the last session

on Friday, water bottles were returned to the home cages until the following Sunday.

Spout training

The rats had access to only one spout (either the sample spout, the left response

spout, or the right response spout) and each spout was connected to a reservoir that

contained water. The purpose of this phase was to train the rats to approach and gain

familiarity with getting fluid from each of the spouts. Eventually, the sample spout

would contain a taste stimulus and only the response spouts would contain water. The

rats had to learn to lick from the sample spout and then select one of the response spouts









by licking it. There were a total of 6 days of spout training so that the rats experienced

two sessions with each spout.

Side training

Only one trial type was presented within a given session during side training. If the

rats were trained with same trials then, the trials within the session alternated between 1)

0.1 M NaCl followed by 0.1 M NaC1, and 2) 0.1 M sucrose followed by 0.1 M sucrose.

During the next session, the rats received only different trials in which the first sample

differed from the second (0.1 M NaCl followed by 0.1 M sucrose or 0.1 M sucrose

followed by 0.1 M NaC1). After sampling, rats had 180 s (limited hold period) during

which they were required to respond. If they made the correct response, they had limited

access to water (20 licks or 10 s, whichever occurred first). Side training lasted a total of

4 days.

Alternation

During alternation training, the rats started out with a single trial type (either same

or different). Upon completion of a set criterion of correct responses, the program

automatically switched to delivery of the opposite trial type. The correct responses did

not have to be consecutive. The limited hold was changed from 180 s to 15 s.

Additionally, if the rat failed to initiate the second sample within 15 s of the spout

becoming available, the trial terminated and punishment (timeout) was delivered. During

the decision phase, if a rat failed to make any response, or made the incorrect response, a

10-s timeout was presented.

Discrimination training I-II

Trials were delivered in a block with a random pattern selected by the computer

program. Therefore, the rats had no indication from the prior trial, which solutions would









be offered on the current trial. The block size was 8; consequently, every trial was

repeated twice within the block before a new block of randomized presentations

occurred. Reinforcement licks were changed from 20 to 25, and timeout increased to 20

s during this phase. Session length was increased to 50 min.

Trial structure (final parameters)

During the 65-min test session, each rat was allowed to complete as many trials as

possible within the time allotted. Each trial (see Figure 2-1) consisted of six different

phases: sample 1, inter-stimulus interval, sample 2, decision, consequence, and inter-trial

interval. The sample phase began when the rat made contact with the dry sample spout

and initiated licking. The rat was required to lick the dry drinking spout twice within 250

ms, upon which the shaft of the drinking spout was filled with the stimulus and each

subsequent lick resulted in an additional deposit of 5 ul into the fluid column. The rat was

allowed 3 s access to the stimulus or five additional licks, whichever came first. A 6-s

interstimulus delay followed the first sample during which 30Ll of fluid was infused into

the mouth through the left intraoral cannula. Additionally, during this inter-stimulus

interval, the sample spout was rotated over a funnel, rinsed with purified water, and air-

dried in preparation for the second sample, which followed the same initiation

requirements as stated above. If the rat failed to initiate the second sample within 2 s of

the spout becoming available, the spout rotated away from the access port and the trial

moved immediately into the consequence phase during which the rat received a timeout.

In a trial in which the rat properly initiated both samples, the houselights in the

gustometer were turned off and the cue lights above each lever were illuminated,

signaling the start of the decision phase. Concurrently, the sample spout was rotated out

of position so that it could no longer be accessed. During the decision phase, rats were









allowed a prescribed period of time (5 s during the testing phase, referred to as the

limited hold) to respond by licking the correct response spout. If the correct spout was

licked, the houselights were reactivated and the rat had the opportunity to receive 10 s or

40 licks access to water, whichever came first. If the incorrect spout was selected or no

response was made within the limited hold period, the cue lights were extinguished and

the rat was given a 40-s timeout, during which fluid was unavailable. The trial terminated

with a 48-s intertrial interval, during which all lights were off until the next trial began.

Testing

Testing began 46 sessions after the very first spout training day. The parameters

were the same as those used at the end of Discrimination Training II.

Adjustments to Testing Parameters

Initially, the trial parameters were set during the Discrimination Training II phase.

There were, however, some adjustments made to the trial parameters, during the 21-week

testing phase, in an attempt to increase performance in the rats. In the fifth week, the

magnitude of the reinforcer was increased from 25 licks to 40 licks. In the sixth week,

the timeout was increased from 20 s to 40 s. In the twelfth week, the inter-trial interval

was increased from 10 s to 48 s. In the sixteenth week, the session length was increased

from 60 min to 65 min. During the seventeenth week, session length was increased from

65 min to 70 min, but was reduced again because the rats stopped responding near the

end of the session. Finally, beginning in the nineteenth week, the intraoral rinses were

discontinued because of problems with intraoral cannulae coming loose. Consequently,

two of the rats had to be euthanized because of this problem during the nineteenth and

twentieth weeks of testing.









Statistical Analyses

For data analyses, repeated measures analysis of variance (ANOVA), one-sample t-

tests, and paired t-tests were used; Bonferroni adjustment was applied where appropriate.

The mean weekly performance score for each trial type for every animal during the first

eighteen weeks of testing were used in the analyses. This time period was chosen for

analysis because it spanned the testing period in which all rats had intraoral rinses and it

also included the weeks for which data were available from all subjects. For each of the

weeks tested, every animal had six performance scores: the two same trial types, the two

different trial types, and an integrated score for both same and different trials.

Results

Overall Performance

Results are shown in Figures 2-2 through 2-5. As shown in Figure 2-2, the mean

performance on the task did not exceed 75%.

A repeated measures ANOVA of overall performance across testing weeks

revealed that the rats performed significantly better over the 18 testing weeks analyzed

(F(17,102) = 10.070, p < 0.001). Multiple one-sample t-test comparisons (null

hypothesis is 50%) of performance during each week, revealed that performance was

better than chance levels initially (t(6) = 2.680, p = 0.037), but a Bonferroni adjustment

eliminated the statistical significance of the comparison (p = 0.658). Beginning at the

third week of testing, however, both the p-value and the Bonferroni adjusted p-value

revealed significant differences (all ps < 0.035), which remained so for the duration of

testing (all Bonferroni adjusted ps < 0.03). Of note is a drop in performance at week 14,

which was attenuated by recalibration of the apparatus to deliver the appropriate volume

per lick.









Performance on Same Trials

A graph representing performance on same trials when the trial was NaC1-NaCl or

sucrose-sucrose is shown in Figure 2-3.

A repeated measures ANOVA was used to analyze performance to both types of

same trials. There was a main effect of time (F(17, 102) = 4.52, p < 0.001), but no main

effect of trial type and no interaction was present (both p-values > 0.2). Therefore, these

data could be used to support the claim that rats may have learned to respond to the trial

type regardless of what the chemical compound was.

Performance on Different Trials

Figure 2-4 depicts the performance to different trial types. A repeated measures

ANOVA was used to compare performance on NaCl-sucrose trials to sucrose-NaCl trials.

There was a main effect of time (F(17,102) = 3.290, p < 0.001), but no evidence of a

main effect of trial type (p > 0.34) nor an interaction (p > 0.80). Therefore, when both

compounds are presented within a trial, it does not appear to matter whether the first

sample is NaCl or sucrose.

Performance on Same Trials versus Different Trials

Figure 2-5 shows the performance of same trials collapsed across compounds

versus performance of different trials also combined together. It would appear (Figure 2-

5) that rats perform better on different trials, especially initially, but a statistical analysis

of the performance between same and different trials does not support such a claim. A

repeated measures ANOVA comparing the 18 weeks of testing revealed a main effect of

time (F(17,102) = 3.303, p < 0.001), but no effect of trial type and no interaction was

present (both p-values > 0.50). Additionally, a paired t-test examining the first week of









testing did not provide evidence that performance on the two trial types differed (t(6) = -

2.153, p = 0.084).

Discussion

Results from the present study indicate that rats are able to reliably respond to two

taste stimuli, separated by a 6-s delay, and sampled within a single trial, on the basis of

whether they are the same or different. This is the first known report of its kind involving

the taste modality. Below, the performance of the rats in this taste behavioral paradigm is

placed in context with other sensory modalities.

Steckler, Drinkenburg, Sahgal, and Aggleton (1998) published a series of three

articles outlining the ability of rodents at, what they termed, "recognition memory" tasks

and the underlying neuroanatomical substrates mediating such performance. Overall,

they claimed that rodents can acquire these tasks, but do not typically perform at high

levels. Their work, however, focuses on particular tasks using objects or spatial stimuli.

It is interesting that the rats in this experiment did not perform better on the

different trials. Wright and Delius (2005) reported that pigeons performing a matching-

and oddity-to-sample task acquire the oddity-to-sample most rapidly. In fact, there are

published data that suggest a preference for stimuli that do not match (the oddity-

preference effect) (Ginsburg, 1957). There is also a previous account in which matching

performance begins at or below chance (50%) and non-matching performance begins

higher than chance, though these studies used pigeons and differed procedurally from the

task presented here (Zentall Edwards, Moore, & Hogan, 1981).

An experiment by Wallace, Steinart, Scobie, and Spear (1980) might also provide

information worth considering regarding the difficulty the rats had performing at high

levels in this task. In their study, rats performed better in a delayed matching task on









trials that contained auditory sample stimuli rather than visual (an illuminated light)

stimuli. The differences in performance between the two modalities disappeared when

the delay was 0 s, but emerged when delays were longer. Perhaps taste stimuli are not as

salient as stimuli from other modalities.

Interestingly, Slotnick and colleagues (1993) reported that rats can learn an odor

matching task and perform at very high levels (>90%) even with a delay of 10 s and

presentation of a masking odor between samples. The reason for the disparity in

performance between their rats and those in the present task are unknown, but there are

procedural differences that may explain some of them. They used a conditional go/no-go

discrimination task, which allowed many more trials and far fewer reinforcers to be

delivered; that difference may have helped acquisition of the task in their case.

Additionally, they used a learning set of stimuli, consisting of several different scents;

thus, it is possible that experience with a variety of training stimuli would improve

acquisition of the task. If such an approach was adopted with taste stimuli, it remains

possible that higher levels of performance would be seen.

Finally, one reason that the mean performance did not surpass 75% might be

related to the ratio between the interstimulus delay and the intertrial interval. One

published study, using pigeons in a visual discrimination paradigm, showed that the

overall correct responding changed when the experimenter varied the ratio of

interstimulus delay to intertrial interval (Roberts & Kraemer, 1982). Specifically, they

tested ratios of 0.5, 2, 8, 16, 32, and 64 and reported that when the delay between trials in

their experiment was the greatest, the highest levels of performance occurred (Roberts &

Kraemer, 1982). In the present study, design limitations of the gustometer restricted the









minimum interstimulus interval to 6 s. According to Roberts & Kraemer's 1982 study,

with a delay this long, it would have been optimal to use an intertrial delay of 386 s. This

was not practical because either the number of trials or the number of sessions possible

per day would have been dramatically reduced. In light of these findings, one might even

conclude that the rats in the present experiment performed as well as would be expected;

this statement is based on the fact that the subjects in Roberts & Kraemer's (1982) task

performed at 77% when the ISI/ITI ratio was 8, as it was in the present study. Therefore,

reducing the delay or lengthening the intertrial interval would be predicted to improve

performance. Perhaps in contrast to that statement, however, is evidence from Sargisson

and White (2001), who showed that delay appears to become part of the training stimulus

and shares a portion of discriminative control, thus lowering the delay in testing might

actually decrease performance if the animal acquired the task at a higher interstimulus

delay. These are potentially addressable issues empirically.

It might have been insightful to include different test compounds at the end of the

testing period to establish if the rats would be able to apply the concept of sameness or

difference. It is possible that the performance in this test was contingent on prior training

with these compounds, and the learning would not generalize to novel compounds. Thus,

it would have been informative to discern whether such a transfer would have occurred.

If rats acquired high levels of performance to the new set of stimuli more quickly than

with the first set, then it might support the claim that rats could learn to perform the

conceptual task of sameness and/or difference. We felt that the current level of

performance was not sufficiently high to pursue this question. Nevertheless, in the

future, especially if optimal testing parameters can be achieved to increase overall









performance levels, adding a variety of test compounds should be included in the

experimental design. Perhaps using multiple training compounds would actually help to

establish higher levels of performance (see Slotnick et al., 1993).

Overall, the results of the present study were encouraging that such a procedure

could be used to study rodent discrimination ability. It certainly seems reasonable that

lowering the delay between stimuli would increase the overall task performance and

allow more options for discrimination (e.g., solutions that vary in intensity).

Additionally, this approach also shows promise for the investigation of short-term

memory in the gustatory neuraxis, which might ultimately provide information about the

properties of the system, the structures involved, and how taste short-term memory

compares with other forms of taste memory and memory processes involving other

sensory modalities. Further development of this task could reveal properties of

neurobiological mechanisms underlying certain forms of behavior.

Unfortunately, because the performance of the rats on the task was not optimal for

continuing in the same research direction, an alternative avenue to assess taste quality in

rats was required. This, however, does not detract from the potential success of the task

outlined above, but because the technical limitations could not be overcome at present, it

was decided to move ahead in a different direction.






























Figure 2-1. Trial structure for DMTS/DNMTS (same/different) task.












TOTAL PERFORMANCE


0 1 2 3 4 5 6


7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)


Figure 2-2. The mean overall performance to all trial types is shown. Performance on
the task increased over the course of the experiment and became significantly
different from chance during the 3rd week of testing.


_______________________











100 NaCI-NaCI -0-NaCI Same
*g NaCI-NaCI a )
Svs. Sucrose Same
Sucrose-Sucrose |
90



80 i



w 70


60
3 60



50 ------- --------------------



40
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)


Figure 2-3. Mean performance to same trials. The performance of the rats did not differ
depending on the stimulus that was included in the same trials. The rats
improved over the course of the experiment.











100 NaCI-Sucrose NaCI Sucrose
NaCI-Sucrose a
vs. Sucrose- NaCI
Sucrose-NaCI .
90
W W






70



S60



50 ------------------------------------------------ ------------------------------



40
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)


Figure 2-4. Mean overall performance to different trials. Rats did not perform
significantly differently on trials containing both compounds regardless of the
order that the stimuli were sampled.











100 -Same
lO00 I : SAME vs. DIFFERENT Same
S--Different

90





90

C-)
E
w 60
40i I



50 J^ - -- -




0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)


Figure 2-5. Mean performance on same versus different trials. There was no statistical
evidence that the performance on different trials was better than performance
on same trials.














CHAPTER 3
A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION IN
RATS

Introduction

With some exceptions, the most common method used to assess taste quality in

rodent models is the conditioned taste aversion (CTA) generalization paradigm. In this

procedure an animal is presented with a taste solution, which serves as the conditioned

stimulus (CS), followed by induction of visceral malaise. After such a conditioning trial,

animals will avoid ingesting the CS as well as compounds that are thought to possess a

similar taste quality. Although this procedure has provided useful information to

researchers interested in taste processing, it has some interpretive and methodological

limitations. One constraint is that a novel CS must be used with each group. Thus, a

large number of animals are required to comprehensively assess taste quality

generalization. Another key problem is that some stimuli (e.g., quinine or HC1) are

inherently avoided by rats, hence making it difficult to differentiate conditioned from

unconditioned suppression of intake (e.g., "floor effect"). Additionally, as described in

Chapter 1, stimulus intensity dynamism presents another caveat for data interpretation

that must be considered. Because an animal will show an increased conditioned response

to concentrations higher than the CS, it becomes important to know what the relative

intensity differences elicited by different compounds might be. Finally, given that-testing

occurs in extinction, the number of test stimuli and test sessions possible is restricted.

For the reasons outlined above, a major goal of this experiment was to develop a









procedure that circumvents the interpretive and methodological limitations associated

with the CTA approach.

Morrison (1967) introduced a unique behavioral procedure that examined taste

generalization in a different manner. He trained a group of rats to press one lever if the

compound sampled was 0.1 M NaCl (the standard), and another lever if the sample was

0.1 M sucrose. He trained another group of rats to discriminate that same concentration

of NaCl from 0.01 M HC1. Finally, he trained a third group of rats to discriminate the 0.1

M NaCl from 0.5 mM quinine. Next, he was able to determine which response each

group made when given a novel test salt. Profiles, based on whether they responded on

the standard (NaC1) lever or the comparison lever, were derived. This design included all

four prototypical taste compounds split across the three groups, so by placing the

proportion of responses made on the comparison lever together on the same graph, it

represented how sucrose-like, quinine-like, and hydrochloric acid-like the test salt was.

If the profile was not any of the three, then the compound was assumed to be entirely

NaCl-like.

Though this approach is clever, it still has some limitations. First, within a single

group, it is not intuitively obvious how to interpret a compound that is similar to neither

of the two compounds. If the basic tastes are indeed different from one another,

presenting a compound from a separate taste quality would not be expected to fall

exclusively on either one of the training stimuli for a given group, yet a score of 0.5

would indicate that the test compound shared similarities with both. Morrison does not

address this possibility (Morrison, 1967). Perhaps a better paradigm would involve

training the rats to discriminate a taste compound putatively representing one quality









from taste stimuli thought to represent all other proposed qualities. In this approach, the

rat might learn to focus solely on one taste in order to separate the features of that

compound from all others. If that occurred, then when a rat responded to a novel

compound as if it were the standard, it would indicate that the test compound was similar

to the standard.

Secondly, Morrison (1967) used only a single concentration of each prototypical

stimulus. In that study intensity was not varied to make it an irrelevant cue. Therefore, it

is unknown whether the rats in Morrison's (1967) experiment were responding on the

basis of intensity differences or quality differences. A better approach would be to

include several concentrations of each training stimulus to decrease the relevance of

intensity making taste quality the only reliable cue.

The present study was undertaken to expand upon Morrison's (1967) design and to

incorporate improvements to overcome his experimental shortcomings. Namely, the

differences include an attempt to train the rats to focus on discriminating a single

prototypical compound, representing the putative four basic taste qualities, from the

remaining three. Additionally, inclusion of a broader array of concentrations of the

standard stimulus is intended to circumvent problems that might occur with

generalizations based on intensity features.

In order to choose a broad range of concentrations that represent the prototypical

stimuli and include overlapping intensities, a brief-access taste test was conducted with

one prototypical representative from each of the putative 4 basic taste qualities. The goal

of Experiment I was to identify concentrations of NaC1, sucrose, quinine, and citric acid

that span the dynamic range of intensity, which would be used in Experiment II.









Experiment I

Method

Subjects

Eight naive, adult, male Sprague-Dawley (Charles River Breeders; Wilmington,

MA) rats were used. The rats were housed individually in polycarbonate shoe-box style

cages in a room where temperature, humidity, and light cycle (lights on 7am 7pm) were

controlled automatically. All manipulations were performed during the light phase. The

rats had ad libitum access to Purina Rat Chow (5001) in the home cage. Purified (Elix

10; Millipore, Billerica, MA) water was also available ad libitum except where indicated.

All procedures were approved by the University of Florida Institutional Animal Care and

Use Committee.

Training Stimuli

All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica,

MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli

consisted of six concentrations of sucrose (0.01, 0.03, 0.06, 0.1, 0.3, and 1.0 M; Fisher

Scientific, Atlanta, GA), NaCl (0.03, 0.1, 0.2, 0.3, 0.5, and 1.0 M; Fisher Scientific,

Atlanta, GA), citric acid (0.3, 1, 3, 10, 30, and 100 mM; Fisher Scientific, Atlanta, GA),

quinine (0.01, 0.03, 0.1, 0.3, 1.0, and 3.0 mM; Sigma-Aldrich, St Louis, MO) and

purified water.

Procedure

A brief-access procedure similar to that described by others (e.g., Glendinning,

Gresack, and Spector, 2002; St. John, Garcea, and Spector, 1994; Spector, Redman, and

Garcea, 1996) was used. Testing took place in the gustometer, which was described in

Chapter 2. The sample phase began when the rat made contact with the dry sample spout









and initiated licking. The rat was required to lick the drinking spout twice within 250 ms,

upon which the shaft of the drinking spout was filled with the stimulus and each

subsequent lick resulted in an additional deposit of 5 [l into the fluid column. During the

session, the rat was allowed access to a single concentration for a brief period of time (5

s) and then after a 6-s inter-presentation interval during which the sample spout was

rotated over a funnel and rinsed with clean water, a different solution was offered. The

stimulus array for each compound tested included the six different concentrations

detailed above and purified water. A given trial started after the first lick. Trials were

presented in randomized (without replacement) blocks so that every concentration of a

stimulus and water was presented exactly once before the initiation of the subsequent

block. Unconditioned licking responses were recorded for later analysis. Sessions were

30 min in duration during which rats could initiate as many trials as possible. The

animals were first trained to lick a stationary spout delivering water for 30 min in the

gustometer after being placed on -23.5-h restricted water access schedule. For sucrose

testing, animals then received 2 days of testing with six stimulus concentrations and

purified water while maintained on the water-restriction schedule. During this period of

training, the sample spout rotated away from the access slot between trials. The two days

of sucrose training under a water-restriction schedule was done to familiarize the animals

to approaching and licking the spout. Water bottles were then returned to the home cages

for three days, following which, the rats were tested for three days under conditions of

non-deprivation. After the last sucrose session, water bottles were again returned to the

home cages for a rehydration period before the next-testing week. When the test

compound was not sucrose, rats were placed on a water restriction schedule on a Sunday









night, placed into the gustometer for two days of testing with water from a spout which

rotated between trials, and then tested for three days under water-restriction. During the

three 3-day test sessions with NaC1, citric acid, and quinine, respectively, water rinses

were presented between each taste stimulus. A rehydration period always occurred

between test compounds.

Data Analysis

A Tastant/Water Lick Ratio was calculated for the data that were collected during

sessions with water-restricted rats. This ratio was computed by taking the average

number of licks per trial for each concentration and dividing it by the average number of

licks per trial when water was delivered as a taste stimulus. This ratio standardizes the

data to control for individual differences in lick rates. In the non-deprived condition, the

average number of licks per trial for each concentration was divided by that animal's

estimated maximal lick rate (licks/5 s) yielding a Standardized Lick Ratio. The maximal

lick rate was calculated using the reciprocal of the mean of the inter-lick interval (ILI)

distribution (in s) that was measured during training (only inter-lick intervals >50 ms and

<200 ms were used) and multiplying this value by 5. Standardizing the licking response

in this fashion controls for individual differences in lick rates.

These data were used to select concentrations of NaC1, quinine, and citric acid

which elicit similar lick suppression relative to water. The mean lick data for each

concentration were plotted and then a three-parameter logistic equation was used to fit a

curve to the data: f(x) = a/(l+lOb(x-c)), where a is the asymptote (note, for NaC1, quinine,

and citric acid, a was a constant set at 1), b is the slope and c is the point of inflection.

The resulting curve was used to guide the choice of concentrations for Experiment II.









Results

Results from the brief-access test are shown in Figures 3-1 3-4. Table 3-1 lists

the concentrations selected to represent training stimuli for each prototypical compound.

Unfortunately, the incorrect lowest concentration of quinine was included in the proposed

training array through a typographical error. Instead of using the intended concentration

of 0.0827 mM of quinine, 0.027 mM was recorded. Consequently, that low concentration

became incorporated into training array of Experiment II. The lowest training

concentration of quinine, 0.027 mM is only about twice the most conservative measure of

detection threshold for quinine. (0.012 mM, Koh & Teitelbaum, 1961; 0.005 mM, Thaw

& Smith, 1994; 0.003 mM, Shaber, Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St.

John, & Spector).

In order to determine which concentrations resulted in a reduction in licking at a

specific level (i.e., 20%, 40%, and 60% suppression), the equation was rewritten to solve

for x, such that x = (loglo((a/y)-l)/b)+c). Following the selection of concentrations

associated with 20%, 40%, and 60% suppression rates, a concentration that was one order

of magnitude (i.e., 1 logo unit) below the highest concentration of NaCl (which was

associated with a 60% reduction in licking as compared with water) was identified. For

sucrose, the opposite strategy was taken and concentrations that were 40%, 60% and 80%

of their maximal licking rate to water were used along with the concentration that was

approximately 1 logo unit above the lowest concentration selected. The lowest

concentration for citric acid was selected to be -1.5 logo units below the concentration

associated with a 60% reduction in licking because otherwise, there would have been

little difference in behavioral responding for the concentration associated with a 40%

reduction of licking and the intended one that was 1 log unit below the highest









concentration. For quinine, it was our intention to choose a concentration that was 1.0

logo unit lower than highest concentration selected (i.e., 0.0827 mM), but an erroneous

value was selected (0.027 mM) that was actually -1.5 logo units lower. Regardless, all

concentrations spanned at least 1 logo unit and incorporated the dynamic range of

responsiveness measured in this task.

Discussion

The selection of training stimuli suitable for Experiment II was based on the three

isoresponsive concentrations and the additional concentration for each compound that

allowed for the range of concentrations to span at least 1 logo unit. For the aversive

stimuli (NaC1, quinine, and citric acid), intensities at which rats reduced their licking to

the same benchmark level of performance were selected. The three compounds are

referred to as aversive because the rats decreased their licking monotonically as

concentration was raised. For the appetitive stimulus, sucrose, the concentrations that

resulted in alterations in licking were similarly selected except that the changes in

concentration resulted in increased levels of licking rather than suppression. Thus, we

attempted to match the three highest concentrations of aversive compounds with the three

lowest concentrations of sucrose, with respect to the effect that increasing concentration

has on behavior. Although this procedure likely does not result in exactly matching

intensities between compounds, we assume that it is a good approximation and

importantly provides some confidence that the concentrations chosen at least are

overlapping. Here, the same rats were used to determine the dynamic range of

concentrations for which licking is modulated across four compounds representing the

basic taste qualities.









It is plausible that there were order effects associated with the curves obtained for

each compound, considering that sucrose was the first stimulus to be tested, which was

followed by NaC1, citric acid, and then quinine. The nature of the prior experience with

sucrose may have trained the animal to accept stronger concentrations of the taste stimuli,

thus inflating the range of concentrations selected. Perhaps using a naive set of rats, or

randomizing the order of presentation between the rats, for each of the four compounds

would have yielded different results. An examination of the literature revealed that

comparison of the midpoint of the concentration-dependent curve for quinine obtained

here (approximately 0.4 mM) with those from two published studies examining brief-

access using quinine (approximately 0.3 and 0.2 mM) suggests that these rats did perhaps

accept higher concentrations than naive rats do (Spector and Kopka, 2002; St. John,

Garcea, and Spector, 1994). Nevertheless, potential parametric influences aside, the

experiment provided some basis upon which to choose a broad range of concentrations

for each stimulus that at the very least overlap in intensities.

Experiment II

The following experiment attempted to adapt Morrison's (1967) procedure,

described above, but incorporated a broader array of training concentrations and

comparison stimuli in order to test the following two hypotheses: 1) rats can learn to

discriminate prototypical compounds, characteristic of the putative basic taste qualities,

when a variety of concentrations are used to represent each compound, and 2) rats will

generalize the responses learned with training stimuli to novel untrained test stimuli.









Method

Subjects

Forty-eight naive adult male Sprague-Dawley (Charles River Breeders;

Wilmington, MA) rats served as subjects. The rats were housed individually in

polycarbonate shoe-box style cages in a room where temperature, humidity, and light

cycle (lights on 7am 7pm) were controlled automatically. All manipulations were

performed during the light phase. The rats had ad libitum access to Purina Rat Chow

(5001) in the home cage. Purified (Elix 10; Millipore, Billerica, MA) water was also

available, but was removed approximately 16 hours before (-4:00 pm the night before)

the first behavioral session of the week and was replaced at the completion of the last

session of the week. A contingency was in place that would allow rats to receive

supplemental water if body weight decreased to 85% of the ad libitum weight calculated

each week, but no rat dropped below that criterion in this experiment. One of the animals

was removed before side training (see below) began because it exhibited self-injurious

behavior. All procedures were approved by the Institutional Animal Care and Use

Committee at the University of Florida.

Apparatus

The apparatus was the same as that described in Chapter 2. There was, however,

no cannula lead entering the chamber from the port in the ceiling of the sound attenuation

chamber.

Task overview

The prototypical taste compounds NaC1, sucrose, quinine HC1, and citric acid were

used to represent the putative 4 basic tastes, salty, sweet, bitter, and sour, respectively.

Four groups of rats were trained to respond by licking one response spout after sampling









any of the 4 training concentrations of a particular standard, which for each group was

one of the prototypical compounds, and they were trained to lick a different response

spout after sampling any of the comparison stimuli (the remaining three compounds).

Stimuli

All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica,

MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli

consisted of four concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M;

Fisher Scientific, Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M;

Fisher Scientific, Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM;

Fisher Scientific, Atlanta, GA), quinine (0.027 mM, 0.131 mM, 0.360 mM, and 0.827

mM; Sigma-Aldrich, St Louis, MO) and purified water.

Groups

For overview of the four groups (N, S, Q, and C) and their associated standard and

comparison stimuli, see Table 3-2. Each of the groups was named for their standard

stimulus and was trained to discriminate four concentrations of that compound from four

concentrations each of the comparison stimuli (those from the remaining three

prototypical compounds).

Trial structure

On any given trial (see Figure 3-5), rats were trained to lick a centrally positioned

stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two

times with an inter-lick interval < 250 ms, then the predetermined solution filled the shaft

of the spout, after which the rat could receive up to 5 licks (-5 l was deposited into the

fluid column upon each lick) before the spout was rotated out of position. Next, a

decision phase was initiated, during which the rat was required to lick one response spout









after sampling the standard stimulus or the other response spout after sampling a

comparison stimulus. During the consequence phase, if the rat responded correctly to the

stimulus, water reinforcement was delivered directly through the response spout (20 licks

@ -5 tl per lick or a total of 10 s access, whichever occurred first). If the rat failed to

respond, or responded on the incorrect response spout, then the rat was punished with a

20-s timeout. After either consequence of the decision phase, the trial moved into an

intertrial interval that lasted 6 s.

Training

Spout training. The rats had access to only one spout (either the sample spout, the

left response spout, or the right response spout) and each spout was connected to a

reservoir that contained water. The purpose was to train the rats to approach and gain

familiarity with obtaining fluid from each of the spouts. Eventually, the sample spout

would contain a taste stimulus and only the response spouts would contain water.

Side training. Only one trial type was presented within a given session during side

training so that the solutions available alternated with each session. That is, if the rats

were trained with their standard compound in the first session, then during the next

session, the rats received only comparison compounds. After sampling, rats had 180 s

(limited hold period) during which they were required to respond. Side training lasted a

total of 4 days. Only the third highest concentration of each stimulus was presented. The

rats were required to lick the sample spout to obtain a small volume of the stimulus and

then select one of the response spouts by licking it. If the rat responded correctly, then

water reinforcement was available (10 s access or 20 licks, whichever came first). The

intertrial interval during this phase was 6 s.









Alternation. During alternation training, the rats started out with either a standard

or one of the comparison stimuli. Upon completion of a set criterion of correct

responses, the program switched to the opposite trial type. Each time the rat completed

the criterion of correct responses, the program automatically switched to delivery of the

other trial type. When the trial type consisted of comparison stimuli, the computer

randomly selected (without replacement) the solution to deliver. The correct responses

did not have to be consecutive. The limited hold was changed from 180 s to 15 s.

During the decision phase, if a rat failed to make any response, or made an incorrect

response, a 10-s timeout was initiated.

Discrimination training I-II. Stimuli were delivered in a block with a random

pattern selected by the computer program. Therefore, the rats had no indication from the

prior trial, which solutions would be offered on the current trial. All four training

concentrations were used in this phase, but because the gustometer had a limited number

of fluid reservoirs, only two concentrations (always one of the highest two and one of the

lowest two) of each prototypical compound were included per session. The block size

was 12; consequently, every standard concentration for a given session was repeated

three times within the block so that the number of standard stimuli matched the number

of comparison stimuli available (which were each only presented once per block). The

timeout period was increased to 20 s during this phase. After 12 days of discrimination

training, a partial schedule of reinforcement was introduced. During the session, two

trials (one standard and one comparison) from each block of 12 trials were randomly

selected to have neither reinforcement nor punishment delivered contingent on the

animal's response. That is, the animal did not receive reinforcement if it made the









correct response and it did not receive punishment if it made the incorrect response on

those selected trials. There was, however, a punishment contingency in place if the rat

failed to make a response. The partial schedule of reinforcement was introduced in

anticipation of the eventual inclusion of test stimuli, which would make up approximately

16% of the total trials in a session. The limited hold period (the time the animal was

allowed to make a response after sampling) was 5 s for this phase.

Test compounds

There was no correct response associated with a test stimulus, so the animal would

not receive reinforcement, but it also did not receive punishment for a response, unless it

failed to make the response before the limited hold period expired. In order to validate

whether rats would generalize untrained test stimuli to the standard compound, novel

concentrations of the training stimuli were presented. The following novel

concentrations of the training compounds and mixtures of NaCl and sucrose compounds

served as test stimuli:

* 0.847 MNaCl

* 0.068 M Sucrose

* 0.546 mM Quinine

* 42.56 Mm Citric acid

* 1.07 M (high) NaCl + 0.421 M (high) Sucrose

* 1.07 M (high) NaCl + 0.077 M (low) Sucrose

* 0.376 M (low) NaCl + 0.421 M (high) Sucrose

* Water









Retraining water as a comparison stimulus

Water was selected as a test compound because it has an interesting history in the

literature. Conceptually, water should represent the absence of a taste. The literature,

however, reveals that some humans (Anderson, 1959) report water as having a "bitter"

taste and animals respond to water as if it were quinine-like (Bartoshuk, 1977; Morrison,

1967).

Here, the profile for water as a test stimulus showed that water appeared to

generalize to quinine (see Results). It was not clear whether this was a result of the

erroneous inclusion of the very weak concentration of quinine in the training array, or if

water indeed has a quinine-like taste (note, these are not mutually exclusive).

Consequently, we attempted to train the rats to identify the difference between water and

quinine by adding water to the comparison group.

Negative control test

A water control session was included at the end of the experiment, in which all of

the reservoirs were filled with water. Two reservoirs were arbitrarily assigned to the

"standard" spout, and another six were designated as the "comparison" spout. This was

done to examine whether the rats were using non-chemical cues to guide their behavior.

Data analysis

A 1-way analysis of variance (ANOVA) was conducted for each test stimulus to

determine the presence of differences among groups followed by more detailed

Bonferroni-adjusted paired comparisons. Separate one-sample t-tests against both of the

null hypotheses 1.0 (i.e., the test compound was similar to the standard stimuli) and 0

(i.e., the test compound was similar to the comparison stimuli) were performed. The

conventional p < 0.05 value was used as the statistical rejection criteria.









Data for the negative control test were analyzed using a one-sample Binomial

analysis with null hypothesis = 0.5, which corresponds with the chance level of

performance.

Generalization score

A Generalization Score was calculated for each animal, which essentially

quantified the degree to which the test compound was similar to the standard stimulus.

The following equation was used to calculate the Generalization Score: [P(T)-P(C)] /

[P(S)-P(C)]; where, P(T) = proportion of times the rats responded on the standard

response spout when presented with a test stimulus; P(C) = proportion of times the rat

responded on the standard response spout when presented with a comparison stimulus;

and P(S) = proportion of times the rat responded on the standard response spout when

presented with a standard stimulus. Performance (reported as errors) to the comparison

stimuli was included in the equation in an attempt to account for response bias that may

have developed for individual animals, thus the Generalization Score serves to

standardize performance scores for each animal.

The data are presented as Generalization Scores for each group. Each vertical bar,

represents a different group and shows the degree to which the test compound was

behaviorally treated like the standard. A Generalization Score of 0 indicates that the rat

responded to the test compound as if it were a comparison stimulus. A score of 1.0

indicates that the rat responded to the test compound as if it were a standard stimulus. A

Generalization Score of 0.5 indicates that the compound was no more like the standard

than it was the comparison. A score of 0.5, therefore, could indicate that the test

compound shares some similarities with both the standard and one (or more) of the

comparison compounds. Alternatively, a score of 0.5 could indicate that the test









compound is completely unlike any of the trained stimuli (standard and comparison) and

the score is obtained because the rat is randomly placing its behavior between the trained

responses.

Results

The generalization profiles for each test compound are shown in Figures 3-6

through 3-13. These figures are used to reveal the proportion of responding to the test

stimulus as compared with the standard stimulus. This format is similar to that used by

Morrison (1967), except that the Generalization Score is plotted on the ordinate instead of

proportion of responses to the standard; the group names are listed along the horizontal

axis. Tables 3-4, 3-6, 3-8, 3-10, 3-12, 3-14, 3-16, and 3-18 list the performance for each

group to individual concentrations of the training stimuli for each test compound. Data

reported in these tables can be used to support the conclusion that rats in this experiment

were reliably able to discriminate between training compounds and that stimulus control

was maintained during the testing period. Each table reflects the data for those particular

sessions that contained the test stimulus. It is noteworthy that these scores were generally

high and the variance was low. Interestingly, the scores for the lowest concentration of

citric acid in the quinine group for many of the test stimuli were lower than the other

concentrations, which implies that the group had more trouble discriminating that

concentration of citric acid from their standard (quinine concentrations). Indeed, results

from studies using electrophysiological and CTA approaches suggest that the signals for

"bitter" and "sour" stimuli may overlap to some extent (e.g., Frank, Contreras, and

Hettinger, 1983; Lemon and Smith, 2005; Nowlis, Frank, and Pfaffmann, 1980), but

clearly the generally high levels of behavioral performance seen here would argue against

that. Besides, similarly poor performance to the lowest sucrose concentration can be seen









during some of the same test weeks, which might suggest a problem with an overall

ability to maintain stimulus control for the weakest solutions in that group.

Novel concentrations: NaCI

Figure 3-6 depicts the untrained responses to the novel concentration of NaC1,

0.847 M. An ANOVA comparing performance in the 4 groups revealed that there was a

significant difference between one or more of the groups (F(3, 43) = 2607.5, p < 0.01).

Subsequent post-hoc analysis with Bonferroni adjustment revealed that the

Generalization Scores for the different groups could be ordered in the following way: N>

S > Q > C. Separate one-sample t-test tests (see Table 3-3) showed that the

Generalization Scores for the N group were actually greater than 1.0, indicating that

novel NaCl is more standard-like than the standard concentrations used to maintain

stimulus control, but the actual value was indeed very close to unity. Conversely, the

Generalization Scores from the S and C groups were significantly less than 0, indicating

that those groups treated the novel NaCl as more comparison-like than their actual

comparison stimuli. Both of these types of findings can probably be explained as

statistical artifacts.

In general, it is fair to say that the N group responded as if novel NaCl was

standard-like and rats in the S, Q, and C groups treated the test compound as if it were

comparison-like; this was expected given that NaCl is one of the comparison compounds

for each of these latter three groups. The overall performance on training stimuli, which

were used to maintain stimulus control during testing sessions, is listed in table 3-4; the

performance values during the sessions with the test compound present are shown.









Novel concentrations: Sucrose

Figure 3-7 depicts the untrained responses to the novel concentration of sucrose,

0.068 M. An ANOVA comparing Generalization Scores obtained for the 4 groups

revealed that there was a significant difference between one or more of the groups (F(3,

43) = 1587.9, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that

the Generalization Scores for the different groups could be ordered in the following way:

S> N > Q > C. Separate one-sample t-test analyses of the Generalization Scores (see

Table 3-5) revealed that in the S group, novel sucrose was not different from the standard

(sucrose) training stimuli and that the N and Q groups were statistically not different from

comparison training stimuli. The C group did, however, treat the novel sucrose as more

comparison-like than their comparison training compounds. Again, this can likely be

explained by statistical artifact. Overall, there is statistical evidence to support the claim

that the novel concentration of sucrose generalizes to sucrose in the S group, and not at

all to the standards for the N, Q, and C groups. Table 3-6 includes the performance data

for all of the animals during this phase of testing.

Novel concentrations: Quinine

Figure 3-8 describes the untrained responses to the novel concentration of quinine,

0.546 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups

revealed that there was a significant difference between one or more of the groups (F(3,

43) = 2329.181, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that

the Generalization Scores for the different groups could be ordered in the following way:

Q> C > N > S. Of interest, separate one-sample t-test tests of the Generalization Scores

(see Table 3-7) showed that the Generalization Scores to novel quinine in the Q group

were not statistically different from their standard stimulus and that performance in the N









and S groups was not different from comparison stimuli. Analysis of the C group

revealed that Generalization Scores were statistically greater than 0, but this difference

did not survive a Bonferroni correction and it was minor in magnitude. Therefore,

performance to the novel concentration of quinine appears to generalize completely to the

trained concentrations of quinine in the Q group and all other groups respond as if the

stimulus were comparison-like. Table 3-8 lists the performance data for all of the

animals during this phase of testing.

Novel concentrations: Citric acid

Figure 3-9 shows the untrained responses to a novel concentration of citric acid,

42.56 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups

revealed that there was a significant difference between one or more of the groups (F(3,

43) = 2734.3, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that

the Generalization Scores for the different groups could be ordered in the following way:

C> N > Q > S. Of interest, separate one-sample t-tests of the Generalization Scores (see

Table 3-9) showed that the novel citric acid test stimulus was statistically more standard-

like for the C group than the training concentrations used, though that effect disappeared

with Bonferroni correction. Also of note is that the S and Q groups responded as if the

novel concentration of citric acid was more comparison-like than the training compounds,

though Bonferroni adjustment resulted in the Q group failing to reach significance. The

N group responded as if the test stimulus was not different from the comparison training

stimuli. Overall, the rats in the C group responded as if the novel concentration of citric

acid were similar to the training concentrations, while the rats in the other groups

responded as if it were a comparison stimulus. Table 3-10 contains the performance to

all concentrations of training stimulus for all of the rats.









Mixtures between NaCI and sucrose: 1.07 M NaCI + 0.421 M sucrose

Figure 3-10 shows the untrained responses to a mixture of 1.07 M NaCl and 0.421

M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups

revealed that there was a significant difference between one or more of the groups (F(3,

43) = 89.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the

Generalization Scores for the different groups could be ordered in the following way: N

S > Q > C. Of interest, separate one-sample t-tests of the Generalization Scores (see

Table 3-11) showed that all of the groups differ statistically from 1.0 (the test compound

is standard-like), and only the C group responds as if the test stimulus is not statistically

different than the comparison stimuli. Consequently, the N and S groups report that the

mixture is also not comparison-like, while the Q group responded as if the mixture was

more comparison-like than the training compounds. The performance in the N and S

groups showed similar levels of responding (ANOVA post hoc between N and S p =

1.000), which suggests that both qualities (NaCl-like and sucrose-like) contributed to the

overall experience of the solution. Table 3-12 contains performance data for the training

stimuli.

Mixtures between NaCI and Sucrose: 1.07 M NaCI + 0.077 M Sucrose

Figure 3-11 shows the untrained responses to a mixture of 1.07 M NaCl and 0.077

M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups

revealed that there was a significant difference between one or more of the groups (F(3,

43) = 1122.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that

the Generalization Scores for the different groups could be ordered in the following way:

N > = C = S =Q. Of interest, separate one-sample t-test analyses of the Generalization

Scores (see Table 3-13) showed that all groups were statistically different from 1.0 (i.e.,









the test stimulus was standard-like) and only the N group differed significantly from 0

(the test stimulus is comparison-like). Taken together, these data indicate that a NaCl-

like taste appears to be the only quality present in the mixture. It would seem that the

relatively strong concentration of NaCl overshadows the relatively weak concentration of

sucrose. Table 3-14 contains data for performance to training stimuli.

Mixtures between NaCI and Sucrose: 0.376 M NaCI + 0.421 M Sucrose

Figure 3-12 shows the untrained responses to a mixture of 0.376 M NaCl and 0.421

M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups

revealed that there was a significant difference between one or more of the groups (F(3,

43) = 78.0, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the

Generalization Scores for the different groups could be ordered in the following way: S >

N > C > Q. Of interest, separate one-sample t-test analyses of the Generalization Scores

(see Table 3-15) showed that all groups differed significantly from 1.0 (i.e., that the test

stimulus was standard-like) and that the S and N groups also differed significantly from 0

(the test stimulus is comparison-like). Both the Q and C groups responded as if the test

compound was comparison-like. The post hoc test of the ANOVA indicated that the S

component was statistically greater than the N component. This suggests that rats can

distribute their behavior according to the relative contribution of each compound that is

present in a mixture. The overall performance to training stimuli, which were used to

maintain stimulus control during testing sessions, is listed in Table 3-16.

Novel test compound: Water

Figure 3-13 shows the untrained responses to water as a test compound. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there

was a significant difference between one or more of the groups (F(3, 43) = 386.4, p <









0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization

Scores for the different groups could be ordered in the following way: Q > C > S > N. Of

interest, separate one-sample t-tests of the Generalization Scores (see Table 3-17) showed

that all groups were statistically different from 1.0 (the test compound is standard-like)

and only the N group was not statistically different from 0 (the test stimulus is

comparison-like). Taken together, these results show that, under these testing conditions,

there is a strong quinine-like component, followed by citric acid-like and sucrose-like

components to water. The significance of this will be addressed in the discussion section,

but briefly it may have occurred because the lowest training concentration of quinine was

only about twice the most conservative measure of detection threshold reported and also

because water might actually possess a weak quinine taste. Performance to all stimulus

control concentrations are shown in Table 3-18.

Retraining water as a comparison stimulus

The results of the phase in which we attempted to retrain the rats include water with

the comparison stimuli are shown in figure 3-14. The results for the overall performance

when all training compounds were present were poor for the water stimulus in the Q

group (data not shown). Because the percentage of the trials in the session that were

water was very low when all training stimuli were presented, we reasoned that to increase

the ability of the rats to specifically learn the discrimination, it was necessary to limit the

types of training stimuli encountered to only water and quinine. Consequently, to

increase the animals overall experience with discriminating water from quinine, only

water and 0.360 mM & 0.827 mM quinine were present in training sessions shown in

Figure 3-14; for reference, the performance during the first day of retraining is included.

Clearly, the rats were unable to perform this discrimination well. Although it can be seen









that their performance to water improved over the course of training, the ability of the

rats to correctly detect the presence of quinine worsened, indicating that the stimuli were

unable to maintain the high levels of stimulus control previously seen with the other

training compounds.

Negative control session

The results for the negative (water) control session are shown in Figure 3-15.

When all of the testing reservoirs which were normally filled with chemical stimuli were

filled instead with water, performance dropped to chance levels for most of the animals.

There were 8 rats that performed significantly worse than chance. If a Bonferroni

correction is applied to control for multiple tests, then the same rats fail to reach

significance. These data support the claim that rats used only chemical cues to guide

their behavior.

Discussion

The rats in this experiment readily learned to discriminate several concentrations of

one prototypical compound representing one of the putative basic taste qualities from

various concentrations of prototypical compounds representing the three remaining taste

qualities. Moreover, the results from test stimuli support the claim that responses to

training stimuli generalized to novel compounds that likely shared similar taste qualities

with the training compounds. The fact that these trials were presented without

consequence allows the assertion that the behavior generalized on the basis of the training

history of the animal. Additionally, other evidence to support that claim is based on the

performance to the novel concentrations of training compounds; the rats performed as if

the novel concentrations were similar to the training compounds.









The novel concentrations of training stimuli were taken from the midpoint of each

of the curves obtained during the brief-access experiment. Because the concentrations

included were within the range of training stimuli, it remains possible that more intense

compounds would not have generalized as well, but given what is known about stimulus

intensity dynamism, it is likely that higher concentrations would be identified

appropriately. Nevertheless, it remains an empirical question which could be addressed

by further experiments.

The data on the mixtures of NaCl and sucrose were insightful. These data showed

that rats do not fully generalize to their standard concentration just because it is present

within the mixture. When the standard compound is included in a solution with another

compound to which the rat has been trained to make a competing response, a

Generalization Score of 0.5 may result. Therefore, depending on the relative

concentrations of the standard and comparison solutions used, the behavior of the rats

seems to reflect which compounds) is/are dominant in the solution. It suggests, then,

that profiles of this type would be helpful in the identification of the components of

complex stimuli (either naturally complex, or through mixtures).

Overall, the data from this novel paradigm suggest that this testing method has the

potential to provide information similar to that obtainable using the conditioned taste

aversion approach with respect to the way rats categorize taste stimuli, presumably on the

basis of their qualitative feature. The most obvious benefit of this procedure over the

CTA approach, however, is that the same test animals can be used repeatedly to report on

essentially an unlimited number of test compounds. The initial training that is required









could be described by some as rather lengthy, but the potential for information return is

quite large, and arguably worthwhile.

This paradigm could serve as a more efficient method of obtaining information

about the taste quality of several compounds. It is fair to state that the procedure has

promise as an alternative or complementary testing protocol to the study of taste quality

in animal models. Clearly, more test compounds should be used to extend previous

findings and to identify similarities between this method and other existing procedures.

While it is feasible that this paradigm would yield different findings (e.g., because of

different-testing parameters), it is also possible that this method would provide

converging lines of evidence for results obtained using the conditioned taste aversion

approach and taste discrimination tasks. Such an outcome would increase the confidence

that these different approaches are tapping into similar principles.

Even if this paradigm, however, resulted in conflicting findings from those

generated with other procedures such as CTA, it still does not undermine the information

that this method could potentially provide. As long as the results are reproducible some

aspect of taste behavior is being measured. Perhaps the unique strengths of this

procedure will be realized with further development. One possible avenue which sets this

approach apart from the CTA method is that extinction of learning is not a factor.

Theoretically, the same compound could be tested weeks apart and the animal would

respond to it in the same manner, provided the training stimuli still maintained stimulus

control. The usefulness of this aspect of the task is that it is compatible with

manipulations of the gustatory system in which subsequent re-testing in the same animal









subjects is required by design. This feature (i.e., the strength of within subject designs

for data interpretation) is a benefit that the CTA approach does not offer.

When water served as a test compound, the profile generated was unexpected. In

the planning stages of the experiment, the wrong concentration of quinine was included

in the proposed training array through an unfortunate typographical error. Because the

lowest training concentration of quinine, 0.027 mM is only about twice the most

conservative measure of detection threshold for quinine reported in the literature (0.012

mM, Koh & Teitelbaum, 1961; 0.005 mM, Thaw & Smith, 1994; 0.003 mM, Shaber,

Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St. John, & Spector), it remains possible

that animals generalized water responses to quinine because, of all the stimuli, quinine

had the weakest of the low concentrations. It is also possible that water might have a

quinine-like taste as has been reported for both humans (Anderson, 1959), and rats

(Bartoshuk, 1977; Morrison, 1967). These two possibilities are not mutually exclusive,

but as the next experiment will suggest, however, the latter explanation seems to have

some merit.










Table 3-1. Training compounds selected from Experiment I.
Compound 1 2 3 4
NaCl (M) 0.107 0.376 0.668 1.07
Sucrose (M) 0.042 0.077 0.148 0.421
Quinine (mM) 0.027 0.131 0.360 0.827
CitricAcid (mM) 2.04 10.4 28.2 64.3


Table 3-2. Experimental groups
Group Standard Comparison Solutions
1) N NaCl Sucrose, Quinine, Citric Acid
2) S Sucrose NaC1, Quinine, Citric Acid
3) Q Quinine NaC1, Sucrose, Citric Acid
4) C Citric Acid NaC1, Sucrose, Quinine


Table 3-3. Results from one-sample t-tests for a novel concentration of NaC1.
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 5.0 < 0.01 < 0.01 228.62 < 0.01 < 0.01
S 10 -148.2 < 0.01 < 0.01 -4.85 < 0.01 < 0.01
Q 11 -67.3 < 0.01 <0.01 -1.77 0.11 0.84
C 11 -93.5 < 0.01 <0.01 -7.12 < 0.01 < 0.01


Table 3-4. Performance to training stimuli during novel NaCl testing
Group
NaCl Sucrose Quinine Citric Acid
Solution Conc. Mean SE Mean SE Mean SE Mean SE
NaCl 0.107 0.107 92.1 1.4 96.0 1.6 88.9 1.9 97.5
(M) 0.376 0.376 97.8 0.7 96.7 1.3 96.0 2.2 98.9
0.668 0.668 97.9 0.6 97.0 0.9 97.9 1.1 97.6
1.07 1.07 97.9 0.6 97.6 1.3 98.1 1.6 97.4
Sucrose 0.042 0.042 98.7 0.9 90.5 2.8 87.6 3.3 94.7
(M) 0.077 0.077 98.0 1.1 96.3 1.1 93.5 1.7 98.9
0.148 0.148 100.0 0.0 96.8 0.7 96.7 2.1 98.2
0.421 0.421 99.0 0.5 98.8 0.8 96.8 1.4 97.6
Quinine 0.027 0.027 96.1 1.3 87.4 3.3 94.8 1.0 79.3
(mM) 0.131 0.131 97.9 0.9 92.4 1.5 94.3 1.2 80.7
0.360 0.360 96.2 0.7 94.4 0.9 94.7 1.0 86.2
0.827 0.827 98.4 0.7 93.1 1.3 95.3 0.6 85.6
Citric 2.04 2.04 98.6 0.8 96.1 1.4 79.8 3.6 89.2
Acid 10.4 10.4 96.3 1.2 97.9 0.8 90.9 1.6 94.4
(mM) 28.2 28.2 97.3 1.1 99.1 0.9 96.9 1.2 97.7
64.3 64.3 98.2 1.0 99.0 0.7 95.6 2.5 99.1










Table 3-5. Results from one-sample t-tests for a novel concentration of sucrose.
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 -112.36 < 0.01 <0.01 0.19 0.85 1.00
S 10 -0.25 0.81 1.00 77.92 < 0.01 < 0.01
Q 11 -54.94 < 0.01 < 0.01 0.51 0.62 1.00
C 11 -146.99 < 0.01 < 0.01 -7.88 < 0.01 < 0.01



Table 3-6. Performance to training stimuli during novel sucrose testing
Group
NaCl Sucrose Quinine Citric Acid
Solution Conc. Mean SE Mean SE Mean SE Mean SE
NaCl 0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7
(M) 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6
0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6
1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7
Sucrose 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2
(M) 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9
0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1
0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7
Quinine 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9
(mM) 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7
0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1
0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6
Citric 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3
Acid 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9
(mM) 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3
64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9


Table 3-7. Results from one-sample t-tests for a novel concentration of quinine
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 -153.10 < 0.01 <0.01 -0.62 0.55 1.00
S 10 -122.15 < 0.01 < 0.01 -1.80 0.10 0.82
Q 11 2.66 0.02 0.16 109.65 < 0.01 < 0.01
C 11 -61.17 < 0.01 <0.01 3.11 0.01 0.08










Table 3-8. Performance to training stimuli during novel quinine testing


Solution Conc.
NaCl 0.107
(M) 0.376
0.668
1.07
Sucrose 0.042
(M) 0.077
0.148
0.421
Quinine 0.027
(mM) 0.131
0.360
0.827
Citric 2.04
Acid 10.4
(mM) 28.2
64.3


NaCl
Mean
92.1
97.8
97.9
97.9
98.7
98.0
100.0
99.0
96.1
97.9
96.2
98.4
98.6
96.3
97.3
98.2


Group
Sucrose Quinine Citric Acid
SE Mean SE Mean SE Mean SE
1.4 96.0 1.6 88.9 1.9 97.5 1.1
0.7 96.7 1.3 96.0 2.2 98.9 0.6
0.6 97.0 0.9 97.9 1.1 97.6 1.0
0.6 97.6 1.3 98.1 1.6 97.4 1.0
0.9 90.5 2.8 87.6 3.3 94.7 1.6
1.1 96.3 1.1 93.5 1.7 98.9 0.6
0.0 96.8 0.7 96.7 2.1 98.2 1.0
0.5 98.8 0.8 96.8 1.4 97.6 0.8
1.3 87.4 3.3 94.8 1.0 79.3 3.6
0.9 92.4 1.5 94.3 1.2 80.7 3.2
0.7 94.4 0.9 94.7 1.0 86.2 2.9
0.7 93.1 1.3 95.3 0.6 85.6 2.6
0.8 96.1 1.4 79.8 3.6 89.2 1.3
1.2 97.9 0.8 90.9 1.6 94.4 1.1
1.1 99.1 0.9 96.9 1.2 97.7 0.4
1.0 99.0 0.7 95.6 2.5 99.1 0.2


Table 3-9. Results from one-sample t-tests for a novel concentration of citric acid
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 -178.99 < 0.01 < 0.01 -0.21 0.84 1.00
S 10 -145.84 < 0.01 < 0.01 -6.11 < 0.01 < 0.01
Q 11 -68.01 < 0.01 < 0.01 -32.85 0.01 0.08
C 11 3.50 0.01 0.08 109.77 < 0.01 < 0.01



Table 3-10. Performance to training stimuli during novel citric acid testing
Group
NaCl Sucrose Quinine Citric Acid
Solution Conc. Mean SE Mean SE Mean SE Mean SE
NaCl 0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7
(M) 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6
0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6
1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7
Sucrose 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2
(M) 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9
0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1
0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7
Quinine 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9
(mM) 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7
0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1
0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6
Citric 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3
Acid 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9
(mM) 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3
64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9










Table 3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 -6.89 < 0.01 < 0.01 12.88 < 0.01 < 0.01
S 10 -5.72 < 0.01 < 0.01 9.47 < 0.01 < 0.01
Q 11 -104.61 < 0.01 < 0.01 -6.29 < 0.01 < 0.01
C 11 -62.61 < 0.01 < 0.01 -0.56 0.59 1.00



Table 3-12. Performance to training stimuli during high NaCl + high sucrose testing
Group


Solution Conc.
NaCl 0.107
(M) 0.376
0.668
1.07
Sucrose 0.042
(M) 0.077
0.148
0.421
Quinine 0.027
(mM) 0.131
0.360
0.827
Citric 2.04
Acid 10.4
(mM) 28.2
64.3


NaCI
Mean
89.9
96.6
97.0
98.8
98.7
99.6
99.3
97.5
98.0
99.0
98.5
97.5
99.0
99.2
99.2
98.6


Sucrose Quinine Citric Acid
SE Mean SE Mean SE Mean SE
2.2 94.6 1.4 78.2 3.2 96.1 1.3
1.4 95.8 1.2 93.6 1.6 95.9 2.6
1.3 96.1 1.1 96.2 2.7 88.2 8.1
0.4 100.0 0.0 98.0 1.0 95.2 1.6
0.5 86.9 1.2 80.0 3.0 89.6 2.7
0.4 94.7 1.8 91.3 2.6 97.7 0.9
0.5 98.4 0.4 97.2 1.2 97.2 1.4
1.2 99.0 0.5 98.0 1.2 97.0 1.5
0.9 88.1 2.5 94.9 1.0 81.5 1.9
0.5 94.7 1.6 95.2 1.0 87.7 2.4
0.6 95.7 1.6 97.1 0.8 89.7 2.3
1.0 99.0 0.5 97.0 0.9 89.3 3.8
0.7 97.3 1.3 80.2 2.5 87.0 1.4
0.5 97.8 0.8 88.7 2.6 94.0 1.1
0.5 98.6 0.8 96.4 0.8 98.4 0.6
1.1 97.8 1.1 97.9 0.6 99.5 0.3


Table 3-13. Results from one-sample t-tests for 1.07 M NaCl + 0.077 M sucrose
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 -4.91 < 0.01 < 0.01 98.04 < 0.01 < 0.01
S 10 -110.81 < 0.01 < 0.01 0.48 0.63 1.00
Q 11 -63.78 < 0.01 <0.01 -0.68 0.51 1.00
C 11 -51.85 < 0.01 <0.01 -0.95 0.36 1.00










Table 3-14. Performance to training stimuli during high NaCl + low sucrose testing


Solution Conc.
NaCl 0.107
(M) 0.376
0.668
1.07
Sucrose 0.042
(M) 0.077
0.148
0.421
Quinine 0.027
(mM) 0.131
0.360
0.827
Citric 2.04
Acid 10.4
(mM) 28.2
64.3


NaCl
Mean
94.6
97.4
98.6
98.6
99.7
100.0
98.6
99.4
99.0
99.1
98.9
98.0
99.7
99.6
99.0
99.0


Group
Sucrose Quinine
SE Mean SE Mean SE
1.6 95.9 1.3 89.0 2.6
1.0 98.4 0.7 98.1 1.0
0.4 98.3 0.9 99.7 0.3
0.3 98.2 0.8 97.7 0.6
0.3 92.2 1.9 87.2 2.3
0.0 97.9 0.8 95.1 1.6
0.8 97.7 1.3 99.1 0.5
0.4 98.5 1.0 97.3 1.1
0.5 87.5 3.9 93.7 1.3
0.5 94.2 1.0 96.8 0.5
0.6 94.3 1.4 97.5 0.5
0.8 96.0 2.1 96.1 0.8
0.3 94.3 1.7 71.9 2.6
0.4 96.8 1.2 89.8 1.8
0.7 98.3 1.0 97.2 1.4
0.5 99.7 0.3 98.8 0.7


Citric Acid
Mean SE
95.2 1.9
98.6 0.5
98.8 0.7
98.0 0.8
96.5 2.0
98.1 0.7
97.4 1.1
96.4 1.2
79.7 4.0
84.8 3.3
90.1 2.5
92.3 1.8
88.9 1.6
94.3 0.9
98.3 0.5
99.1 0.3


Table 3-15. Results from one-sample t-tests for 0.376 M NaCl + 0.421 M sucrose
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 -17.58 < 0.01 <0.01 15.07 < 0.01 < 0.01
S 10 -4.43 < 0.01 < 0.01 14.87 < 0.01 < 0.01
Q 11 -51.13 < 0.01 <0.01 -1.17 0.27 1.00
C 11 -18.29 < 0.01 <0.01 3.18 0.01 0.08










Table 3-16. Performance to training stimuli during low NaCl + high sucrose testing


Solution Conc.
NaCl 0.107
(M) 0.376
0.668
1.07
Sucrose 0.042
(M) 0.077
0.148
0.421
Quinine 0.027
(mM) 0.131
0.360
0.827
Citric 2.04
Acid 10.4
(mM) 28.2
64.3


NaCl
Mean
92.2
99.2
98.9
98.7
98.1
99.7
99.6
99.6
97.5
97.5
98.9
98.3
98.3
98.9
99.7
97.7


Group
Sucrose Quinine Citric Acid
SE Mean SE Mean SE Mean SE
1.5 95.0 1.5 86.2 2.2 98.1 0.6
0.3 98.5 0.6 98.6 0.6 98.7 0.5
0.5 99.3 0.5 98.1 1.1 96.7 1.6
0.4 99.2 0.5 90.6 8.3 97.6 0.7
0.6 88.9 1.9 81.7 3.4 91.2 2.3
0.3 93.0 1.7 87.0 2.2 93.3 3.1
0.4 98.7 0.7 98.0 1.0 97.6 1.0
0.4 98.9 0.5 97.8 0.8 98.9 0.6
1.2 87.5 2.0 93.1 1.0 78.8 3.4
1.2 95.1 1.6 95.1 1.0 85.0 3.5
0.6 96.8 1.2 88.4 8.0 91.4 1.9
0.6 98.4 0.7 96.3 0.8 90.1 2.3
0.8 93.9 1.4 57.3 4.2 79.3 2.5
0.8 98.7 0.7 81.4 7.2 91.8 1.4
0.3 99.5 0.5 99.3 0.7 99.0 0.4
1.0 98.5 0.9 97.1 1.0 99.4 0.3


Table 3-17. Results from separate one-sample t-tests for water
Test against 1.0 Test against 0
p- Adjusted Adjusted
Grp df t value p-value t p-value p-value
N 11 -130.33 < 0.01 < 0.01 3.01 0.01 0.08
S 10 -30.60 < 0.01 < 0.01 7.19 < 0.01 < 0.01
Q 11 -4.73 < 0.01 < 0.01 90.67 < 0.01 < 0.01
C 11 -24.67 < 0.01 < 0.01 7.880 < 0.01 < 0.01



Table 3-18. Performance to training stimuli during water testing
Group
NaCl Sucrose Quinine Citric Acid
Solution Conc. Mean SE Mean SE Mean SE Mean SE
NaCl 0.107 97.3 0.6 99.2 0.8 94.5 1.0 97.9 0.8
(M) 0.376 98.3 0.5 98.9 0.6 98.6 0.6 98.4 0.8
0.668 98.7 0.4 96.7 0.9 99.2 0.5 98.5 0.6
1.07 99.0 0.3 98.3 0.9 99.3 0.5 99.7 0.3
Sucrose 0.042 96.7 1.0 90.7 2.4 87.3 2.5 94.2 1.9
(M) 0.077 98.3 1.4 95.2 1.3 96.7 1.1 98.0 1.0
0.148 99.2 0.5 98.4 0.4 98.4 0.7 97.9 1.2
0.421 99.3 0.5 98.1 0.8 97.4 0.8 96.9 1.2
Quinine 0.027 97.2 1.6 89.1 2.7 92.3 1.2 81.6 2.2
(mM) 0.131 96.9 1.2 94.0 2.3 94.7 0.9 88.7 2.5
0.360 97.5 1.0 92.4 2.3 94.3 1.2 91.0 2.6
0.827 98.0 0.7 97.1 1.1 94.0 1.2 90.1 1.6
Citric 2.04 98.3 0.7 97.2 1.1 90.9 1.6 93.4 1.0
Acid 10.4 97.4 1.1 98.8 0.8 95.8 1.1 93.9 1.6
(mM) 28.2 97.2 1.1 99.2 0.5 92.8 1.6 93.4 1.2
64.3 98.6 0.6 99.5 0.5 97.9 1.0 98.6 0.6









1.0 -
----------------- Na CI
NaCI
0.8 ---- ------- --

0.6 -----------------------
II

0.4 ---------------- ----- -

0.2 -

0 .0 ... . .. .
0.01 0.1 1
Concentration (M)

Figure 3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test. Rats
monotonically decreased licking as concentration increased.


1.0

0.8

0.6

0.4

0.2


0.0 -
0.001


0.01 0.1
Concentration


(M)


Figure 3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test. Rats
monotonically increased licking as concentration increased.










1.0

1. Quinine
0.8 ------- ----
I0I

0.6 -------------
I

0.4 -------------- --- --


0.2 -


0.0 I


0.001


0.01 0.1 1
Concentration (mM)


Figure 3-3. Mean (n=8) unconditioned licking to quinine in a brief access test. Rats
monotonically decreased licking as concentration increased.


1.0


0.8 -


0.6 -


0.4 -


0.2 -


0.0


0.01


.1 1 10 100
Concentration (mM)


1000


Figure 3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test. Rats
monotonically decreased licking as concentration increased.


Citric Acid
----------------- L-


--------------------


------------------ ----

































Figure 3-5. An overview of the trial structure.










0.847 M NaC1


1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2


N S C
N S Q C


Groups

Figure 3-6. The generalization profile obtained when 0.847 M NaCl was used as a test
compound. The novel concentration generalized to the trained standard.

0.068 M Sucrose
1.2
1.0 ---------------------------
0.8
0.6
0.4
0.2
0.0
-0.2
N S Q C
Groups

Figure 3-7. The generalization profile obtained when 0.068 M sucrose was used as a test
compound. The novel concentration generalized to the trained standard.








0.546 mM Quinine


1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2


N S Q C


Groups

Figure 3-8. The generalization profile obtained when 0.546 mM quinine was used as a
test compound. The novel concentration generalized to the trained standard.

42.56 mM Citric Acid
1.2
1.0------- ---------
0.8
0.6
0.4
0.2
0.0
-0.2
N S Q C
Groups

Figure 3-9. The generalization profile obtained when 42.56 mM citric acid was used as a
test compound. The novel concentration generalized to the trained standard.


-I---


iI









1.07 M NaCI
+ 0.421 M Sucrose



--------------------------------------


- IM


N S Q C
Groups


Figure 3-10. The generalization profile
was used as a test stimulus.
sucrose-like.


1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2


obtained when 1.07 M NaCl + 0.421 M sucrose
The profile obtained was equally NaCl- and


1.07 M NaCI
+ 0.077 M Sucrose

------ ----------------------------


N S Q C
Groups

Figure 3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose
was used as a test stimulus. The profile obtained was NaCl-like.


1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2








0.376
+ 0.421


1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2


M NaCI
M Sucrose


N S Q C
Groups

Figure 3-12. The generalization profile obtained when 0.376 M NaC1 + 0.421 M sucrose
was used as a test stimulus. The profile obtained was more sucrose-like than
NaCl-like, but there was no statistical evidence for quinine-like or citric acid-
like components.

Water


1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2


N S Q C


Groups


Figure 3-13. The generalization profile obtained when water was used as a test stimulus.
The profile obtained was predominantly quinine-like, indicating that either
water has a quinine-like taste quality and/or because, of all of the stimuli,
quinine had the weakest of the low concentrations.


---------------------------------------
*


I--













Retraining 0.360 mM & 0.827 mM Concentrations
of Quinine Versus Water


--Quinine
- Water


14 15 16 17 18 19 20 21 22 23 24 25 26 27
Days


Figure 3-14. Performance of the Q group during retraining for discrimination of water
from the two mid-range concentrations of quinine.
100
Overall Performance During Negative Control Test

90


80


70


50 -


(- CO c ^r i D ^ 00 o (N CO co r CON- (D 0) 0 (N 0) o r CON-) 0 N 0) co r CO N- 0) 0 (NO CM m CO N-
Rat Number

Figure 3-15. Performance on water control test. Individual performance scores for all
rats indicate that taste did not serve as a cue to guide behavior.


5 0.5 -..

0.4

0.3

0.2

0.1














CHAPTER 4
APPLICATION OF A NEW BEHAVIORAL PARADIGM TO ASSESS TASTE
QUALITY GENERALIZATION

Introduction

Chapter 3 explored whether rats would be able to perform a task in which they

were required to discriminate one prototypical taste compound, thought to be

representative of one of the putative four basic taste qualities, from the other three

prototypical taste compounds. Furthermore, we wanted to determine whether we could

use the untrained responses of the animals, when presented with novel taste compounds,

to generate profiles which would indicate how NaCl-like, sucrose-like, quinine-like, and

citric acid-like each novel test compound is. A few questions and confounds were not

addressed in that particular paradigm. Therefore, the following experiment was modified

by 1) increasing the lowest concentration of quinine from 0.027 mM to 0.083 mM, and 2)

including a water (W) group specifically trained to discriminate the four prototypical

stimuli (comparison stimuli) from their water standard in an attempt to overcome the

pitfalls encountered in Experiment II of Chapter 3. Additionally, the current experiment

was also designed to extend the findings of the previous chapter by including test

compounds that were completely novel to the rats.

Method

Subjects

Thirty naive adult male Sprague-Dawley (Charles River Breeders; Wilmington,

MA) rats served as subjects. The rats were housed individually in polycarbonate shoe-









box style cages in a room where temperature, humidity, and light cycle (lights on 7am -

7pm) were controlled automatically. All manipulations were performed during the light

phase. The rats had ad libitum access to Purina Rat Chow (5001) in the home cage.

Purified (Elix 10; Millipore, Billerica, MA) water was also available, but was removed

approximately 16 hours before (4:00 pm the day before) the first behavioral session of the

week and was replaced at the completion of the last session of the week.

Apparatus

The apparatus was the same as that described in Experiment II of Chapter 3.

Task Overview

The prototypical taste compounds, NaC1, sucrose, quinine HC1, and citric acid,

were used to represent the putative 4 basic tastes, salty, sweet, bitter, and sour,

respectively. Five groups of rats were trained in a manner similar to that in Chapter 3.

Briefly, they were trained to respond by licking one response spout after sampling any of

the 4 training concentrations of a particular standard, which for each group was one of the

prototypical compounds or water, and they were trained to lick a different response spout

after tasting any of the comparison stimuli, which included the remaining compounds

(see Table 4-1).

Stimuli

The same concentrations of each of the prototypical compounds were used during

training and were based on the results of Experiment I in Chapter 3. All solutions were

prepared daily with purified water (Elix 10, Millipore, Billerica, MA) and reagent grade

chemicals, and were presented at room temperature. Test stimuli consisted of four

concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M; Fisher Scientific,

Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M; Fisher Scientific,









Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM; Fisher Scientific,

Atlanta, GA), quinine (0.083 mM, 0.131 mM, 0.360 mM, and 0.827 mM; Sigma-Aldrich,

St Louis, MO) and purified water. Note the originally intended lowest concentration of

quinine, 0.083 mM, was included in this experiment.

Trial Structure

On any given trial (see flow chart), rats were trained to lick a centrally positioned

stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two

times with an interlick interval < 250 ms, then the shaft of the spout was filled with the

stimulus solution, after which the rat could sample up to 5 licks (-5[l was deposited into

the fluid column upon each lick) before the spout was rotated out of position. Next, a

decision phase was initiated, during which the rat was required to lick one response spout

after tasting the standard stimulus or the other response spout after tasting a comparison

stimulus. During the consequence phase, if the rat responded correctly to the stimulus,

water reinforcement was delivered directly through the response spout (20 licks @ -5[ l

per lick or a total of 10 s access, whichever occurred first). If the rat failed to respond, or

responded on the incorrect response spout, then the rat was punished with a 20-s timeout.

After either consequence of the decision phase, the trial moved into an inter-trial interval

that lasted 6 s. See Figure 3-5 in Chapter 3 for an overview of the trial structure.

Training

Table 4-2 contains the training parameters associated with this experiment. The

inclusion of water as a comparison had to be abandoned in order to proceed with training,

but the water group was maintained, albeit on a different training schedule than the other

4 groups (see Table 4-3).









Spout training

This phase was the same as that described in Experiment II Chapter 3. The rats had

access to only one spout (either the sample spout, the left response spout, or the right

response spout) and each spout was connected to a reservoir that contained water. The

point of this phase was to train the rats to approach and gain familiarity with getting fluid

from each of the spouts. Eventually, the sample spout would contain a taste stimulus and

the response spouts would only contain water. The rats had to learn to lick from the

sample spout and then select one of the response spouts by licking it. If the rat responded

correctly, then water reinforcement was available (10 s access or 20 licks, whichever

came first). The inter-trial interval during this phase was 6 s.

Side training

This phase was the same as that described in Experiment II Chapter 3. Briefly,

only one trial type was presented within a given session during side training. If the rats

were trained with their standard in the first session, then during the next session, the rats

received only comparison trials. After sampling, rats had 180 s (limited hold period)

during which they were required to respond. Side training lasted a total of 4 days. Only

the third highest concentration of each stimulus was presented.

Alternation

This phase was the same as that described in Experiment II Chapter 3. Briefly,

during alternation training, the rats started out with either a standard or one of the

comparison stimuli. Upon completion of a set criterion of correct responses, the program

switched to the opposite trial type. The criterion was set at 6 the first day, 4 the second

day, and 2 the third day of alternation training. Each time the rat completed the criterion

of correct responses, the program automatically switched to delivery of the other trial









type. The correct responses did not have to be consecutive. The limited hold was

changed from 180 s to 15 s. During the decision phase, if a rat failed to make any

response, or made the incorrect response, a 10-s timeout was initiated.

Discrimination training I-III

Trials were delivered in a block with a random pattern selected by the computer

program. Therefore, the rats had no indication from the prior trial, which solutions would

be offered on the current trial. All 4 training concentrations were used in this phase, but

because the gustometer had a limited number of fluid reservoirs, only two concentrations

(always one of the highest two and one of the lowest two) of each prototypical compound

were included per session. The block size was 16 to accommodate the water standard;

consequently, every standard concentration for a given session was repeated three times

within the block so that the number of standard stimuli matched the number of

comparison stimuli available (which were each only presented once per block). The

timeout period was increased to 20 s during this phase. The training schedules differed

for the W group and the N, S, Q, & C groups at this point.

Once performance reached an asymptote for all animals in the N, S, Q, and C

groups (85% or better two consecutive days), a partial schedule of reinforcement was

introduced. During the session, 2 trials (one standard and one comparison) from each

block of 12 trials were randomly selected to have neither reinforcement nor punishment

delivered contingent on the animal's response. That is, the animal did not receive

reinforcement if it made the correct response but it also did not receive punishment if it

made the incorrect response. There was, however, a punishment contingency if the rat

failed to make a response. There was no correct response associated with a test stimulus,









so the animal would not receive reinforcement, but it also did not receive punishment for

a response, unless it failed to make the response before the limited hold (5 s) timed out.

Test Compounds

In order to extend the results from the last experiment, only novel taste compounds

were tested. The following novel compounds served as test stimuli:

* 0.376 M sodium gluconate

* 0.668 M sodium gluconate

* 0.131 mM denatonium

* 0.360 mM denatonium

* 0.077 M maltose

* 0.148 M maltose

* 0.376 M KC1

* 0.668 M KC1

* 0.077 M MSG

* 0.148 MMSG

* 0.077 M fructose

* 0.148 M fructose

Data Analysis

The same calculation and interpretation for the Generalization Score was used as

described in Experiment II of Chapter 3. One-way analyses of variance (ANOVAs) were

conducted for each test stimulus to determine the presence of differences among groups

followed by detailed Bonferroni-adjusted paired comparisons. Separate one-sample t-

tests testing group means against both of the null hypotheses 1.0 (the test compound was

similar to the standard stimuli) and 0 (the test compound was similar to the comparison









stimuli) were performed. The conventional p < 0.05 value was used as the statistical

rejection criteria.

Data for the negative control test were analyzed using a one-sample Binomial

analysis with null hypothesis = 0.5, which corresponds with the chance level of

performance.

Results

Results for groups N, S, Q, and C for the two concentrations of each of the 6 test

compounds can be seen in Figures 4-1 through 4-10.

Test Stimulus: Sodium Gluconate

0.376 M sodium gluconate

Figure 4-1 shows the behavioral profile obtained for 0.376 M sodium gluconate.

An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that

there was a significant difference between one or more of the groups (F(3, 20) = 68.7, p <

0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization

Scores for the different groups could be ordered in the following way: N > S = Q = C.

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-4) showed

that all groups were statistically different than 1.0. In addition, the N and S groups were

statistically different than 0. These results show that although the N group did not treat

0.376 M sodium gluconate exactly like a standard stimulus, the profile was still

predominantly NaCl-like. In addition, Bonferroni post hoc comparisons of the

Generalization Score revealed that S, Q, and C groups did not differ from each other,

while the N group differed from all three.









0.668 M sodium gluconate

Figure 4-2 shows the behavioral profile obtained for 0.668 M sodium gluconate.

An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that

there was a significant difference between one or more of the groups (F(3, 20) = 44.4, p <

0.01). A post-hoc analysis with Bonferroni adjustment indicated that the Generalization

Scores for the different groups could be ordered in the following way: N > Q = S = C.

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-5) showed

that all groups were statistically different than 1.0, although with a Bonferroni adjustment

for multiple t-tests, the N group failed to reach statistical significance. Thus, statistical

evidence exists to support the claim that the N group was standard-like. After Bonferroni

correction was applied to the results from the t-test aimed at discerning which groups

differed statistically from 0 (that the test stimulus was comparison-like), the analysis

revealed that only the N and Q groups differed from 0. Thus, there is a predominant

NaCl-like component in 0.668 M sodium gluconate and possibly also a slight quinine-

like component. Performance to the training stimuli used during testing for 0.376 M and

0.668 M sodium gluconate is shown in Table 4-6.

Test Stimulus: Denatonium

0.131 mM denatonium

Figure 4-3 shows the behavioral profile obtained for 0.131 mM denatonium. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there

was a significant difference between one or more of the groups (F(3, 20) = 508.9, p <

0.01). A post-hoc analysis with Bonferroni adjustment showed that the Generalization

Scores for the different groups could be ordered in the following way: Q > S > C = N.

Separate one-sample t-tests of the Generalization Scores (see Table 4-7) determined that









all groups except the Q group differed significantly from 1.0 (the test compound was

standard-like). Thus, denatonium is statistically not different than the training

concentrations of quinine. On the other hand, the t-test comparing the Generalization

Scores to 0 revealed that both the Q and S groups were statistically greater than 0,

indicating that 0.131 mM denatonium is treated behaviorally as predominantly quinine-

like, and very slightly sucrose-like.

0.360 mM denatonium

Figure 4-4 shows the behavioral profile obtained for 0.360 mM denatonium. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there

was a significant difference between one or more of the groups (F(3, 20) = 258.1, p <

0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization

Scores for the different groups could be ordered in the following way: Q > C = N = S.

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-8) revealed

that the Q group is not statistically different than 1.0 (i.e., the test compound is standard-

like), while all of the other groups are different. Furthermore, only the Q group is

statistically different than 0, indicating that the N, S, and C groups had performance that

was comparison-like. This test compound, 0.360 mM denatonium, was clearly quinine-

like. Performance to the stimulus control concentrations for both 0.131 mM and 0.360

mM denatonium is shown in Table 4-9.

Test Stimulus: Maltose

0.077 M maltose

Figure 4-5 shows the behavioral profile obtained for 0.077 M maltose. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there

was a significant difference between one or more of the groups (F(3, 20) = 25.7, p <









0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization

Scores for the different groups could be ordered in the following way: Q > S > C = N.

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-10)

revealed that all of the groups differed significantly from 1.0 but that only the S and Q

groups differed from 0. This indicates that there was both a sucrose-like and quinine-like

component to the maltose. Since the post hoc analyses of the ANOVA revealed that Q >

S, it can be concluded that there is a stronger Q component to the compound than an S

component. It should be stated again that the Generalization Score does not reflect the

intensity of the taste quality, but it is an indicator of how similar the test compound is to

the standard stimulus concentrations.

0.148 M maltose

Figure 4-6 shows the behavioral profile obtained for 0.077 M maltose. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there

was a significant difference between one or more of the groups (F(3, 20) = 28.9, p <

0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization

Scores for the different groups could be ordered in the following way: S = Q > C > N.

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-11)

revealed that all of groups differed statistically from a hypothesized mean of 1.0.

Additionally, the S and Q groups also differed significantly from a hypothesized mean of

0. It is interesting that the post hoc analysis of the ANOVA showed no differences

between the S and Q group means. These results, taken together, indicate that there is an

equal sucrose-like and quinine-like component arising from 0.148 M maltose. These data

might reveal the basis of taste cues which allow discrimination of maltose and sucrose in









rats. Performance to the training stimuli for both concentrations of maltose can be seen

in Table 4-12.

Test Stimulus: Potassium Chloride (KCI)

0.376 M KCI

Figure 4-7 shows the behavioral profile obtained for 0.376 M KC1. An ANOVA

comparing Generalization Scores obtained from the 4 groups revealed that there was a

significant difference between one or more of the groups (F(3, 20) = 6.2, p < 0.01). A

post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for

the different groups could be ordered in the following way: Q > C = N = S (also Q>N=S).

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-13) show

that all groups are statistically different than 1.0 (standard-like). After Bonferroni

adjustment for multiple comparisons, only the performance of the Q and N groups

differed from 0 (comparison-like). Collectively, these data indicate that while KC1 is

predominantly quinine-like there is also a NaCl-like component. The profile is that of a

complex taste, with two qualities contributing at least some portion to the overall

experience

0.668 M KCI

Figure 4-8 shows the behavioral profile obtained for 0.668 M KC1. An ANOVA

comparing Generalization Scores obtained from the 4 groups revealed that there was a

significant difference between one or more of the groups (F(3, 20) = 5.4, p < 0.01). A

post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for

the different groups could be ordered in the following way: Q > C = N = S. Separate

one-sample t-test analyses of the Generalization Scores (see Table 4-14) show that

performance in all groups was statistically different than it was for their respective