Visual Feedback and Bimanual Force Coordination

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Visual Feedback and Bimanual Force Coordination
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english
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Bhullar, Amitoj S
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University of Florida
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Master's ( M.S.)
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University of Florida
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Applied Physiology and Kinesiology
Committee Chair:
Cauraugh, James H
Committee Members:
Tillman, Mark D
Christou, Evangelos A

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visualfeedback
Applied Physiology and Kinesiology -- Dissertations, Academic -- UF
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Applied Physiology and Kinesiology thesis, M.S.
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Abstract:
Bilateral tasks requiring asymmetric force production are more complex that symmetric force production tasks because they require error compensatory negative coordination. The purpose of the present research was to investigate a complex bilateral task while manipulating visual angles and task coefficient conditions. The manipulated visual angles were 1o, 10o, 32o and 59 o and the task coefficients involved placing equal coefficients on each hand (i.e., control) and unequal coefficients on each hand (i.e., left greater than right and right greater than left). Fifteen right handed volunteers between the ages of 20 to 35 years participated in the study. The results revealed reduced task error, decreased variability and stronger negative coordination at an increased visual feedback in each task coefficient condition. Even though, the RMSE was reduced at a visual angle of 10o (i.e., visual gain of 80 pixels/N), reduction in bilateral force variability and a strong negative coordination between hands was seen at the visual angle of 32o (i.e., visual gain of 256 pixels/N). In sum, this study achieved an important purpose of identifying visual feedback gains that can be used to improve motor performance during a complex bilateral task. These visual feedback conditions can be used for retraining bilateral tasks in patients with neurological impairments.
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In the series University of Florida Digital Collections.
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Includes vita.
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by Amitoj S Bhullar.
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Thesis (M.S.)--University of Florida, 2013.
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Adviser: Cauraugh, James H.
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1 VISUAL FEEDBACK AND BIMANUAL FORCE COORDINATION By AMITOJ BHULLAR A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIV ERSITY OF FLORIDA 2013

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2 2013 Amitoj Bhullar

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3 I dedicate this work to my parents and friends who have been extremely supportive and have helped me throughout the course of my study.

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4 ACKNOWLEDGMENTS The re are several individuals who have contributed their time, effort and support for the completion of this thesis First, I would like to thank my advisor, Dr. James Cauraugh for directing my focus, providing input, feedback and support. Additionally, I wo u ld like to thank Drs. Mark Tillman and Evangelos Christou for their valuable input, suggestions and support. A special note of gratitude goes to Ny eo nju Kang my lab mate who tirelessly worked on my program for data collection and provided his valuable sug gestions and help whenever I needed the most I would also like to thank my lab mate Jerelyne Idica for being supportive and helping me in data collection and analysis

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ............................. 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 11 Research Question ................................ ................................ ................................ 17 Hypothesis ................................ ................................ ................................ .............. 18 Significance ................................ ................................ ................................ ............ 18 2 METHODS ................................ ................................ ................................ .............. 20 Participants ................................ ................................ ................................ ............. 20 Apparatus ................................ ................................ ................................ ............... 20 Procedures ................................ ................................ ................................ ............. 21 Instructions ................................ ................................ ................................ ....... 21 M aximal Voluntary Contractions (MVC) Estimation ................................ .......... 21 Experimental task ................................ ................................ ............................. 21 Data analysis ................................ ................................ ................................ .......... 23 3 RESULTS ................................ ................................ ................................ ............... 25 Task Performance ................................ ................................ ................................ .. 25 Performance error ................................ ................................ ............................ 25 Force Variability ................................ ................................ ................................ 25 Bimanual Force Coordination ................................ ................................ ................. 27 Structure of Force Variability ................................ ................................ ................... 28 4 DISCUSSION ................................ ................................ ................................ ......... 34 Task performance ................................ ................................ ................................ ... 34 Performance Error ................................ ................................ ............................ 34 Force Variability ................................ ................................ ................................ 35 Bimanual Coordination ................................ ................................ ............................ 36 Structure of Force Variability ................................ ................................ ................... 37 Implications and Future Directions ................................ ................................ .......... 38

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6 LIST OF REFERENCES ................................ ................................ ............................... 42 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 46

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7 LIST OF TABLES Table page 3 1 Correlation Coefficient means with standard error () for all conditions ............. 29

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8 LIST O F FIGURES Figure page 2 1 Apparatus and finger/hand position ................................ ................................ .... 24 2 2 Different visual feedback display (A) 1 o (B) 10 o (C) 32 o (D) 59 o .......................... 24 3 1 For bilateral force, mean root mean square error ( SE) as a function of interaction between visual feedback and task coefficients. ................................ 29 3 2 For bilateral force, mean coefficient of variation ( SE) as a function of visual feedback main effect. ................................ ................................ ......................... 30 3 3 For bilateral force, mean coefficient of varia tion ( SE) as a function of task coefficient main effect ................................ ................................ ......................... 30 3 4 For individual forces, mean coefficient of variation ( SE) as a function of task coefficient main effect. ................................ ................................ ................ 31 3 5 For bilateral force, mean standard deviation ( SE) as a function of visual feedback main effect. ................................ ................................ ......................... 31 3 6 For bilateral force, mean standard d eviation ( SE) as a function of task coefficient main effect. ................................ ................................ ........................ 32 3 7 For individual forces, mean standard deviation ( SE) as a function of task coefficient main effect. ................................ ................................ ........................ 32 3 8 For bilateral force, mean approximate entropy ( SE) as a function of task coefficient main effect. ................................ ................................ ........................ 33

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9 LIST OF ABBREVIATIONS ApEn approximate entropy CV coefficient of var iation RMSE root mean square error SD standard deviation

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements For The Degree Of Master Of Science VISUAL FEEDBACK AND BIMANUAL FORCE COORDINATION By A mitoj B hullar A ugust 2013 Chair: James H. Cauraugh Major: Applied Physiology and Kinesiology Bilateral tasks requiring asymmetric force production are more complex that symmetric force production tasks because they r equire error compensatory negative coordination. The purpose of the present research was to investigate a complex bilateral task while manipulating visual angles and task coefficient conditions. The manipulated v isual angles were 1 o 10 o 32 o and 59 o and the task coefficients involved placing equal coefficients on each hand (i.e., co ntrol) and unequal coefficients on each hand (i.e., left greater than right and right greater than left) Fifteen right handed volunteers between the ages of 20 to 35 years par ticipated in the study. The results revealed reduced task error, decreased variability a nd stronger negative coordination at an increased visual feedback in each task coefficient condition Even though, the RMSE was reduced at a visual angle of 10 o (i.e., visual gain of 80 pixels/N ) reduction in bilatera l force variability and a strong negative coordination between hands was seen at the visual angle of 32 o (i.e., visual gain of 256 pixels/N ) In sum, this study achieved an important purpose of identifying visual feedback gains that can be used to improve motor performance during a complex bilateral task. These visual feedback conditions can be used for retraining bilateral tasks in patients with neurological impairments.

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11 CHAPTER 1 INTRODUCTION Everyday t asks require motor actions. Depending on the task goal, several motor solutions are available. Although a limited number of solutions are sufficient, there are infinite motor solutions available to achieve task goals (Bernstein, 1967) These motor solutions are related to the presence of more degrees of freedom than necessary for task performance (Gelfand & Latash, 1998; Latash et al., 2010) For example, most of the upper extremity activities involve positioning or orienting the hand in space which minimally requires between 3 and 6 degrees of freedom. However, there are about 10 degrees of freedom in the arm alone and even more if we allow upper body movements. A consequence of additional degrees of freedom, the sensorimotor system is continually addressing the problem of choosing from various solutions (Latash, Kang, & Patterson, 2002 ; Latash & Turvey, 1996) Over the years, researchers have argued that the motor solution is chosen on the basis of optimization of the cost function (e.g., minimum jerk, minimum torqu e change, minimum effort, and minimum discomfort; Cruse & Brwer, 1987; Flash & Hogan, 1985; Hasan, 1986; Latash, 2012; Nelson, 1983; Prilutsky & Zatsiorsky, 2002; Rosenbaum et al., 2001; Uno et al., 1989) Even though more motor solutions than required creates motor redundancy in the system (Bernstein, 1967) the criteria used by the sensorimotor system to choose one particular solution from a set of possibilit ies remain largely unexplained. Recently, the traditional view that more degrees of freedom create motor redundancy and need to be eliminated has been challenged (Bernstein, 1967; Latash, 2012; Latash, Kang, & Patterson, 2002 ). The alternate view on redundant design holds

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12 that the additional degrees of freedom offer motor abundance needed for flexib ility in actions which is a characteristic of voluntary movements (Latash, 2000; Latash, 2012) According to the principle of motor abundance, the central nervous system (CNS) imposes time varying constraints on the system thus allowing solutions to emerge (Hu & Newell, 2011; Latash, 2012) Typically, the multiple solutions that emerge are equally able to solve the task and achieve movement goals. The support for motor abundance is partially derived from a classic study by Bernstein (1967) on professional blacksmiths. These highly trained individuals represented optimal solutions for striking their hamm er on a chisel. Bernstein discovered that instead of using the same strategy each time, the blacksmiths achieved the task goal using multiple solutions over time. Over the years, the concept of multiple solutions was extended by showing the presence of re dundancy in motor actions even after considerable practice (Jaric & Latash, 1999; Latash, 2012; Latash, Scholz, & Schner, 2002; Latash, 2008; Yang & Scholz, 2005) Nonetheless, motor abundance or redundancy allows variability in the system which in turn may interfere with the successful acco mplishment of task goals (Churchland Afshar, & Shenoy, 2006; Hu & Newell, 2011a; Jones, Hamilton, & Wolpert, 2002) However, variability does not always present a threat to successful task performanc e and is considered good or bad depending upon whether the stage of occurrence is during movement execution or task outcome consistent with an end effector position. This notion of good and bad variability is explained in a phenomenon called uncontrolled m anifold hypothesis (Latash, 2012; Latash, Scholz, & Schner, 2007; Martin, Scholz, & Schner, 2009; Scholz & Schner, 1999)

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13 Uncontrolled Manifold Hypothesis The principle of uncontrolled manifold (UCM) hypothesis assumes that the controller of a multi element system attempts to stabilize a particular value of a performance variable by defining a subset termed uncontrolled manifold (Latash, 2012; Latash, Kang, & Patterson, 2002) The variability of t he elements within UCM is not controlled or stabilized thus allowing more freedom to the elemental variables. For example, all the combinations of joint angles that can achieve one end effector position are in this elemental subset. The variability that ta kes place within the UCM is termed good variability. On the other hand, the variability that takes place orthogonal to UCM is termed bad variability and is controlled or restricted. Good variability is consistent with the movement execution stage of task performance, and facilitates adaptability as well as error compensation (Bernstein, 1967; Hu & Newell, 2011a; Latash, 2012; Latash, Scholz, & Schner, 2007) Furthermore, good variability is attributed to the emergence of several motor solutions (Latash, 2012; Todorov & Jordan, 2002) On the other hand, bad variability is experienced at the end effector position or outcome stage and is directly responsible for the deterioration in task performance. Ideall y, bad variability is lower than good variability (Latash et al., 2010) However, complex ta sks pose a challenge to individuals and frequently lead to higher levels of variability at the task outcome level (Hu & Newell, 2011a; Ranganathan & Newell, 20 08a) Many researchers consider bimanual movements as complex tasks. Constraints on bimanual motor actions In a classic study, Newell (1986) proposed that the optimal coordination patterns during bilateral activities emerge from an interaction among va rious categories of constraints (pp. 341 360). Constraints on

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14 bimanual motor actions are broadly classified under three categories: (a) intrinsic to the person, (b) extrinsic or environmental, and (c) task. Intrinsic constraints originate from the inher ent characteristics of the individual and are termed organismic constraints. One such constraint related to bilateral movements is bimanual positive coupling. Bimanual positive coupling is based on the principle that during bilateral movements, both upper limbs have the tendency to produce symmetric actions such that the spatial and temporal components of both arms are positively synchronized (Hu & Newell, 2011a; Kelso, 1995) The second category involves extrinsic constraints that arise from the environment and are related to the surrounding conditions in which tasks are performed. An example of an environmental constraint is the information normally perceived by the sensory system of individuals (Hu & Newell, 2011a, 2011b) Although several sensory information sources have been identified, vision remains the most common and reliable source of task and performance related information (Hu & Newell, 2011a; Ranganathan & N ewell, 2008b) Indeed, reduced visual information is associated with increased performance error during a motor task (Kantowitz & Elvers 1988; Newell & MacDonald 1994). Lastly, task constraints involve the limitations or rules imposed by the task to achi eve a specific goal. For example, a task goal can be to achieve specific force amplitude or a movement distance within a time interval. Asymmetrical or dissimilar force production by both hands is a common task constraint required to achieve a bilateral t ask goal.

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15 Many bimanual force production activities require both hands to produce dissimilar forces and are inherently more complex than bilateral tasks that require similar force production. For instance, when one hand loosens a lid, the other hand typic ally holds the jar tightly. In this case, a negative correlation is required for the purpose of error compensation. Individuals must supersede the positive correlation enforced by bimanual coupling so as to satisfy the constraints imposed by the specific t ask requirements (i.e., producing dissimilar forces). However, to supersede the positive correlation and satisfy task requirements, information related to the task plays an important role. Therefore, the final bimanual coordination pattern emerges from the interaction of the organismic and task constraints with the environmental constraint of task relevant information. In other words, an ideal configuration of organismic, environmental, and task constraints is required for the performance of error free bila teral actions. Studies on bimanual force coordination in isometric tasks have investigated the interactive effects of these categories of constraints (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011a, 2011b, 2011c) In two of these studies, task constraints were manipulated to determine their pattern of interaction for bimanual positive coupling to influence t he force coordination patterns (Hu & Newell, 2011a, 2011c) These studies presented evidence favoring an interaction between constraints by showing that changes in task constraints cause the reorganization of the influence of other constraints on force coordination patterns. Additionally, researchers manipulated vision and task constraints together to determine the interactive influence on bimanual force coordination patterns (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011a) Hu,

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16 Loncharich, and Newell (2011) used a vision and no vision conditions whereas Hu and Newell (2011a) manipulated visual information by using visual gains of 8 and 80 pixels/N. Hu, Loncharich, and Newell (2011) found that error compensatory negative correlation was present in the condition where visual feedback was available and Hu and Newell (2011a) found that these negative patterns were more efficiently coordinated a t visual feedback of 80 pixels/N. Even though the two studies confirmed the contribution of vision in bimanual movements, Hu and Newell (2011a) reported that the influence of visual information was dependent on the task coefficients (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011a) Moreover, studies on unimanual movements investigating the importance of vision reported deterioration of motor performance in conditions when visual feedba ck is either reduced or withheld (Kantowitz & Elvers, 1988; Tracy, 2007; Vaillancourt & Russell, 2002) Given the importance of visual information for motor performance, further research is necessary. Specifically, the influence of a potent ial interaction between a wide range of visual feedback and different task constraints on bimanual force coordination patterns deserves further investigation. Therefore, a logical research extension involves investigating the interactive influences in whic h two different categories of constraints (i.e., environmental and task) are manipulated together. Furthermore, the present experiment is novel because no previous study has determined the interaction effect between different task constraint conditions and a range of visual feedback gain beyond 80 pixels/N on bimanual force coordination patterns.

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17 Whereas most of the previous studies on bimanual force production manipulated the task constraints over a wide range, the evidence on visual feedback more than 80 pixels/N comes from the studies on unimanual tasks (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011a) The studies investigating the influence of visual feedback on unimanual force produc tion tasks demonstrate performance deterioration and a plateau in variability with increased visual feedback (Baweja et al., 2010; Prodoehl & Vaillancourt, 2010; Sosnoff & Newell, 2006a, 2006b) Explanations based on unimanual movements may not apply to complex bima nual force coordination patterns that emerge from an interaction between organismic, task, and environmental constraints. Further, coordination patterns are more challenging in bimanual tasks because different coefficients require both hands to break away from the preferred mode of positive correlation imposed by bimanual coupling. Therefore, the purpose of the current study is to manipulate the environmental and task constraints to investigate their interaction with positive coupling to determine bimanual force coordination patterns. Specifically, this study will determine the interaction between different task constraints and a wide range of visual feedback on the bimanual force coordination patterns in an isometric force production task. Research Questio n How does an increased visual feedback interact with different task constraint conditions to influence the bimanual force coordination patterns in an isometric force production task?

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18 Hypothesis A primary hypothesis is that increased visual feedback will i nteract with the different task coefficients to influence the bimanual positive coupling which in turn will facilitate error compensatory negative coordination. Significance Bimanual tasks that involve asymmetric force production by both hands are more com plex than tasks that involve similar force production. Dissimilar force production tasks pose a challenge for the performer and present greater variability in force leading to higher performance error. As Hu and Newell (2011a) argued, to minimize error dur ing performance, such force production tasks require a force coordination pattern that involves negative correlation between hands (Hu & Newell, 2011a) However, negative correlation is difficult to achieve because the bimanual force coordination pattern has to break away from the positive correlation imposed by bimanual coupling (Hu & Newell, 2011a; Kelso, 1995) Availability of adequate sensory information has been associated with reduction in performance error and maintaining constant force output (Hu & Newell, 2011a) Vision has been identified as an important sensory feedback source as well as being associated with facilitating error compensatory negative correlation (Hu & Newell, 2011a; Ranganathan & Newell, 2008b) Therefore, an ideal configuration of visual feedback with different coefficients of tas k constraints is required to facilitate efficient task performance. In other words, bimanual force coordination patterns emerge out of the interaction of visual feedback and task constraints with the bimanual positive coupling. Previous studies investigat ed the ideal configuration of the interaction between various task constraints and visual feedback (Hu, Loncharich, & Newell, 2011; Hu & Newell,

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19 2011a) Indeed, whereas the focus of previou s studies has been on manipulating task constraints, no bimanual coordination study has investigated the interaction of different task constraint with visual feedback beyond 80 pixels/N (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011a) The present study addresses this research gap by determining the interac tion of visual feedback greater than 80 pixels/ N with task constraint conditions while investigating bimanual force coordination patterns during an isometric task.

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20 CHAPTER 2 METHODS Participants Fifteen right handed healthy young volunteers participated in this study. Hand dominance was confirmed usi normal or corrected to normal vision with no injury of the index fingers of the left and right hands. Before testing, participants read and signed an informed consent approved by the University of F lorida Institutional Review Board. Apparatus Participants were seated on a comfortable chair facing a 17 inch monitor. They maintained a straight trunk position with the elbows flexed to about 20 o to 45 o and shoulder flexed to about 15 o to 20 o The left an d right hands were in fully extended prone position with the fingers straight out. The index fingers of left and right hands were comfortably extended away from the rest of the digits. The arm and the hand positions allowed participants to contact two sepa rate force transducers. Required movements involved simultaneous index finger abduction of both hands to contact two force transducers mounted on small wooden blocks and attached to the platform. The display included a center green horizontal target line spanning the entire width of the screen and a white trajectory moving from left to the right and representing the combined force output of both index fingers of the participant. The target and trajectory line were of different colors to avoid confusion dur ing the performance of the task. The task requires participants to match their trajectory line with the target horizontal line through the bilateral isometric abduction of index fingers of both hands.

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21 Procedures Instructions To obtain isolated simultaneous index finger movements of their left and right hands, participants comfortably placed their index fingers away from the rest of the digits and were instructed to minimize movement in their other digits and wrist. Moreover, forearms were strapped to the ta ble and the three fingers (i.e., middle, ring and pinky) were strapped together to avoid i nvolvement in the movement ( F igure 2 1). Maximal Voluntary Contractions (MVC) Esti mation To determine MVC, participants produced maximal force by performing the s imultaneous bilateral index finger abduction movements while pressing the force transducers. They were instructed to push against the force transducer as hard as they can for 6 s. A total of three 6 s trials were performed. Each trial was followed by 30 s rest period. An average of these three trials was used as a representative MVC value of each participant. Experimental task The experimental task required that participants produced force at 20 % of their MVC. Twenty percent MVC was chosen because this for ce level is functionally relevant by being frequently used for most everyday activities (Marshall & Armstrong, 2004) To perform the task, individuals were instructed to produce simultaneous forces by their index fingers of both hands in such a manner that the combined sum of the forces will control the white trajectory line attem pting to match the 20 % green horizontal line. To manipulate the environmental constraint, visual feedback information was chosen at the visual gains of 8, 80, 256 and 512 pixels/N during the task. These visual

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22 gains were then converted into four visual angles using equation 2 1 (Vaillancourt, Haibach, & Newell, 2006) = 2 tan 1 (H1/D) (2 1) The visual angle is represented by and is calculated using the height and distance of the video display. The distance (D) was kept constant at 78 cm, and the height (H) was varied by altering the visual gain. The visual angles tha t were calculated using the visual gains of 8, 80, 256 and 512 pixels /N were 1 o 10 o 32 o and 59 o respectively. Increased visual feedback lead s to greater number of pixels representing a un it of force on the monitor ( F igure 2 2 ). In other words, greate r number of pixels representing a unit of force indicate s increased visual resolution. Manipulating the task constraints, three coefficients settings were imposed on the left and right hand index fingers. These coefficients settings were: 0.4:1.6, 1:1, 1. 6:0.4. Unequal coefficients essentially change the finger force output inducing a force asymmetry between left and right index fingers. The specific coefficients imposed on each finger were informed at the beginning of the block. One practice trial with ea ch hand was provided for the participants to understand the coefficient placed on each hand. This was followed by two practice trials where participants performed the bilateral task for familiarization and selection of their preferred finger force combinat ion. Each participant completed 12 blocks of trials (i.e., three coefficient ratios at each of four visual gains). The order of these block trials were randomized across individuals. Each block started with four practice trials followed by nine experiment al trials. A total of 108 experiment trials were performed in 12 blocks. Trials were 15 s long and a 10 s rest period was given between trials. A 1 min rest was provided between blocks.

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23 Data analysis Two load cells (MLP 25, Transducer Techniques, 4.16 1 .27 1.90 cm, range, 0.1% sensitivity) attached to the platforms were used to collect force data. A bidirectional fourth order Butterworth filter (cut off frequency; 20 Hz) was used for force filtering. Data analysis was performed on 9 s (6 14 s) of the force generation task. The ramping up and down of the force represented by initial 5 s and last 1 s of the 15 s trials was removed from data analysis. Outcome measures were the RMSE, SD and CV of the force output. Force coordination patterns were quantifi correlation coefficient. In addition, the structure of force output var iability was examined using ApEn Data were processed using a c ustom built Lab view program. Each dependent variable was analyzed using separate two way repeat ed measures ANOVAs with 3 levels of task coefficients and 4 levels of visual feedback. The experimental design is a completely within subjects design. Post hoc analysis was ed for all statistical analysis.

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24 Figure 2 1. Apparatus and finger/hand position Figure 2 2. Different visual feedback display (A) 1 o (B) 10 o (C) 32 o (D) 59 o Target line (green line) Force production A B C D Load cell Forearm restraint Finger restraint Load Cell Finger Restraint Forearm restraint Load Cell Finger Restraint Forearm restraint

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25 CHAPTER 3 RESULTS A primary hypot hesis of the current study was that a wide range of visual feedback will interact with task coefficients to reorganize bimanual force coordination facilitating error compensatory negative coordination. To test this hypothesis, data were collected during a force generation task and analyzed for task performance, bimanual force coordination, and structure of force variability. A two way Vis ual Feedback Task Coefficient (4 3) ANOVA with repeated measures on both factors was performed on the outcome variabl es Normality of the data was examin Any violatio ns of the sphericity assumption were adjusted with Greenhouse degrees of freedom Task Performance Performance error RMSE quantified performance error. A significant in teraction [ F (6, 84) = 3.03; p = 0.01; partial 2 = 0.18] was observed between visua l feedback and task coefficient ( Figure 3 1 ). In the bilateral (1:1) task condition, RMSE was hi gher for the visual feedback angle of 1 o than the other visual feedback c onditions. A similar trend was seen in the left (1.6) > right (0.04) task condition. However, in the left (0.4) < right (1.6) task condition, RMSE was higher fo r the visual feedback angle of 1 o than 1 0 o and 32 o Force Variability Force variability was qu antified by two outcome measures: (a) CV and (b) SD For bilateral forces, the completely within subjects design for CV revealed significant main effects for visual feedback [ F (1.254, 17.557) = 13.81; p = 0.001; partial 2 = 0.5] and task coefficient [ F (1.172, 16.410) = 11.03; p = 0.003; partial 2 =

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26 post hoc test on visual feedback revealed that the mean of CV in the 1 o visual angle condition was greater than the 32 o and 59 o visual conditions ( Fig ure 3 2 ). Follow up tests on the three task coefficients revealed that the left (1.6) > right (0.04) condition was greater than the bilateral (1:1) and left (0.4 ) < right (1.6) conditions ( Figure 3 3 ). Further analysis examined individual forces Unilateral force production of the left and right hands were submitted to a Visual Feedback Task Coefficient (4 3) ANOVA with repeated measures on both factors. The completely within subjects design for CV revealed task coefficient main effects for t he left hand [ F (1.198, 16.728) = 25.36; p = 0.00; partial 2 = 0.64] and the right hand forces [ F (1.293, 18.099)=132.56; p = 0.00; partial 2 = 0.9]. For the left hand, post hoc testing showed that the CV for the left (0.4) < right (1.6) condition was gre ater than the bilateral (1:1) and left (1.6) > right (0.04) task conditions. Follow up analysis for the right hand revealed that the CV for the left (1.6) > right (0.04) condition was greater than the bilateral (1:1) and left (0.4) < right (1.6) t ask condi tions. M eans for the task coefficient main effect are show n in Figure 3 4 Similarly, the two way repeated measures ANOVA on bilateral forces for SD indicated two significant main effect s: (a) visual feedback [ F (1.233, 17.259) = 10.60; p = 0.003; partial 2 = 0.43] and (b) t ask coefficient [F(1.092, 15.293) = 8.7; p = 0.009; partial 2 = 0.38]. The visual feedback main effect indicated that SD decreased with increased mean of SD in the 1 o visual angle c ondition was greater than the visual conditions of 32 o and 59 o Follow up analysis on the task coefficient main effect indicated that the SD for left (1.6) > right (0.04) condition was greater than the bilateral (1:1) condition. The bilateral force means

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27 f or the visual feedback and task coefficient main effects are shown in Figures 3 5 and 3 6 respectively. The two way repeated measures ANOVA on the unilateral forces revealed task coefficient main effects for the left hand [ F (1.045, 14.624) = 16.61; p = 0.00; partial 2 = 0.54] and right hand forces [ F (1.228, 17.191) = 52.68; p =0.00; partial 2 = 0.79]. Post hoc analysis on the left hand data indicated that the SD for left (0.4) < right (1.6) condition was greater than the bilateral (1:1), and left (1.6 ) > right (0.04) task conditions. For the right hand, the SD for left (1.6) > right (0.04) condition was greater than the bilateral (1:1), and left (0.4) < right (1. 6) task conditions ( Figure 3 7 ). Bimanual Force Coordination Consistent with Hu and Newell (2011a, b) analysis correlation was used to evaluate the coordination between force outputs for t he left and right hands. V way Vis ual Feedback Task Coeff icient (4 3) ANOVA with repeated measures on both factors. The analysis identified two significant main effects: (a) visual feedback [ F (3, 42) = 9.48; p = 0.00; partial 2 = 0.4] and (b) task coefficient [ F (2, 28) = 3.81; p = 0.034; partial 2 = 0.21]. F ollow up tests on visual feedback showed that the correlation coefficients were higher at the 1 o visual angle condition than the visual conditions of 32 o and 59 o This indicated a greater negative correlation between the left and right hands with increased visual feedback. Post hoc analysis of the task coefficient main effect showed that the correlation coefficient for the bilateral (1:1) condition was lower than the left (1.6) > right (0.04) condition. This showed a stronger negative correlation in the

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28 bil ateral (1:1) condition as compared to the left (1.6) > right (0.04) condition. Table 3 1 shows the correlation means and standard deviations. Structure of Force Variability The structure of force variability was quantified by Approximate Entropy (ApEn). A completely within subjects repeated measures ANOVA indicated a significant task coefficient main effect [ F (1.162, 16.262) = 16.02; p = 0.001; partial 2 = 0.534]. Post hoc analysis revealed that the mean of the left (1.6) > right (0.04) task condition was greater than the mean of the bilateral (1:1) and left (0.4) < right (1.6) c onditions ( Figure 3 8 ). Even though a difference between the task coeff icient conditions was found, the values were close to zero indicating that the time dependen t structure of force output was highly regular for each task condition (Lodha et al., 2010; Pincus, 1991).

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2 9 Table 3 1. Correlation Coeff icient means with standard error ( ) for all conditions Visual Feedback ( degrees ) 1 o 10 o 32 o 59 o Task Coefficients Bilateral (1:1) 0.03 0.07 0.16 0.06 0.24 0.06 0.18 0.05 Left>Right (1.6:0.4) 0.01 0.07 0.04 0.05 0.12 0.05 0.15 0.05 L eft
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30 Figure 3 2. For bilateral force, mean coefficient of variation ( SE) as a function of visual feedback main effect. Figure 3 3 For bilateral force, mean coefficient of variation ( SE) as a function of task coefficient main effect

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31 Figure 3 4 For individual forces, mean coeffi cient of variation ( SE) as a function of task coefficient main effect. Figure 3 5 For bilateral force, mean standard deviation ( SE) as a function of visual feedback main effect.

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32 Figure 3 6 For bilateral force, mean standard deviation ( SE) as a function of task coefficient main effect. Figure 3 7 For individual forces, mean standard deviation ( SE) as a function of task coefficient main effect.

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33 Figure 3 8 For bilateral force, mean approximate entropy ( SE) as a function of tas k coefficient main effect.

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34 CHAPTER 4 DISCUSSION The purpose of the current research was to investigate the interaction of visual feedback and task constraints on bimanual force coordination patterns. Overall, t he findings revealed that visual feedback an gle of 32 o that corresponded to the visual gain of 256 pixels/N decreased performance error and facilitated negative force coordination between both hands. Task performance Performance Error Previous studies reported that visual information is associated w ith an improvement in task performance (Hu, Loncharich, & Newell, 2011; Kantowitz & Elvers, 1988; Miall, Weir, & Stein, 1993; Prodoehl & Vaillancourt, 2010; Baweja et al., 2010 ) In line with previous findings, the current results revealed a more accurate force production with increased visual feedback for each of the three task coefficient conditions. Accurate force production indicated a decreased performance error However, this decrease in error was only seen from the visual angle of 1 o to 10 o or from t he visual feedback gain of 8 to 80 pixels/N Considering that previous studies have not investigate d the effect of a wide range of visual feedback on a bilateral task, current study is the first to report a plateau in performance error (i.e., initial drop followed by a stable condition) beyond 80 pixels/N. In other words, the error at visual angles of 32 o (i.e., 256 pixels/N) and 59 o (i.e., 512 pixels/N) was lower than 1 o but not different from the visual angle of 10 o Taken together, these findings sugges t that the optimal visual feedback condition to minimize performance error during a bilateral task can be at the visual angles of 10 o and 32 o

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35 Force Variability Force variability was analyzed bilaterally and unil aterally. For bilateral force, a decrease in force variability was observed in each task coefficient condition at the visual angle of 32 o The visual gain used at the visual angle of 32 o was 256 pixels/N. Contradictory fin dings exist in the literature for the effect of a wide range of visual feedbac k conditions on force variability during a unilateral task. Whereas Sosnoff and Newel (2006) reported an increase in force variability at visual feedback higher that 80 pixels/N, other studies report an initial drop followed by a stable level with no furth er decline or increase (Baweja et al., 2010; Prodehl & Vaillencourt, 2010). The present study found a decrease in force variability at a visual angle of 32 o The inconsistency with previous studies can be attributed to the different task that the participa nts performed. Participants in previous studies performed a unilateral task whereas the task performed in the current study was bilateral. Although visual angle of 32 o decreased variability in bilateral force, no such effect was seen on the variability of individual hands. Because the task goal was to match the target line using a combination of the forces of both hands, a possible interpretation involves one hand compensating for the higher variability of the other by reducing variability. Current findings on unilateral forces are contrary with studies investigating the effect of visual feedback on force variability during a bilateral task (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011a). Given that the present study had 12 different conditions, the len gth of the testing was twice as long as previous studies. Perhaps fatigue or decreased attention in the longer duration of testing confounded the effects of different visual feedbacks. Nonetheless, reduced bilateral variability and performance

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36 error indica ted that a n increase in visual feedback improved motor performance during the bilateral task. Bimanual Coordination Negative coordination patterns are required for error compensation in bilateral tasks that involve asymmetric force production between hand s. However, such patterns are difficult to achieve because of intrinsic bimanual coupling. Hu, Loncharich, and Newell (2011) manipulated the availability of visual information and found a negative correlation when visual feedback was available. Further, a strong negative correlation was reported at a visual feedback of 80 pixels/N than 8 pixels/N (Hu & Newell, 2011a). Additionally, Rangnathan and Newell (2008) reported a negative coordination with shorter visual information intermittency. Taken together, th e availability of visual information influences bimanual coordination patterns. The current study extends the previous findings by showing a novel negative correlation between hands at a visual angle of 32 o or v isual gain of 256 pixels/N. Among the three task coefficient conditions, a stronger negative correlation was found in the bilateral (1:1) than t he left (1.6) > right (0.04) or the left (0.4) < right (1.6) task conditions The bilateral (1:1) task condition was used as a control where no task constra ints were placed on hands. These findings are compatible with the previous studies and indicate that when the task became complex, individuals were unable to maintain the error compensatory negative correlation (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011 a, b, c). Thus, participants appeared to switch to a weak positive correlation or no correlation at all in the more complex task conditions. Although among the three task coefficient conditions, a negative correlation was most difficult to main tain i n complex task conditions; visual feedback at a visual angle of 32 o facilitated a stronger

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37 negative correlation in each task condition. In general the present findings indicate the importance of visual information in each task condition. This is incompati ble with previous studies that reported that task coefficients weighted more than visual feedback (i.e., increasing visual feedback facilitated a negative correlation in the bilateral condition but, not in the different task coefficient conditions; Hu, Lon charich, & Newell, 2011; Hu & Newell, 2011 a). This inconsistency can be explained by the wide range of visual feedback investigated in the present study (i.e., visual feedback gain of 256 pixels/N at 32 o has not been tested before) Structure of Force Va riability ApEn is a regularity statistic used to quantify the time dependent structure of force output (Pincus, 1991; Ho et al.,1997). A highl y structured signal reflects ApEn approaching zero and indicates predictability in force. On the other hand, a les s structured or unpredictable signal is reflected through an increase in ApEn. Increased irregularity or higher ApEn manifests a greater ability to incorporate additional control processes to achieve solution s for the task goal (Pincus 1991; Sosnoff et al. 2009; King & Newell, 2013) The present findings revealed bilateral ApEn in all the conditions closer to zero indicating that the time dependent structure of variability was highly regular (i. e., high predictability). These findings are partially consistent with previous studies that reported no increase in bilateral ApEn with different task coefficient conditions (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011 a, c) However, these studies found less regular structure of ApEn with increased visual feedback wh ich is contrary to the present findings A possible interpretation involves a force target of 20 percent. Although, 12 different conditions created by the combination of four visual and three task coefficient

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38 conditions were presented to the individuals in the current experiment force target remained the same in each of these conditions (i.e., 20 % of MVC). Thus, no task required the participants to increase the complexity of force (Hu & Newell, 2011c). A confounding factor for the benefit of visual feedback on ApEn observed in previous studies might be the length of testing in the current study. Practice associated with the repeated performance of the constant target task as seen in th e current study has been shown to increase the irregularity of force output (King & Newell, 2013) However, the participants showing higher irregularity with practice performed the task on two separate days limiting the duration to about half on each day. O n the other hand, the current study tested all conditions in one sitting which may have caused decreased atten tion or increased fatigue. Implications and Future Directions A large number of b ilateral everyday tasks require asymmetric force production between hands. Individuals with a neurological injury such as a stroke have difficulty regaining the level of precision required to perform complex bilateral tasks, limiting their independence. Th erefore, rehabilitation programs should focus on including complex bilateral tasks in training protocols to assist patients in reaching appropriate functional levels. The importance of visual feedback for motor performance during a unimanual task has been well documented (Kantowitz & Elvers, 1988; Miall, Weir, & Stein, 1993; Sosnoff & Newell, 2006; Baweja et al., 2010; Prodehl & Vaillencourt, 2010).The current findings add to this knowledge by reporting an optimal visual feedback level for performing bilat eral complex tasks. Perhaps visual feedback tha t should be used for retraining complex bilateral tasks is 256 pixels/N. However, the present findings are

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39 limited to young adults between the ages of 20 to 35. To generalize these findings, future research sh ould include older adults as well as people with neurological disabilities in similar force production studies. The purpose of the present study was to investigate visual feedback manipulations as an environmental constraint. However, other sources of sen sory feedback are available during movement (i.e., proprioception). Possibly, the sensory feedback is a combination of vision and proprioception (van Beers et al., 2002). Future studies can manipulate more than one sensory source to investigate how differe nt sources of feedback interact within the environmental constraint category. Integration of visual information is influenced by attention (Block & Bastian, 2010; Hu & Newell, 2011a). Because of the long duration of testing (i.e., about 90 min), attention and fatigue might be two potential confounds in the current study. Although plenty of rest periods were provided, some participants reported difficulty in maintaining the force levels throughout entire testing. Atten tion and fatigue should be consider ed i n future studies especially ones involving older participants and neurological deficit patients. The present experiment used different task coefficients to create asymmetry between force production of the left and right hands. Placing different coefficien t s on each hand altered the force production by changing the representation of the force on the screen. For example in the left (1.6) > right (0. 4) condition, 1 Newton of force p roduced by the left hand was represented as 1.6 Newton. On the other hand, 1 Newton of force pr oduced by the right hand was viewed as 0. 4 Newton. Although this method has been shown to successfully alter the force production of each hand,

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40 controlling the individual force levels of each hand is challenging (Hu, Loncharich, & Newell, 2011; Hu & Newell, 2011a, b, c) Because the current focus was to produce dissimilar forces, a n alternative method could require participants to match specific force levels with each hand. For example, in a left>right condition where the target force is set at 10 Newton, left hand should be required to produce 80 % (i.e., 8 Newton) and the right hand 20 % (i.e., 2 Newton) force. However, the practical implication of a m ethod involving each hand matching specific force levels is questionable. Thr ee different visual targets would have to be presented to the subject (i.e., one target each for the left hand, the right hand, and both hands force leve ls). Future research should consider a method w hich could be used to control force of each hand at a specific level without producing unnecessary force distraction s The c urrent findings part ially support the hypothesis that increasing visual feedback reorganize s the bimanual force coordination patter n to facilitate error comp ensatory negative correlation. A s trong negative correlation was seen in each task coefficient condition at a visual angle of 32 o or visual feedback gain of 256 pixels/N. Moreover, bilateral or total force variability was reduced with visual feedback in each task coefficient condition. Based on these findings, visual feedback of 256 pixels/N red uced force variability and increased negative force coordination in complex (i.e., producing dissimilar forces between hands) as well as si mple (i.e., producing similar forces) bilateral tasks. In conclusion, the present stu dy achieved an important finding: identifying a visual feedback condition that can be used to improve motor performance during a bilateral task. Furthermore, these finding s suggest that unilateral and bilateral tasks have different visual feedback requirements and should be taken into

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41 consideration while designing rehabilitation programs for patients with neurological impairments.

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42 LIST OF REFERENCES Baweja, H. S., Kennedy, D. M., Vu, J., Vaillancourt, D. E., & Christou, E. A. (2010). Greater amount of visual feedback decreases force variability by reducing force oscillations from 0 1 and 3 7 Hz. European Journal of Appl ied Physiology 108 (5), 935 943. Bernstein, N. A. (1967). The co ordination and regulation of movements, (1st English ed.). Pergamon Press. Block, H. J., & Bastian, A. J. (2010). Sensory reweighting in targeted reaching: effects of conscious effort, error history, and target salience. Journal of Neurophysiology 103 (1), 206 217. Churchland, M. M., Afshar, A., & Shenoy, K. V. (2006). A central source of movement variability. Neuron 52 (6), 1085 1096. Cole, J. D., & Sedgwick, E. M. (1992). The perceptions of force and of movement in a man without large myelinated sensory afferents below the neck. The Journal of Physiology 449 503 515. Cruse, H., & Brwer, M. (1987). The human arm as a redundant manipulator: the control of path and joint angles. Biological Cybernetics 57 (1 2), 137 144. Flash, T., & Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. Journal of Neuroscience 5 (7), 1688 1703. Gandevia, S. C., Macefield, G., Burke, D., & McKenzie, D. K. (1990). Voluntary activation of human motor axons in the absence of muscle afferent feedback. The control of the deafferented hand. Brain 113 1563 1581. Gelfand, I. M., & Latash, M. L. (1998). On the problem of adequate language in motor control. Motor Control 2 (4), 306 313. Hasan, Z. (1986). Optimized movement trajectories and joint stiffness in unperturbed, inertially loaded movements. Biological Cybernetics 53 (6), 373 382. Ho, K. K., Moody, G. B., Peng, C. K., Mietus, J. E., Larson, M. G., Levy, D., & Goldb erger, A. L. (1997). Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96 (3), 842 848. Hu, X., Loncharich, M., & Newell, K. M. (2011). Visual information interacts with neuromuscular factors in the coordination of bimanual isometric force. Experimental Brain Research 209 (1), 129 138.

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43 Hu, X., & Newell, K. M. (2011a). Visual information gain and task asymmetry interact in biman ual force coordination and control. Experimental Brain Research 212 (4), 497 504. Hu, X., & Newell, K. M. (2011b). Modeling constraints to redundancy in bimanual force coordination. Journal of Neurophysiology 105 (5), 2169 2180. Hu, X., & Newell, K. M. ( 2011c). Adaptation to bimanual asymmetric weights in isometric force coordination. Neuroscience Letters 490 (2), 121 125. Jaric, S., & Latash, M. L. (1999). Learning a pointing task with a kinematically redundant limb: Emerging synergies and patterns of f inal position variability. Human Movement Science 18 (6), 819 838. Jones, K. E., Hamilton, A. F., & Wolpert, D. M. (2002). Sources of signal dependent noise during isometric force production. Journal of Neurophysiology 88 (3), 1533 1544. Kantowitz, B. H., order of control and control display gain. Journal of Motor Behavior 20 (1), 53 66. Kelso, J. A. S. (1995). Dynamic patterns: the self organization of brain and behavior MIT Pres s. King, A. C., & Newell, K. M. (2013). The learning of isometric force time scales is differentially influenced by constant and variable practice. Experimental brain research, 227 (2), 149 159. Latash, M. L. (2000). There is no motor redundancy in human movements. There is motor abundance. Motor Control 4 (3), 259 260. Latash, M. L. (2008). Synergy (1st ed.). Oxford University Press. Latash, M. L. (2012). The bliss (not the problem) of motor abundance (n ot redundancy). Experimental Brain Research 217 (1), 1 5. Latash, M. L., Kang, N., & Patterson, D. (2002). Finger coordination in persons with Down syndrome: atypical patterns of coordination and the effects of practice. Experimental Brain Research 146 (3 ), 345 355. Latash, M. L., Levin, M. F., Scholz, J. P., & Schner, G. (2010). Motor control theories and their applications. Medicina (Kaunas, Lithuania) 46 (6), 382 392. Latash, M. L., Scholz, J. P., & Schner, G. (2002). Motor control strategies reveale d in the structure of motor variability. Exercise and Sport Sciences Reviews 30 (1), 26 31.

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44 Latash, M. L., Scholz, J. P., & Schner, G. (2007). Toward a new theory of motor synergies. Motor Control 11 (3), 276 308. Latash, M. L., & Turvey, M. T. (1996). De xterity and Its Development Psychology Press. Lodha, N., Naik, S. K., Coombes, S. A., & Cauraugh, J. H. (2010). Force control and degree of motor impairments in chronic stroke. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 121 (11), 1952 1961. Marshall, M. M., & Armstrong, T. J. (2004). Observational assessment of forceful exertion and the perceived force demands of daily activities. Journal of Occupational Rehabilitation 14 (4), 281 294. Martin, V. Scholz, J. P., & Schner, G. (2009). Redundancy, self motion, and motor control. Neural Computation 21 (5), 1371 1414. Miall, R. C., Weir, D. J., & Stein, J. F. (1993). Intermittency in human manual tracking tasks. Journal of Motor Behavior 25 (1), 53 63 Nelson, W. L. (1983). Physical principles for economies of skilled movements. Biological Cybernetics 46 (2), 135 147. Newell K.M. (1986). Constraints on the development of coordination. In M.G. Wade & H.T.A. Whiting. Development in children: aspects of coordination and control (pp. 341 360). Springer. Newell, K. M., & McDonald, V. P. (1994). Information, coordination modes and control in a prehensile force task. Human Movement Science 13 (3 4), 375 391. Prilutsky, B. I., & Zatsiorsky, V. M. (2002). Opt imization based models of muscle coordination. Exercise and Sport Sciences Reviews 30 (1), 32 38. Prodoehl, J., & Vaillancourt, D. E. (2010). Effects of visual gain on force control at the elbow and ankle. Experimental Brain Research 200 (1), 67 79. Ranganathan, R., & Newell, K. M. (2008a). Motor synergies: feedback and error compensation within and between trials. Experimental Brain Research 186 (4), 561 570. Ranganathan, R., & Newell, K. M. (2008b). Online feedback and the regulation of degrees of freedom in motor control. Human Movement Science 27 (4), 577 589.

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45 Rosenbaum, D. A., Meulenbroek, R. J., Vaughan, J., & Jansen, C. (2001). Posture based motion planning: applications to grasping. Psychological Review 108 (4), 709 734. Sosnoff, J. J., & New ell, K. M. (2006a). Aging, visual intermittency, and variability in isometric force output. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 61 (2), P117 124. Sosnoff, J. J., & Newell, K. M. (2006b). Information processing limitations with aging in the visual scaling of isometric force. Experimental Brain Research 170 (3), 423 432. Teasdale, N., Forget, R., Bard, C., Paillard, J., Fleury, M., & Lamarre, Y. (1993). The role of proprioceptive information for the production of isometric forces and for handwriting tasks. Acta Psychologica 82 (1 3), 179 191. Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5 (11), 1226 1235. Tracy, B. L. (2007). Visuomotor contr ibution to force variability in the plantarflexor and dorsiflexor muscles. Human Movement Science 26 (6), 796 807. Uno, Y., Kawato, M., & Suzuki, R. (1989). Formation and control of optimal trajectory in human multijoint arm movement. Minimum torque chang e model. Biological Cybernetics 61 (2), 89 101. Vaillancourt, D. E., & Russell, D. M. (2002). Temporal capacity of short term visuomotor memory in continuous force production. Experimental Brain Research 145 (3), 275 285. Vaillancourt, D. E., Haibach, P. S., & Newell, K. M. (2006). Visual angle is the critical variable mediating gain related effects in manual control. Experimental brain research, 173 (4), 742 750. Van Beers, R. J., Baraduc, P., & Wolpert, D. M. (2002). Role of uncertainty in sensorimotor co ntrol. PhilosophicalTtransactions of the Royal Society of London. Series B, Biological Sciences 357 (1424), 1137 1145. Yang, J. F., & Scholz, J. P. (2005). Learning a throwing task is associated with differential changes in the use of motor abundance. Exp erimental Brain Research 163 (2), 137 158.

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46 BIOGRAPHICAL SKETCH Amitoj Bhullar was born in Punjab, India He completed his b Physical Therapy from Guru Nanak Dev University, India. Before enrolling for a m program he was employed a s a physical t herapist in Apollo Physiotherapy center, Amritsar for two years H e plans to work as a p h ysical t herapist after the completion of his m degree