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Articulator involvement in naming

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Title:
Articulator involvement in naming a test of the articulatory feedback hypothesis of naming
Alternate title:
Test of the articulatory feedback hypothesis of naming
Creator:
Lu, Lisa Hsiao-Jung, 1971-
Publication Date:
Language:
English
Physical Description:
xi, 146 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Articulation disorders ( jstor )
Dental articulators ( jstor )
Dyslexia ( jstor )
Learning disabilities ( jstor )
Naming conventions ( jstor )
Phonemes ( jstor )
Phonological awareness ( jstor )
Phonology ( jstor )
Reading achievement ( jstor )
Reading comprehension ( jstor )
Articulation Disorders ( mesh )
Department of Clinical and Health Psychology thesis Ph.D ( mesh )
Dissertations, Academic -- College of Health Professions -- Department of Clinical and Health Psychology -- UF ( mesh )
Feedback ( mesh )
Knowledge ( mesh )
Names ( mesh )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph.D.)--University of Florida, 2000.
Bibliography:
Bibliography: leaves 141-145.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Lisa Hsiao-Jung Lu.

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University of Florida
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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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024732629 ( ALEPH )
53182203 ( OCLC )

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ARTICULATOR INVOLVEMENT IN NAMING:
A TEST OF THE ARTICULATORY FEEDBACK HYPOTHESIS OF NAMING














By

LISA HSIAO-JUNG LU


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


2000













ACKNOWLEDGMENT


I would like to thank each of my committee members for their guidance: Eileen

Fennell for her clinical wisdom, conceptual inquiry, and generous support; Ken Heilman

for the opportunity to approach neuropsychology through clinical hypothesis testing;

Bruce Crosson for his belief in my abilities; Duane Dede for helping me think broadly;

and Jamie Algina for his patient and thorough teaching of statistics. I have been very

lucky in crossing the paths of mentors who have such vigor and integrity both

professionally and personally. I would also like to thank Ann Alexander, Linda

Lombardino, Tim Conway, and the American Psychological Association for their support

of this project. To my family, especially to my husband, I would like to say thank you

for your undying support through these most challenging years.













TABLE OF CONTENTS


Page
ACKNOWLEDGMENT .......................................................................... ii

LIST O F TA BLES ..................................... ...................... ......... ........ ... vi

LIST OF FIGURES .. ...... ......................... ......... ......... ...... ix

A B STR A C T ...................................... ..................... .......................... x

IN TRO D U CTIO N ................................ .................. ............................. 1

Development of the Hypothesis ...................................................... 1
Liberman's Motor Theory of Speech Perception .......................... 1
Heilman's Motor-Articulatory Feedback Hypothesis ..................... 3
Anatomy of the Articulatory Feedback System ........................... 4
Proposed Hypothesis: Articulatory Feedback Hypothesis of Naming ........... 6
Developmental Phonological Dyslexia .............................................. 8
Definition of Developmental Phonological Dyslexia ..................... 8
Nature and Extent of Naming Deficit ....................................... 14
Role of Phonological Awareness ............................................. 18
Anatomical Evidence of Anomalies ......................................... 20
Co-morbidity with Attention-Deficit/Hyperactivity Disorder ............ 26
R research Q questions ..................................................................... 27
What Is the Correlation Between Articulatory Knowledge and
N am ing? ........................................................ ...... .. 28
Do Dyslexics Have Worse Articulatory Knowledge? ..................... 28
Is There Support for the Articulatory Feedback Hypothesis of
N am ing? ........................................................ ...... .. 29
What Is the Relationship Between Articulatory Knowledge and
Phonological Awareness? ............................................ 30

M ETH O D S ......................................... ............................................ .. 32

Subjects ............................................ ................................. .... 32
Descriptive M measures ................. ............... ........................... ...... .. 38
Articulatory Awareness Test .................................................. 38
N am ing ...................................... ........................... ...... .... 40
I'







Phonological Awareness ..................................................... 41
Attention-Deficit/Hyperactivity Disorder ................................... 41
Experimental Measures ................................................................. 42
Naming Assessed via Phoneme Match (NAPM) ........................... 42
V isual M atch ..................................................................... 45
Phoneme Match ................................................................. 46
N am ing Test ................................... ................................ .. 47
Procedures ......................................... .............................. ...... .... 48

R E SU LT S ............................................ ..................... ............... ...... .... 51

Articulatory Knowledge ................................................................. 51
Phonologically Impaired vs. Controls ................................................ 53
Articulatory Awareness Test .................................................. 54
Descriptive Measures .......................................................... 55
Experimental Measures ........................................................ 57
Predictors of Articulatory Knowledge ....................................... 68
Predictors of Phonological Awareness ...................................... 70
Developmental Phonological Dyslexics vs. Adequate Readers with
Poor Phonology vs. Controls .................................................. 72
Articulatory Awareness Test .................................................. 73
Descriptive Measures .......................................................... 75
Experimental Measures ........................................................ 76
Predictors of Articulatory Knowledge ....................................... 81
Predictors of Phonological Awareness ...................................... 84
Poor vs. Adequate Articulatory Knowledge .......................................... 85
Descriptive Measures .......................................................... 86
Experimental Measures ........................................................ 88

DISCUSSION ...................................... ......................... .................. 97

Review of Hypothesis .................................................................. 97
Correlation Between Articulatory Knowledge and Name Retrieval ............. 100
Group Differences in Articulatory Knowledge ..................................... 103
Group Differences on Naming Measures .......................................... 104
Reaction Time and Response Accuracy ................................... 106
Interference Movement Frequency ......................................... 110
Attention-Deficit/Hyperactivity Disorder .......................................... 113
Relationship Between Articulatory Knowledge and Phonological
A w areness ...... ............ .................... ... .. .... .. ... .... ............ 114
Articulatory Feedback Hypothesis of Naming .................................... 118
Lim stations .............................................................................. 12 1
Summary of Findings ................................. ........................... 123
Correlation Between Articulatory Knowledge and Naming .............. 123
Dyslexics Do Not Have Worse Articulatory Knowledge ................. 123
Relationship Between Articulatory Knowledge and Phonological







A w areness ....................... ...................................... 124
Modification of the Articulatory Feedback Hypothesis of Naming .... 124

APPENDIX 1 ARTICULATORY AWARENESS TEST ................................ 125

APPENDIX 2 ATTENTION-DEFICIT/HYPERACTIVITY DISORDER
IN TER V IEW .......................... ........................................ 135

APPENDIX 3 NAPM STIMULI ............................................................. 139

REFER EN CES ................................... .................. ............................ 141

BIOGRAPHICAL SKETCH .................................................................. 146













LIST OF TABLES


Table Page

1. Summary of grouping criteria ..................................................... 36

2. Summary of demographics and grouping criteria scores ....................... 38

3. AAT scores of the Morris Center population ................................... 40

4. Sample of the chart for determining the order of task, interference,
and stimulus set for each subject ................................................. 49

5. Order of test administration .......................... .............................. 50

6. Pearson correlations between the Articulatory Awareness Test (AAT)
score and reaction time on experimental measures ............................. 53

7. AAT and AAT-R scores obtained by Phonologically Impaired (PI)
and Control (CTRL) groups ........................................................ 54

8. Pearson correlations between the AAT score and reaction time on
experimental measures for PI and CTRL groups ............................... 55

9. PI and CTRL groups' performance on descriptive measures ................. 56

10. Means and standard deviations of reaction time (RT in milliseconds)
and accuracy (% Correct) on the Phoneme Match Test....................... 58

11. Reaction time and accuracy on the NAPM and Visual Match Tests
for PI and CTRL groups............................................................. 59

12. Means and standard deviations of reaction time for each block .............. 61

13. Response accuracy (percentage) reflecting the
Task X Interference X Block interaction ...................................... 62

14. Comparison of overall findings with Block 1 and Block 2 findings ......... 63







15. Block 1 reaction time and accuracy on the NAPM and Visual
Match Tests for PI and CTRL groups ........................................... 64

16. Block 2 reaction time and accuracy on the NAPM and Visual
Match Tests for PI and CTRL groups ........................................... 64

17. Reaction time and accuracy of the Non-ADHD and ADHD subgroups
on the NAPM and Visual Match Tests ......................................... 66

18. Interfering movement frequency index (i.e., number of movements
per second) for the PI and CTRL groups ........................................ 68

19. Pearson correlations between the AAT score and interfering movement
index for PI and CTRL groups ................................................... 69

20. Pearson correlations between the AAT score and variables entered
into stepwise regression analysis for PI and CTRL groups ................... 70

21. Pearson correlations between the LAC score and variables entered
into stepwise regression analysis for PI and CTRL groups ................... 71

22. Means and standard deviations of AAT scores obtained by DPD,
ARPP, and CTRL groups ........................... .... ... .. .... .. .... ............ 73

23. Pearson correlations between the AAT score and reaction time on
experimental measures for DPD, ARPP, and CTRL groups .................. 74

24. Means and standard deviations on descriptive measures for the
DPD, ARPP, and CTRL groups ................................................... 75

25. Reaction time and accuracy on the Phoneme Match Test for
DPD, ARPP, and CTRL groups ................................................... 76

26. Reaction time and accuracy on the NAPM and Visual Match Tests
for DPD, ARPP, and CTRL groups ............................................. 77

27. Reaction time and accuracy of the phonologically impaired
Non-ADHD and ADHD subgroups .............................................. 79

28. Interfering movement frequency index for the DPD, ARPP, and
C TR L groups ................................................................. ..... .. 80

29. Pearson correlations between the AAT score and interfering movement
frequency index for DPD, ARPP, and CTRL groups ......................... 82







30. Pearson correlations between the AAT score and variables entered
into stepwise regression analysis for DPD and ARPP groups ................ 83

31. Pearson correlations between the LAC score and variables entered
into stepwise regression analysis for DPD and ARPP groups .................. 85

32. Demographics of the Poor Articulatory Knowledge (PAK) and
Adequate Articulatory Knowledge (AAK) groups ............................. 86

33. Means and standard deviations on descriptive measures for the
PAK and AAK groups ............................. ................................. 87

34. Reaction time and accuracy on the Phoneme Match Test for the
PA K and AAK groups ............................. ................................. 88

35. Reaction time and accuracy on the NAPM for PAK and AAK groups.
Numbers represent data without the covariate extracted ....................... 89

36. Reaction time and accuracy on the Visual Match Test for PAK
and A A K groups .............................................................. ..... 93

37. Interfering movement frequency index for the PAK and AAK groups ....... 95













LIST OF FIGURES


Figure Page

1. A simplified model of reading from Ellis and Young (1988)................ 11

2. Block 1 NAPM reaction time, plotted against the ability to match end
phone es ................................... .................. ...................... 91

3. Block 2 NAPM reaction time, plotted against the ability to match end
phone es ...................................... ..................................... 92

4. Formula for calculating the effect size reflecting the Group X Task X
Interference interaction .......................... ......................... ........ 96













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

ARTICULATOR INVOLVEMENT IN NAMING:
A TEST OF THE ARTICULATORY FEEDBACK HYPOTHESIS OF NAMING

By

LISA HSIAO-JUNG LU

August, 2000

Chair: Eileen B. Fennell
Major Department: Clinical and Health Psychology

The articulatory feedback hypothesis of naming posited that articulatory feedback

facilitates name retrieval. This was tested using an interference paradigm. Naming

performance during a condition that allowed for articulatory feedback was contrasted

with a condition that interfered with articulatory feedback by providing inappropriate

articulatory feedback. Because Montgomery found that dyslexic children had impaired

articulatory knowledge, performance of phonologically impaired readers was contrasted

with those of normal readers. Subjects were also grouped by their level of articulatory

knowledge, and performance between knowledgeable and unknowledgeable groups was

compared. One assumption of the hypothesis was that those with adequate articulatory

knowledge would benefit from articulatory feedback while those with poor articulatory

knowledge would not. The hypothesis predicted that interfering with articulatory

feedback would affect subjects who have articulatory knowledge by removing the







facilitation effects provided by articulatory feedback. Results did not directly support the

hypothesis. For individuals with articulatory knowledge, naming latency during the

condition that allowed for articulatory feedback was not better than the condition that

interfered with feedback. Subjects did not spontaneously use articulatory feedback to

assist name retrieval. However, other data did suggest a relationship between articulatory

knowledge and name retrieval. Among individuals with poor articulatory knowledge,

inappropriate articulatory feedback and name retrieval interfered with each other and

competed for neural resources. This suggested a neural connectivity between articulatory

knowledge and name retrieval that was not evident between articulatory knowledge and a

nonverbal control task. Those with articulatory knowledge appeared to have processed

name retrieval automatically and efficiently, and they had sufficient extra neural

resources to process extraneous information such as interfering feedback. In contrast,

those with poor articulatory knowledge retrieved names less efficiently. They had

limited capacity to simultaneously process interfering information while engaging in

name retrieval. It was also found that articulatory knowledge and phonological

awareness were dissociable phenomena. Both normal and phonologically impaired

readers demonstrated a wide range of articulatory knowledge, and dyslexic children did

not have worse articulatory knowledge.













INTRODUCTION


The problem of name retrieval is one that has received extensive attention in the

neuropsychology literature. By focusing on this limited aspect of language, researchers

hope to generalize the knowledge learned here to other aspects of linguistic functioning.

The term name retrieval has been defined differently by different research groups. Here,

name retrieval refers only to the activation of phonological representation of a word and

does not include activation of motor patterns to produce such a representation. The

current project proposes to test a new hypothesis of name retrieval, the articulatory

feedback hypothesis of naming. First, the development of this hypothesis from

Liberman's motor theory of speech perception and Heilman's theory of motor-articulatory

feedback will be presented. Then the articulatory feedback hypothesis of naming will be

proposed. This hypothesis was tested with a population of children who have

phonological dyslexia of the developmental type. Therefore a discussion of dyslexia,

related name retrieval issues, neurological anatomy of this population, and co-morbidity

with Attention-Deficit/Hyperactivity Disorder (ADHD) will follow.


Development of the Hypothesis


Liberman's Motor Theory of Speech Perception


Liberman, Cooper, Shankweiler, and Studdert-Kennedy first proposed their motor

theory of speech perception in 1967. Extensive research following the first proposal of







their theory has led to subsequent revisions of their theory, the most recent of which was

presented in Liberman and Mattingly, 1985. Their current theory on speech perception

was based on two tenets: 1) The object of speech perception is the intended gestures of

the speaker; 2) speech perception and speech production are innately (i.e., biologically)

linked, not learned.

The first tenet of their theory speaks to why this theory is a "motor" one instead of

a "sensory" one. Unlike auditory theories, which posit that perception of speech depends

on an analysis of auditory signals, Liberman and Mattingly (1985) proposed that the goal

of speech perception is not to uncode the auditory signals, but to infer the intended

gestures of the speaker's vocal system. They argued that the uncoding of auditory cues

cannot be sufficient for speech perception because there is no correspondence between

acoustic signals and phonemic categories. Acoustic signals for the same phonemic

category vary by speaker, prosodic tone, and context. Though acoustic signals are

different in these different conditions, the same phonemic percept is perceived.

Conversely, the exact same acoustic signal under different contexts can yield different

phonemic percepts. The lack of a relationship between acoustic signals and phonemic

categories suggests that acoustic signal by itself is not sufficient for the perception of

speech. Furthermore, that visual feedback of oral gestures can influence the perception

of a speech sound (McGurk & MacDonald, 1976; MacDonald & McGurk, 1978)

suggests that both visual and acoustic signals are merely cues for the object of perception.

Liberman and Mattingly (1985) argued that the object of speech perception is the

intended gesture, or the actual motor movement, of the speaker. The object is the







intended gesture because much of the gesture takes place inside the speaker's oral cavity

and out of sight of the perceiver.

These motor theorists propose that the speech perception system is able to decode

intended gestures from auditory signals because speech perception system is a specialized

neural module evolved to perform such linguistic functions. They assumed that the

development of motor control over the vocal tract preceded the evolution of speech.

Adaptations made coarticulation of rapid phonetic gestures possible. A perceiving

system developed concomitantly, and this system is specialized to take into account

complex acoustic consequences. Because the perception system developed

concomitantly with the production system, they are biologically linked. The calculations

necessary to perceive speech are done automatically via hardwired neural structures that

connect the production and the perception parts of the system. This system is one

specialized for linguistic functions. It is a linguistic module that operates independently

from the general auditory system that processes other non-linguistic signals.


Heilman's Motor-Articulatory Feedback Hypothesis


Heilman, Voeller, and Alexander (1996) elaborated on Liberman's theory and

proposed a motor-articulatory feedback hypothesis of speech perception. They

emphasized that the perception of spoken words is associated with the production of

intended articulatory gestures. As an infant learns to perceive words, s/he imitates the

sounds heard by replicating intended articulatory gestures of the speaker with his/her own

articulators. As the infant fine-tunes this imitation of sounds, s/he associates each

phoneme with a movement of his/her articulators. Feedback from the articulators to the







neural module is essential for the individual to be aware of this relationship between

phoneme and articulatory gesture. Understanding of this relationship constitutes

articulatory knowledge, which facilitates parsing spoken words down into phonemic

parts.

Heilman et al. (1996) applied their theory to the problem of grapheme-to-

phoneme conversion in reading. They pointed out that when first learning to read, one

needs to break words down into letters clusters and associate them with their respective

phonemes. The motor-articulatory feedback hypothesis posits that children use the

articulatory apparatus when learning to associate specific graphic representations with

phonemic representations. Learning to read includes using the articulatory knowledge

learned earlier to associate phonemes with graphemes. Without this articulatory

knowledge as a mediating tool, the means by which written words are broken down into

letter clusters may seem arbitrary. Reading thus involves associating already established

phoneme-articulatory relationships to graphemic representations. Beginning readers

often move their lips and tongue even when reading silently. Adults also engage the

articulators when reading novel or hard-to-pronounce words. These findings suggest that

engaging the articulators facilitates reading. The motor-articulatory feedback hypothesis

proposed that it is the feedback that articulators provide which facilitates the learning of

grapheme-to-phoneme conversion.


Anatomy of the Articulatorv Feedback System


Central to the human language system are two major neural regions, Wernicke's

and Broca's areas in the left cerebral hemisphere of most right-handed individuals (Kolb







& Whishaw, 1990). Wemicke's area, which is located in the left posterior portion of the

superior temporal gyrus, and surrounding regions are sometimes referred to as posterior

language areas that process the perception of language. Classically, a lesion in

Wemicke's area results in fluent aphasia, characterized by fluent speech but impaired

comprehension, repetition, and naming. Information from these posterior perisylvian

regions travels anteriorly to Broca's area via the arcuate fasciculus. Broca's area, located

in the left inferior frontal gyrus, is conceptualized as an area specialized for motoric

programming of speech. Lesion of Broca's area results in nonfluent aphasia characterized

by intact comprehension but effortful, nonfluent speech.

The motor-articulatory hypothesis proposed by Heilman et al. (1996) emphasizes

the learned association between a phoneme and an articulatory gesture. When an infant

hears a novel word, his/her auditory and auditory association cortex (Wernicke's area)

analyze the sounds in the word. Pars opercularis, triangularis, and the foot of the motor

cortex (Broca's area) execute a complex motor program to approximate the heard word.

Primary motor cortex activates the articulators in the oral cavity. During word imitation,

as the articulators move, they send sensory feedback (i.e., proprioceptive and tactile) to

the primary sensory cortex and sensory association cortex. These sensory cortices

presumably connect with the frontal areas involved in motor planning (e.g., Broca's area),

thus providing linkage back to the articulatory system. The sensory areas also project to

polymodel sensory cortex in the temporal-parietal region that stores auditory

representations of words (e.g., Wernicke's area). Eventually, through this connectivity,

the infant learns that each phoneme is associated with one motor pattern for articulation,

and that words are associated with a series of articulatory patterns.








Proposed Hypothesis: Articulatory Feedback Hypothesis of Naming


Articulatory knowledge assists reading presumably because it facilitates retrieval

of information. In the case of reading novel words where it is necessary to use

grapheme-to-phoneme conversion, the subject sees a grapheme, and this grapheme

triggers the phoneme associated with it and the articulatory motor pattern used to produce

that phoneme; execution of this motor pattern results in the production of the phoneme

associated with the target grapheme. Movement of the articulators may not be necessary

for the retrieval of the phoneme, and feedback may even be intracerebral.

If articulatory feedback assists reading, it is likely that it also assists name

retrieval, a more basic language function that came into use much earlier in the

evolutionary process than reading. The articulatory feedback hypothesis of naming states

that articulatory feedback facilitates name retrieval. It does not state that articulatory

feedback is necessary for naming or sufficient for naming. Rather, articulatory feedback

assists other retrieval systems, making the process faster and more efficient.

Many of our everyday experiences suggest that activation of motor patterns can

facilitate retrieval. The tip-of-the-tongue phenomenon, where one experiences problems

retrieving a word, may sometimes be overcome by articulating the beginning sound.

When one cannot recall a phone number, pretending that one is dialing, thereby engaging

the motor system controlling the fingers, sometimes assist the recall of phone numbers.

Whereas retrieval may not need motor activation, if some part of the retrieval system is

compromised, motor activation may provide the extra input necessary to activate a

representation.







Heilman et al. (1996) posited that in order to benefit from articulator feedback,

one must have articulatory knowledge. Articulatory knowledge refers to the ability to

locate the position of the articulatory structures, such as the tongue and the lips, during

phoneme production. This knowledge could be conceptualized as a neural association

between the articulatory system, the sensory/proprioceptive system, and the phonemic

representation system. Without this link, activation of the articulators may not coactivate

neural patterns representing phonemic percepts, which would limit the articulatory

system's ability to facilitate retrieval. The spontaneous use of articulatory knowledge will

be referred to as articulatory awareness.

Montgomery (1981) showed that children with dyslexia have impaired articulator

knowledge compared to children without dyslexia. She presented cartoons of sagittal

drawings through the oral cavity that illustrate the position of tongue, teeth, and lips, then

asked which of the cartoons matched phonemes that she produced. She encouraged the

subjects to repeat the phonemes as much as they want and to think about the location of

their tongue, teeth, and lips. The non-dyslexic children were able to correctly identify

their articulatory positions better than dyslexic children. All children were able to repeat

the phonemes; thus the dyslexic children's deficit cannot be explained by an auditory

perceptual deficit. They appeared to be unknowledgeable about the position and

movement of their articulators. They lacked articulatory knowledge.

Montgomery's (1981) work suggested that dyslexic children lack sensory

feedback about their articulators' position and movement. This population could be

instrumental in testing the articulatory feedback hypothesis of naming. The hypothesis

stated appropriate articulator movements (and the sensory feedback concomitant with







those movements) facilitate lexical retrieval in individuals with articulatory knowledge.

In the normal population, inhibiting appropriate articulator movements while asking

subjects to name objects should impede their naming. This was done in this study by

introducing an interference task. Subjects were asked to engage in mouth movements

that interfered with the articulation of object names. Thus the articulatory system could

send articulatory feedback to the central nervous system that was appropriate to and

facilitated name retrieval. Individuals with impaired articulatory knowledge should

respond differently. They could differ in one of two ways. First, because these

individuals may not be as knowledgeable of their articulator movements as normal

individuals, they may not use an articulatory strategy to assist naming. Therefore

interfering with the articulators during a naming task may not impede the naming process

of these individuals as much as it does in controls. However, individuals with impaired

articulatory feedback may have no other alternative strategy to assist naming. Because

they already have impaired articulatory feedback, adding an interference could impair

their naming ability even more, making them less capable of retrieving names than

controls.


Developmental Phonological Dyslexia


Definition of Developmental Phonological Dyslexia


The terms dyslexia, learning disability in reading, and reading disability have

been used interchangeably in the literature. All three refers to problems with reading, but

a clarification of terms is in order. The termdyslexia has been used in research

attempting to understand the neurological or neuropsychological deficits underlying the







disorder. Dyslexia can be categorized into developmental versus acquired. Acquired

dyslexia refers to those individuals who acquired the disorder through insults to the

central nervous system after a period of normal reading development (Coslett, 1997).

Developmental dyslexia refers to those individuals who demonstrate problems in the

development of reading. Researchers have posited the existence of many types of

dyslexia, including phonological, surface, and deep dyslexia, to name a few (Ellis &

Young, 1988). The Orton Dyslexia Society Research Committee has defined dyslexia as:

one of several distinct learning disabilities. It is a specific language-based
disorder of constitutional origin characterized by difficulties in single word
decoding, usually reflecting insufficient phonological processing abilities. These
difficulties in single word decoding are often unexpected in relation to age and
other cognitive and academic abilities; they are not the result of generalized
developmental disability or sensory impairment. Dyslexia is manifest by variable
difficulty with different forms of language, often including, in addition to
problems reading, a conspicuous problem with acquiring proficiency in writing
and spelling. (Shaywitz, Fletcher, & Shaywitz, 1995, p. S51)

This committee, composed of representatives from the National Institute of Child Health

and Human Development, defined dyslexia as a subtype of learning disability. Thus

learning disability is an umbrella term encompassing many types of problems with

learning, including reading and math. Reading disability is an abbreviated term for

learning disability in reading. This is a legal term used to identify individuals who meet

legal criteria to receive special education services. Dyslexia is used interchangeably with

reading disability, but it is a theoretical/research term, and its use implies neurological

abnormalities within the language system that underlie difficulty with reading processes.

Dyslexia constitutes 80% of children diagnosed with learning disability

(Shaywitz, Fletcher, & Shaywitz, 1995). Of the different types of dyslexia, phonological

dyslexia is the most common form. Phonological dyslexia refers to reading problems







secondary to phonological processing deficits. Specifically, impairment in the

grapheme-to-phoneme conversion system has been implicated. A simplified version of

Ellis and Young's (1988) model for oral reading is depicted in Figure 1. According to

this model, written words are processed initially by the visual system, which processes

visual stimuli by analyzing each individual letter component. After visual analysis,

reading can be achieved by three mechanisms or routes: 1) Results of the visual analysis

system enter into the orthographic input lexicon, which contains visual representations of

words an individual has learned. The selected lexical representation enters into the

semantic system and activates the meaning of the word. From the semantic system, the

appropriate auditory representation of the word is activated in the speech output lexicon.

Then speech is produced by activation of motor patterns. Most proficient readers are

thought to use this lexical system because of its efficiency and completeness compared to

the other two systems. 2) The second method for reading is similar to the first except that

the semantic system is bypassed, so that words can be read without accessing the

meaning of words. These two lexical, or whole-word reading routes are important for

reading irregular and ambiguous words, and cannot be used to read nonwords or

pseudowords. 3) The third method, the phonological route, is a labor-intensive system

used when reading novel words (i.e., words without representation in the orthographic

input lexicon). The visual system analyzes words and parses words into letter

components. The letter or letters graphemee) are converted to the sounds they represent

(phoneme). The phonemes are blended to produce the phonological sequence for the

entire word. Phonological dyslexia results when this grapheme-to-phoneme conversion

link is defective.




























Semantic
System


11

Written word




Visual
Analysis
System

/

Visual
Input
Lexicon




Grapheme-Phoneme
Conversion


Speech
Output
Lexicon


Phoneme
Level


Speech


Figure 1. A simplified model of oral reading from Ellis and Young (1988).







One problem with dyslexia research is that research groups often do not specify

the type of dyslexia subjects demonstrate. This is especially true with treatment-focused

research or service, where the primary goal is to improve patients' reading skills,

regardless of whether patients meet criteria for certain theoretical subtype of dyslexia.

This was true for Montgomery's (1981) work, which reported that subjects were

"dyslexic" without specification of subtype. The present study, in an attempt to strive for

theoretical clarity, will limit dyslexic participants to those with developmental

phonological dyslexia, defined as individuals who have impaired grapheme-to-phoneme

conversion, because this is the largest, most common subtype of dyslexia. Thus an

assumption of the present study is that a large percentage of participants in the

Montgomery (1981) study were phonological dyslexics, and that phonological dyslexics

have decreased awareness of their articulator position and movement.

The identification of developmental phonological dyslexia is problematic for two

reasons. First, dyslexia falls under the broad category of specific learning disability

under Individuals with Disabilities Education Act (IDEA, 1997; Public Law 105-17),

which requires that educational institutions provide special services to meet the

educational needs of individuals with disabilities. Because the law does not require the

specification of the subtype of reading disability, most clinical organizations do not

specify if a patient's reading disability fits the phonological subtype in diagnostic

evaluations. Second, the IDEA specified that reading ability should be discrepant from

intellectual aptitude, but it did not state how such discrepancy should be measured.

Different groups have used intelligence quotient (IQ)-achievement discrepancy

(Ackerman & Dykman, 1993; Cornwall, 1992), chronological age-reading age







discrepancy (Fawcett & Nicolson, 1994; Felton, Naylor, & Wood, 1990; Felton, Wood,

Brown, Campbell, & Harter, 1987; Wolf& Goodglass, 1986; Wolf& Obregon, 1992),

arbitrary cutoff scores on tests of achievement or based on teacher/school referrals

(Bowers & Swanson, 1991; Denckla & Rudel, 1976; Korhonen, 1995; Manis,

Seidenberg, Doi, McBride-Chang, & Petersen, 1996; Mattis, French, & Rapin, 1975;

Swan & Goswami, 1997), or regression approaches that control for the intercorrelation

between achievement and IQ measures (Fletcher, Francis, Rourke, Shaywitz, &

Shaywitz, 1992; Fletcher, Schaywitz, Shankweiler, Katz, Liberman, Stuebing, Francis,

Fowler, & Shaywitz, 1994; Pennington, Gilger, Olson, & DeFries, 1992; Shaywitz,

Escobar, Shaywitz, Fletcher, & Makuch, 1992; Shaywitz, Fletcher, Holahan, & Shaywitz,

1992; Shaywitz, Shaywitz, Fletcher, & Escobar, 1990). Consequently, the literature on

this population is fraught with inconsistent diagnostic criteria for reading disability.

The IQ-achievement discrepancy method has been shown to be problematic in

diagnosing reading disability among minority populations. Duckworth (1999) showed

that among a sample of college students referred for evaluation of learning disability,

African Americans score on average 12 points lower on the Wechsler Adult Intelligence

Scale-Revised (WAIS-R) intelligence quotients than their European-American

counterparts. Although psychologists have attempted to design culture-free intelligence

tests in recent years, Duckworth's data suggest that a commonly used intelligence

measure, WAIS-R, is still biased against minority populations. The IDEA specified that

learning disability cannot be due to mental retardation, which is defined as IQ scores of

below 70. If the normal distribution of African Americans' IQ scores is downshifted by

12 points, then the difference between the mean IQ score of the African-American







population and the mental retardation cutoff of 70 is decreased by 12 points, which in

effect, decreases the potential number of African Americans who can meet diagnosis for

learning disability using a simple difference discrepancy method.

An alternative to this problem is to calculate expected achievements scores based

on intellectual aptitude via a regression method. This method controls for the inter-

correlation between achievement and intelligence measures and the regression of

achievement scores toward the mean intelligence score, and minimizes problems of over-

identifying high-IQ subjects and under-identifying of low-IQ subjects as learning

disabled. Using the regression formula,

Y'= [r, (S, / Sy )(IQ X)] + Y

(where Y' is the expected achievement score for a given IQ, ry is the correlation between

the IQ and the achievement test, S, is the standard deviation of the achievement test, S" is

the standard deviation of the intelligence test, IQ is the achieved intelligence score, X is

the mean of the intelligence test, and Y is the mean for the achievement test), Duckworth

showed that among those referred for a learning disability evaluation, more African

Americans would be classified as learning disabled than using a simple discrepancy

difference method (51% vs. 28%, respectively), while the method of classification used

does not significantly affect the number of European Americans classified as learning

disabled (27% vs. 30%, respectively).


Nature and Extent of Naming Deficit


A basic question relates to the existence of bonafide naming deficits in the

dyslexic population. Because dyslexia is a reading disability, could their naming deficits







be attributable to a lack of vocabulary? If they do have bonafide naming problems, do

they have problems retrieving names of symbols that compose written language? Or do

they have a general retrieval problem that implicates naming of other targets? Fawcett

and Nicolson (1994) examined naming performance of 35 dyslexic children (defined as

having at least an 18 month discrepancy between chronological and reading age, with full

scale 1Q of at least 90) and 32 chronological age controls (CA). They found that

dyslexics have impaired naming of letters, digits, colors, and pictures compared to the

CA group, with the picture naming task being the most robust measure differentiating

groups. The dyslexics' discrepant performance on color and picture naming suggested

that their deficit was not limited to grapheme-to-phoneme translation. They have actual

problems with name retrieval.

A critique of Fawcett and Nicolson's (1994) study was that their groups were not

matched on intellectual aptitude and therefore retrieval differences may be explained by

intelligence differences between dyslexics and controls. Swan and Goswami (1997)

recruited a dyslexia group (n = 16), a CA control group (n = 16), a reading age (RA)

control group (n = 16), and a garden-variety poor reader control group (GV; n = 16). All

groups had matching IQ scores except the GV (101-105 vs. 79), and all groups had

matching reading age except the CA (112-116 mo. vs. 139 mo.). Swan and Goswami

(1997) found the following pattern of performance on a picture naming test (percentage

correct score based on the total number of items familiar to each subject):

CA>RA>GV=dyslexics. Dyslexics performed as well as GV. Both groups' naming

scores were worse than younger but reading age matched controls (RA). All of these

groups performed worse than the CA controls. However, dyslexics were more accurate







16
than any other group in correctly recognizing targets on a follow-up multiple choice task

composing of items they failed to name spontaneously. (Subjects' scores on this test were

coded as a proportion of their total error score, so scores were not inflated for those with

more errors). Dyslexics' ability to correctly identify targets in a recognition paradigm

argued against a vocabulary deficit. Rather, it supported that they have problems of

retrieval. In contrast, GV were found to have poorer vocabulary on a test of receptive

vocabulary. Swan and Goswami (1997) concluded that while GV's poor picture naming

performance was due to poor vocabulary, dyslexics' was due to problems with name

retrieval.

Wolf and Obregon (1992) found similar results using a multiple-choice paradigm

with items on the Boston Naming Test (BNT) that were missed. Their selection criterion

for dyslexia was better defined than the Swan and Goswami (1997) study: Dyslexics

were 2 or more years below expected reading level as assessed by the Gray Oral Reading

Test. Compared to an average reader control group (n = 42), dyslexics' (n = 8) naming

was worse, but dyslexics were more accurate on identifying the correct target in a

multiple-choice format compared to controls. They also concluded that dyslexics'

naming errors were reflective of a retrieval deficit.

These studies showed that dyslexics have lexical retrieval problems on formal

neuropsychological tests. Murphy, Pollatsek, and Well (1988) questioned 1) if dyslexics'

retrieval problem was one of general processing deficit or was it specific to language, and

2) whether dyslexics' retrieval deficit can be seen in their natural/spontaneous use of

language. They reasoned that if dyslexics have a general processing deficit, they should

be slower on tasks not involving the explicit use of language, such as a simple reaction







time task requiring them to move their finger to the side where a visual target appeared,

and on a picture categorization task requiring them to indicate if a picture is an exemplar

of a target category. If their retrieval deficit was specific to language, they should show

deficient performance on tasks of oral expressive and receptive language as well as on

formal neuropsychological measures. They tested dyslexics identified by poor Rapid

Automatized Naming (RAN) performance and who were at least two years below their

expected reading level (n = 14). Controls were matched for age and IQ (n = 14). They

found no difference between groups on basic motor reaction time and picture

categorization, which ruled out the general processing deficit hypothesis. Dyslexics

performed worse than controls on both formal (BNT) and informal language measures.

On informal, expressive language measures, dyslexics generated fewer words in retelling

stories and had slower verbal output. On informal, receptive language measures, they

were slower at categorizing spoken words. The authors concluded that dyslexics' name

retrieval problem reflected a specific linguistic deficit, and not a general processing

deficit, and their name retrieval problem manifested in their oral language as well as on a

formal neuropsychological measure.

The retrieval problems that dyslexic children demonstrate in childhood have been

shown to persist into adulthood. Korhonen (1995) followed a small group (n = 8) of

children who had problems in rapid automatic naming and in word retrieval, and tested

them approximately 9 years later at 18 years of age to examine the persistence of naming

deficits identified during childhood. These children were originally identified by their

teachers as learning disabled children who demonstrated special problems in reading.

Korhonen comparing these individuals' performance to controls matched on age, sex, IQ,







parent SES at nine years of age, and education level at 18 years of age (n = 10).

Korhonen found that learning disabled individuals were slower and made more errors on

rapid color naming and rapid object naming, and on another test of rapid alternating

stimulus naming. The findings were not as robust as at nine years of age; nevertheless

they were present. Fawcett and Nicolson (1994) tested dyslexics from eight to 17 years

of age and also found naming deficits in their 17-years old dyslexic group (n = 13).

Felton, Naylor, and Wood (1990) followed 115 children with dyslexia into adulthood.

They defined dyslexia as a discrepancy of 1.5 years between chronological and reading

age. They found persistent problems in rapid naming, nonword reading, and

phonological awareness. These findings of persistent naming problems suggested a

deficit model of dyslexia, which conceptualized dyslexia as a deficit that does not "catch

up" with maturation.


Role of Phonological Awareness


A hypothesized deficit underlying dyslexia is an impaired sense of phonological

awareness (Liberman & Shankweiler, 1985). Swan and Goswami (1997) used a picture

naming paradigm to study the role of phonological processing in dyslexics. They

hypothesized that if a phonological deficit underlies dyslexia, dyslexics' naming

performance would be worse for longer words of low frequency. Longer words have

more phonemes to encode and retrieve, and thus were more demanding on the

phonological system. Low frequency words occur less often in language, making them

less familiar to the phonological system. They found a Group X Frequency X Length

interaction, where with frequency controlled, dyslexics (n = 16) named short words better







19
than long words. This pattern was not seen in the CA, RA, or GV controls. Lower level

interactions also showed expected findings: Dyslexics named short words better than

GV, but their naming of long words was worse than GV and RA. Dyslexics named high

frequency words better than GV, but their naming of low frequency words was worse

than RA. Swan and Goswami (1997) further posited that dyslexics' picture naming

would be worse than word naming because in word naming, letters were available to

assist the phonological system. In picture naming, no cues were present to assist the

phonological system. They did find impaired picture naming compared to word naming

for dyslexics but not for RA and CA.

A natural question that arose with evidence of phonological and naming deficits

in dyslexia regards the relationship between these processes. Two studies have addressed

this issue but with incongruent results. Cornwall (1992) used a regression analysis to

examine if phonological awareness and rapid automatized naming contributed unique

variances to reading disabled children's scores on academic achievement (n = 54; reading

disability was defined by >= 16 standard point discrepancy between Wide Range

Achievement Test, Revised Reading subtest and WISC-R FSIQ, with WISC-R FSIQ >=

90). If phonological awareness and rapid automatized naming contributed unique

variances, then they were likely independent processes affecting the dyslexic population.

With age, SES, behavioral, and intelligence factors controlled, she found that

phonological awareness (as assessed by Auditory Analysis Test [AAT], a phonemic

deletion test) and rapid naming did contribute unique shares of variance to achievement

scores. Phonological awareness contributed to nonword reading, spelling, and

comprehension. Naming contributed to single-word reading and passage reading speed.







Bowers and Swanson (1991) also conducted regression analyses to examine the same

issue. They found that most variance on nonword reading (after controlling for the

WISC-R Vocabulary score) was explained by the score on the Auditory Analysis Test,

and most variance on a single-word reading test was explained by the score on Odd Word

Out, another phonological awareness measure (a multiple-choice test requiring

identification of the non-rhyming word). In contrast, most variance on comprehension

was explained by rapid automatized naming. A problem with Bowers and Swanson's

(1991) study was that they combined poor readers (n = 19; defined by Woodcock

Reading Mastery Test, Word Identification subtest standard score at or below the 25h

percentile for age) with average readers (n = 19) in their regression analyses. It was

possible that subjects with different reading abilities have different patterns of

relationship between phonological awareness and naming. That is, phonological

processes and naming abilities may contribute differently to the reading achievement

scores of average and disabled readers. The lack of well-controlled studies in this area

rendered the contributions of phonological awareness and naming to reading achievement

equivocal.


Anatomical Evidence of Anomalies


Galaburda and colleagues (Galaburda, Sherman, Rosen, Aboitiz, and Geschwind,

1985; Galaburda, 1989) examined eight post-mortem brains of individuals identified by

the Orton Society as dyslexic. They found abnormal symmetry of the planum temporale

in these eight brains as well as ectopic neurons in the molecular layer of the perisylvian

cortex. The planum temporale lies just posterior to the Heschl's gyrus on the superior







21
surface of the temporal lobe. These and other structures surrounding the Sylvian fissure

compose the language system, which includes reading. The findings ofGalaburda et al.

(1985) suggested that neurodevelopmental abnormalities may contribute to the

symptomatology of dyslexia.

Geschwind and Levitsky (1968) found that among 100 post-mortem samples,

approximately 65% showed a left greater than right plana difference. Approximately

25% had symmetrical plana, and only 10% had a right planum that was larger than the

left. Rumsey, Dorwart, Vermess, Denckla, Kruesi, and Rapoport (1986) measured

temporal lobe volume from magnetic resonance (MR) images and found data consistent

with the results ofGalaburda et al. (1985). Nine of the ten men with documented reading

disability in psychoeducational evaluations from their childhood demonstrated

symmetrical temporal lobes. However, Rumsey et al. (1986) did not measure the planum

temporale specifically. Given the extent of the temporal lobe, it was possible that other

aspects of the temporal lobe contributed to the symmetry rather than the planum

temporale.

In 1990, two independent groups reported on the symmetry of the planum

temporale among dyslexics. Hynd, Semrud-Clikeman, Lorys, Novey, and Eliopulos

(1990) compared plana length and insular length of 10 developmental dyslexic children

with 10 non-dyslexic controls. The average age of their dyslexic children was 10 years,

and dyslexia was defined by normal or better intellectual ability (WISC-R Full Scale IQ

>= 85), reading achievement significantly below their FSIQ (>= 20 standard score points

lower than FSIQ on Woodcock Reading Mastery Test--Revised, Word Attack and

Passage Comprehension subtests), and no co-morbid diagnosis of ADHD. The average







age of their normal controls was 12 years, and they must have normal or better

intellectual ability (WISC-R FSIQ >= 85), no reportedly family history of learning

problems, no significant deficit in achievement, and no reported or observed medical,

educational, social, or emotional difficulties. From MR images, this research group

found that dyslexics have bilaterally shorter insula compared to non-dyslexic controls,

and that 90% of dyslexics have a left planum length that was shorter than their right

planum length. There was no difference between dyslexic and control groups on right

planum length. Dyslexics' overall left planum was shorter than controls' left planum.

They suggested that the nature of plana symmetry in dyslexia was due to a smaller left

planum temporale. Larsen, Hoien, Lundberg, and Odegaard (1990) also examined plana

length from MR images and found symmetrical plana among dyslexic children (n = 19;

dyslexic subjects were identified from a school psychology service and had poor word

recognition in the presence of normal intelligence). However, comparing dyslexics' MR

images to those of age- and intelligence-matched controls' (n = 19), they found that plana

symmetry among dyslexics was due to increased right planum length rather than the

decreased left planum that Hynd et al. (1990) reported.

The above studies did not differentiate between the temporal and parietal banks of

the planum temporale. Leonard, Voeller, Lombardino, Morris, Hynd, Alexander,

Andersen, Garofalakis, Honeyman, Mao, Agee, and Staab, (1993) suggested that

examining the different banks of the planum may explain some of the contradictions in

the literature. They measured the length of these two banks from MR images. Their

subjects were adults previously diagnosed with dyslexia by pediatrician, pediatric

neurologist, or learning disability specialists (n = 9), the dyslexics' biological relatives (n







= 10), and normal controls (n = 12). In contrast to the previous studies, Leonard et al.

(1993) did not find abnormal symmetrical plana in the dyslexic population. All groups

demonstrated a greater left temporal bank compared to the right temporal bank and a

greater right parietal bank compared to the left parietal bank. When only the left

hemisphere was considered, all subjects except two dyslexics had longer temporal bank

than the parietal bank. When only the right hemisphere was considered, the controls also

had longer temporal bank than the parietal bank, but 55% of dyslexics and 40% of

relatives had longer parietal bank compared to the temporal bank. They suggested that

dyslexics had reduced right intrahemispheric asymmetry (between temporal and parietal

banks) compared to controls due to the transfer of planar tissue from the temporal to the

parietal bank.

The same group also examined the structure of the Sylvian fissure. Among

controls, the left Sylvian fissure usually ended in a bifurcation into small ascending and

descending branches. Variations to this typical pattern included no bifurcation and/or

extra gyri in the parietal operculum anterior to the termination of the Sylvian fissure.

There were also variations in the number ofHeschl's gyri present. Normally, there was

one Heschl's gyrus in the left hemisphere that was visible on a mesial section of the MR

image. On more lateral sections, Heschl's gyrus moved anteriorly and dissolved into a

number of convolutions in the superior temporal gyrus. Leonard et al. (1993) found that

every subject with dyslexia showed at least one of the above anomalies. Six (66%) had

bilateral anomalies. Biological relatives had the next highest number of anomalies;

seventy percent had at least one anomaly while 20% had bilateral anomalies. In contrast,

only 17% of control subjects had one anomaly and none had bilateral anomalies. These







24
findings suggested a genetic etiology for dyslexia. The greatest number of anomalies was

found among dyslexics, the group with the next greatest number of anomalies was

biological relatives of the dyslexics, and normal controls without family histories of

dyslexia had the fewest number of anomalies. These anatomical studies indicated that

reading difficulties experienced by dyslexics may have an anatomical basis. However, a

word of caution regarding the Leonard et al. (1993) study is in order. Their subjects were

either professionals or from high functioning professional families. They described their

dyslexic subjects as "recovered dyslexics" who have been able to compensate so well that

there was much overlap between dyslexic and control groups on a measure of

phonological awareness. Thus their dyslexic subjects may not be a representative sample

of the dyslexic population.

Hynd et al. (1990), in addition to finding shorter left planum length among

dyslexic children, also compared the MR images of dyslexics (n = 10) with MR images

of children with attention deficit/hyperactivity disorder (ADHD; n = 10). Their ADHD

subjects had average or better intellectual ability (WISC-R FSIQ >= 85), no reported

family history of learning problems, no significant deficit in reported or measured

achievement, documented behavioral deficits consistent with a Diagnostic and Statistical

Manual of Mental Disorders, Revised Third Edition (DSM-III-R) diagnosis of ADHD,

who responded favorably to stimulant medication. They found that dyslexics and

ADHDs both have smaller right frontal width compared to controls. However, dyslexics'

planum temporale was shorter on the left while ADHDs showed the typical pattern of left

greater than right planum. These findings showed that while frontal anomalies may be







implicated in both groups, anomalies of the planum temporale may be specific to

dyslexia.

Imaging data from cerebral blood flow studies also supported the evidence of

structural anomalies in the dyslexic population. Rumsey, Andreason, Zametkin, Aquino,

King, Hamburger, Pikus, Rapoport, and Cohen (1992) examined cerebral blood flow

differences between dyslexic adults and normal subjects during a rhyming task. Their

dyslexic subjects all had Wechsler Adult Intelligence Scale-Revised (WAIS-R) Verbal

or Performance IQ scores of at least 89 and met DSM-III-R criteria for developmental

reading disorder. All received some special education service while in school. Subjects

were presented word pairs aurally and pressed a button if the word pair rhymed. They

found that dyslexics had decreased activation of left temporal-parietal and midtemporal

areas that corresponded to the angular gyrus and Wernicke's area. This finding of

hypometabolism in temporal parietal and midtemporal areas corresponded well to the

structural abnormalities of planum temporal and Heschl's gyrus reported by Leonard et al.

(1993).

Paulesu, Frith, Snowling, Gallagher, Morton, Frackowiak, and Frith, (1996)

conducted a different rhyming task during a positron emission tomography (PET) study

and found similar results. Their rhyming task involved visual presentation of letters.

Subjects moved a joystick to letters that rhymed with the letter "B." Their subjects were

five dyslexic adults who were university students, postgraduates, or self-employed

entrepreneur, identified from records of a dyslexia clinic, and five education-matched

controls. The non-dyslexic subjects activated left Broca's and Wernicke's areas and the

left insula. Dyslexics showed decreased activation in the left Wernicke's area and a







26
greater decrease in the left insula. On a short-term memory task where subjects judged if

a target letter was present in a previous sequence of English letters, the normals activated

the above areas plus the left supramarginal gyrus. The dyslexics activated the same areas

as the controls except for the left insula. On these two tasks, dyslexics activated the same

major language areas as controls (i.e., Broca's and Wernicke's) while attending to and

judging phonological stimuli. However, they did not activate these areas in concert as

controls. Dyslexics' lack of activation of the insular cortex suggested that the insula was

not necessary for phonological processing. The authors suggested that perhaps the insula

acted as a "bridge" between the Broca's area and the supramarginal gyrus. Though it may

not be necessary for the processing of phonological information, it provided the

connection between posterior and anterior regions. Dyslexics' anatomical anomalies and

their lack of activation of this region during phonological analysis tasks suggested that

disconnection between important regions for phonological analysis may underlie their

problems with phonological processing.


Co-morbidity with Attention-Deficit'Hyperactivity Disorder


While reading disability and ADHD have very different symptoms, they do

overlap much more than one would expect from independent random distributions of

these disorders. Approximately 30-50% of individuals with reading disability have a co-

morbid diagnosis of ADHD (Felton et al., 1987). This high co-morbidity rate has led

researchers to speculate ifattentional problems limit a child's ability to develop

automated processing skills necessary for reading. Felton et al. (1987) aimed to

disentangle the neuropsychological deficits contributed by attention deficit disorder







(ADD) and by reading disability (RD). They formed four groups from two factors, RD

and ADD: RD with ADD, RD with non-ADD, non-RD with ADD, and non-RD with

non-ADD. Using age and receptive vocabulary score as covariates and controlling for

family-wise error rates, Felton et al. (1987) found that RD and non-RD groups differed

on a visual confrontation naming test (BNT) and on a rapid automatized naming test.

There was no main effect of ADD on these measures. The ADD and non-ADD groups

did differ from each other on a test of supraspan verbal memory (RAVLT). In contrast,

there was no main effect of RD on this task. These findings showed that RD and ADD

contributed to different aspects of neuropsychological deficits. If ADD contributed to

impaired reading skills among the RD children, one would expect some overlap of

impaired areas. The findings of Felton et al. (1987) provided indirect support for the

independence of reading disability and attention deficit disorder.


Research Questions


The literature on the dyslexic population indicated that 1) dyslexic individuals

have problems with name retrieval; 2) dyslexic individuals have problems with

phonological processing; and 3) their language difficulties likely have an anatomical

basis. This combination of findings rendered the phonological dyslexic population to be

of special interest to this study, because the anatomical areas identified to be abnormal

(i.e., Wernicke's area) were also implicated by the articulatory feedback hypothesis of

naming. This hypothesis posited that articulatory awareness facilitates naming.

Presumably, sensory feedback received by the primary sensory cortex from articulators

has connectivity with both the Wemrnicke's area and Broca's area. This connectivity






28
allows for articulatory feedback to trigger phonological representations of object names,

and to trigger motor patterns to execute the articulation of those names. Phonological

dyslexic subjects and normal readers should yield a range of naming abilities by which to

examine articulatory knowledge and the relationship between name retrieval and

articulatory knowledge. The aim of this study was to test the articulatory feedback

hypothesis of naming. To achieve this aim, the following questions were asked:


What Is the Correlation Between Articulatory Knowledge and Naming?


If articulatory knowledge and naming are related, better articulatory knowledge

should be associated with either faster name retrieval latency or better name retrieval

accuracy. This relationship should hold for all subjects. Reading achievement status

may put subjects at the lower end of the continuum of naming ability. If articulatory

feedback facilitates naming for all subjects, dyslexics' naming ability will be correlated

with their articulatory knowledge in the same manner as normal readers.


Do Dvslexics Have Worse Articulatory Knowledge?


A secondary aim of this study was to replicate Montgomery's (1981) finding that

dyslexics have impaired articulatory knowledge. This study differed from Montgomery's

(1981) study in some respects. One, it was unclear how Montgomery's dyslexic subjects

were defined. This study included only those who have impaired phonological skills as

measured by impaired grapheme-to-phoneme conversion. Second, because

Montgomery's (1981) version of the articulatory awareness test was unavailable, the







present study used an alternative but similar version of the test, which was based on

Montgomery's task.


Is There Support for the Articulatory Feedback Hypothesis of Naming?


Prediction for individuals with adequate articulatorv knowledge. The articulatory

feedback hypothesis of naming stated that having articulatory feedback appropriate to

naming facilitates name retrieval. This study tested this via an interference experimental

design. If having articulatory feedback appropriate to naming facilitates name retrieval,

interfering with that appropriate articulatory feedback should reduce facilitation effects.

Prediction for individuals with impaired articulator knowledge. The hypothesis

implied that those with poor articulatory knowledge will retrieve names less efficiently

than those with good articulatory knowledge. In an interference paradigm, where

subjects were asked to engage in another task that produced articulatory feedback

incompatible with the naming task at hand, those with poor articulatory knowledge were

predicted to perform differently than controls. Whereas the controls' naming should be

de-facilitated, those with poor articulatory knowledge may respond in one of two ways.

One, because they may not rely in the articulatory feedback system to facilitate name

retrieval in the first place, interfering with articulatory feedback may not produce de-

facilitation effects as expected with controls. Or possibly, because their articulatory

feedback system was already poor, adding another task with demands on the articulatory

feedback system may exacerbate the difficulty these subjects experience, leading to even

worse naming performance than controls' de-facilitated naming performance.







30
Group differences. If Montgomery's (1981) finding is supported and those with

phonological impairments have worse articulatory knowledge compared to controls, then

performance of phonologically impaired subjects can be compared to the performance of

controls. Even if Montgomery's finding is not supported, there is theoretical interest in

comparing the articulatory knowledge of these two groups as it has been well

documented that dyslexic individuals have name retrieval difficulties.

Those with phonological impairment can be further divided into two subgroups:

those with impaired reading skills and those with adequate reading skills. Performance of

these two subgroups can be compared to examine if these subtypes show different

patterns on articulatory knowledge and name retrieval, or if they differ only in the degree

of severity.

To test the hypothesis most directly, subjects can be grouped according to their

performance on a measure of articulatory knowledge. These three ways of grouping

subjects (i.e., phonologically impaired vs. controls; phonologically and reading impaired

vs. phonologically impaired with adequate reading vs. controls; poor articulatory

knowledge vs. adequate articulatory knowledge) may yield performance patterns that

further elucidate the relationship between articulatory knowledge and name retrieval.


What Is the Relationship Between Articulatory Knowledge and Phonological Awareness?


Another secondary aim of this study was to elucidate the relationship between

articulatory knowledge and phonological awareness. Much about phonological

awareness among the dyslexic population has been studied, but little is known about

articulatory knowledge. Are they related or independent of one another? What are the







31
factors that relate to or predict the level of articulatory knowledge and phonological

awareness?













METHODS


Subjects


Three groups of subjects totaling 41 children were recruited from the Gainesville,

Florida and Chicago, Illinois metropolitan areas. Subjects were recruited from offices of

psychologists, speech pathologists, and neurologists, and from flyers distributed

throughout the community. All subjects' parents gave written informed consent and all

subjects gave oral assent to participate in this study in accordance with the requirements

of the Institutional Review Board of the Health Science Center of the University of

Florida and of the University of Chicago Hospitals.

Inclusionary criteria for subjects included:

Age 7-12

Right-handed

Intelligence quotient between 70 and 130

English is first and primary language

A lower limit of 7 years of age was selected because reading disability is often not

apparent until school age; a large percentage of children have age-appropriate, limited

reading skills before that time. An upper age limit of 12 was selected because beyond

this age, children with developmental phonological dyslexia have had several years of

struggling with reading. They may have received special services or developed other

skills on their own in order to compensate for their impaired reading. While older

32







phonological dyslexic children may still demonstrate naming problems, their retrieval

deficit is often mitigated by late adolescence (Korhonen, 1995; Fawcett & Nicolson,

1994; Felton et al., 1990). Forms of compensation may confound the contribution of

articulatory feedback to name retrieval. The age range was limited between 7-0 and 12-

11 in order to include individuals in the early years of developmental phonological

dyslexia. Right-handedness was selected as a predictor of typical language organization

so that results from subjects in this study can be generalized to the population.

Approximately 98% of right-handed individuals are left hemisphere dominant for

language. The intelligence criterion was constrained to the middle 96% of the

population. Individuals at the extremes of the continuum may not process linguistic

information in the same way as most individuals. The intelligence criterion was set so

that results can be generalized to the population. English was required as subjects' first

and primary language in order to rule out reading problems due to socio-cultural or

environmental factors.

Exclusionary criteria included:

History of neurological disorders

Previous treatment in Lindamood or Orton-Gillingham programs

Family history of learning disability

A history of neurological disorders, such as cerebral palsy, epilepsy, or Tourette's

Syndrome, increases the probability of atypical brain organization. Therefore individuals

with neurological histories were excluded. Individuals who have participated in reading

treatment programs described as or based on the Lindamood or Orton-Gillingham

programs were also excluded. These treatment programs include direct or indirect







training of articulatory awareness via training of articulatory gestures. A history of

participation in these programs may confound results because subjects' articulatory

feedback system may no longer reflect its naturalistic connectivity. Family members of

individuals with a learning disability were also excluded because of anatomical studies

suggesting a genetic basis to learning disability (Leonard et al., 1993).

Subjects meeting criteria for the following three groups were recruited:

Developmental phonological dyslexia (DPD)

Adequate reader with poor phonology (ARPP)

Normal reader controls (CTRL)

Group membership was distinguished by performance on three reading

achievement subtests of the Woodcock Reading Mastery Test (WRMT; Woodcock,

1987): Word Identification, Passage Comprehension, and Word Attack. The Word

Identification subtest consisted of single English words that subjects were asked to read.

This subtest assessed subjects' oral reading of real words, but no comprehension of word

meaning was required. The Passage Comprehension subtest consisted of a short sentence

or paragraph with one missing word. Subjects were required to read the entire passage

and come up with one word that would fill the missing blank. This subtest assessed

subjects' comprehension of written material. The Word Attack subtest consisted of

nonwords that followed the rules of pronunciation in the English language. Subjects

were required to read these words aloud. This subtest required subjects to use the

grapheme-to-phoneme conversion route to read. Raw scores were converted to age-

corrected standard scores for each of these three measures.






35
Subjects in the DPD group had impaired reading achievement scores on all three

subtests in comparison to that expected given their intellectual aptitude, as assessed with

the Test of Nonverbal Intelligence, 2nd Edition (TONI-2; Brown, Sherbenou, & Johnsen,

1990). Impairment was operationalized as actual achievement score falling at least one

standard deviation (i.e., 15 standard score points) below the expected score, with the

expected score calculated using the formula

Y' = [r,. (S, / Sy XIQ X)] + Y

(Y' = expected achievement score, r,. = estimated correlation between the TONI-2 and

the WRMT, S, = standard deviation of WRMT [15], Sy = standard deviation of TONI-2

[15], IQ = obtained TONI-2 Quotient, X = mean of TONI-2 [100], and Y = mean of

WRMT [100]).

Subjects in the ARPP group had impaired phonological skills, operationalized by

impaired actual Word Attack score in comparison to the expected score based on TONI-2

Quotient, but non-impaired reading skills as defined by commensurate actual and

expected Word Identification and Passage Comprehension scores. These subjects, as

subjects in the DPD group, were recruited as poor readers. The categorization into DPD

or ARPP groups was done after each subject's completion of participation.

Subjects in the CTRL group were matched to the other two groups on age and

intelligence. The CTRL group's expected reading achievement scores based on

intellectual aptitude were all commensurate with actual achievement scores. The CTRL

group was not matched to the other two groups on reading age because the primary

purpose of this study was to evaluate name retrieval ability. The ability to retrieve names







36
may be affected by age and intelligence. Thus all three groups were matched on age and

intelligence. Table 1 summarized the grouping criteria.

Table 1. Summary of grouping criteria._
Group Word Attack Word Identification Passage Comprehension

DPD ASS < ESS ASS < ESS ASS < ESS

ARPP ASS < ESS ASS = ESS ASS = ESS

CTRL ASS = ESS ASS = ESS ASS = ESS

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; ASS = actual standard score; ESS = expected
standard score based on the formula, Y' = [ry (S, / Sy )(IQ X)] + Y; IQ = TONI-2
Quotient.


The TONI-2 was selected as the measure of intellectual ability for this study

because of its relative lack of dependence on verbal abilities. A problem with measuring

dyslexic children's general intelligence is that most common measures of intelligence

rely heavily on verbal abilities. Because dyslexic children have impaired reading skills,

they may obtain intelligence scores that are lower than their "true" intellectual capability.

To minimize this problem, intelligence quotient of the TONI-2 was used as the measure

of intelligence for this study. The TONI-2 stimuli consisted of visual patterns with a

missing piece. Subjects were required to choose a pattern to fit the visual sequence from

multiple choices. While good performance on this test may still benefit from

verbalization, the TONI-2 has less verbal demands than most other measures of

intelligence. The TONI-2 can be used with subjects from age 5 to 85. Normative data

was collected from over 2,700 subjects in these age ranges. The TONI-2 Form B's

correlation with the WISC-R FSIQ ranged from .75 to .94. Its correlation with WISC-R






37
VIQ ranged from .63 to .73, and its correlation with WISC-R PIQ ranged from .60 to .87.

Form B was selected to be used in this study because of its relatively more stable

correlation with WISC-R indices compared to Form A. Raw scores on the TONI-2 were

converted to age-corrected TONI-2 Quotients, which have a mean of 100 and a standard

deviation of 15.

Table 2 summarized each group's demographic data and grouping criteria scores.

All three groups were matched on age (F = 0.35,p = 0.71), intelligence score (F = 1.50,p

= 0.24), and grade (F = 0.74,p = 0.48). More males were represented in the DPD group

in comparison to the other two groups. The three groups did differ from each other on

the three reading achievement subtests (F = 7.30, p = 0.00). For each of the three reading

achievement subtests, the DPD group scored uniformly lower than the other groups

(Word Attack: DPD vs. ARPP, t = 2.67, p = 0.02, DPD vs. CTRL, t = 9.65, p = 0.00;

Word Identification: DPD vs. ARPP, t = 4.86,p = 0.00, DPD vs. CTRL, t = 9.36,p =

0.00; Passage Comprehension: DPD vs. ARPP, t = 6.48, p= 0.00, DPD vs. CTRL, t =

8.10, p = 0.00). The ARPP group scored lower than the CTRL group on Word Attack (t

= 7.14,p = 0.00) and Word Identification (t = 4.60,p = 0.00), but not on Passage

Comprehension (t = 1.99,p = 0.06). Within the DPD and CTRL groups, there was no

difference between any of the three achievement scores (DPD, F = 2.82, p = 0.11; CTRL,

F = 1.24,p = 0.32). The ARPP group, however, demonstrated better Passage

Comprehension compared to Word Identification (t = 7.40, p = 0.00), which in turn was

better than Word Attack (t = -8.71, p = 0.00).







Table 2. Summary of demographics and grouping criteria scores.
DPD ARPP CTRL
. . . ...... ..... .. .. ..... .... ....... .. ....,. -. .... ..... .. ..... .. .. ..... ..- .-.- .. ... .. .... . .
N 11 10 20

Age 9.0(1.1) 9.4(1.9) 9.5(1.7)

Grade 3(1) 4(2) 4(2)

M:F Ratio 9:2 6:4 12:8

ADHD M:F Ratio 5:1 3:0 3:0

TONI-2 IQ 103(13) 110(7) 106(8)

Word Attack SS 70(11)8 81 (8)b 104 (8)c

Word Identification SS 68 (12)a 89 (7)d 105 (10)c

Passage Comprehension SS 70 (12)Y 96 (5)C 103 (10)Y

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls. Scores with different superscripts indicate
statistically significant difference.


Descriptive Measures


Subjects were administered a battery of relevant tests to compare performance

between groups and for comparison with other findings in the literature. Data from the

following descriptive measures were used to describe the groups on relevant

neuropsychological variables.


Articulatory Awareness Test (AAT)


The Articulatory Awareness Test (AAT) is a non-published, experimental

instrument modeled after Montgomery's (1981) task. The AAT stimuli consisted of eight

picture cards with cartoon drawings of the sagittal view of the oral cavity (see Appendix







1). Each picture represented one or more phoneme. The examiner produced a target

phoneme with her mouth obstructed from subject's view, then asked the subject to

identify one of out three pictures that corresponds to subject's articulatory gesture as s/he

produced that phoneme. Subjects were encouraged to repeat the target phoneme as many

times as necessary, and no time limit to responding was imposed. Three practice items

were given before test items were administered, and the examiner went over a sagittal

cartoon drawing to identify each articulator (i.e., tongue, teeth, lips) at the introduction of

the task. In the event that subject produced an atypical articulatory gesture in producing a

phoneme, the subject's gesture was used in scoring accuracy.

The AAT was produced by the Morris Center of Gainesville, Florida and used as

part of their standard evaluation for reading disability. The AAT consisted of 10 trials,

with 10 additional trials added and piloted during this study (AAT-R). Data on the

original 10 trials were available from 93 patients from the Morris Center. Seventy

percent of these subjects were male (n = 65). Thirty percent were female (n = 28).

Patients' ages ranged from 6 to 22, with an average of 11 years (standard deviation = 4).

Of these 93 subjects, 86 were classified as dyslexic, 7 were classified as borderline

dyslexic; 81 of these subjects were diagnosed with co-morbid ADHD. Table 3 shows

group means on the AAT (out of possible 10 trials). Dyslexic subjects performed more

poorly on the AAT than borderline dyslexics (t = 2.03,p = 0.04). AAT scores did not

differ between ADHD and non-ADHD groups (t = 1.34, p = 0.18). Because the Morris

Center population did not include normal readers, it was not known whether including

normal readers would yield a bimodal distribution of AAT scores, as Montgomery (1981)

showed with her version of this task. The data available from the Morris Center







40
suggested a normal distribution of scores on the AAT from the dyslexic population. This

test was administered to all subjects in the present study to estimate subjects' level of

knowledge about articulator position during phoneme production. The number of

accurate responses in 10 trials (AAT) and in 20 trials (AAT-R) was recorded for analysis.


Table 3. AAT scores of the Morris Center population.
n Mean AAT score (SD)

Dyslexic 86 7 (2)

Borderline Dyslexic 7 8(1)



ADHD 81 7(2)

Non-ADHD 12 8(1)



Total 93 7(2)



Naming


Subjects were administered the Boston Naming Test (BNT; Kaplan, Goodglass,

Weintraub, & Segal, 1983) as a measure of visual confrontation naming. Z-scores

calculated from the norms published by Spreen and Strauss (1998) were recorded for

analysis. Subjects were also administered the Rapid Color Naming and Rapid Object

Naming subtests of the 1997 experimental version of the Comprehensive Test of

SPhonological Processing, which was same as the 1999 published version of the same test

(Wagner, Torgesen, & Rashotte, 1999). Z-scores based on normative data collected in

1997 by the research group developing this battery were calculated and recorded for
Ns







analysis. These measures assessed subjects' rapid naming ability and allowed for

comparison of data from the present subject groups to other findings reported in the

literature.


Phonological Awareness


The Lindamood Auditory Conceptualization Test (LAC; Lindamood &

Lindamood, 1979) was administered to all subjects to yield an index of phonological

awareness. This test assessed subjects' phonological awareness by asking subjects to

manipulate color blocks, with each color representing one phoneme. Phoneme patterns

changed in degrees of difficulty, and subjects manipulated blocks to demonstrate their

perception of how phoneme patterns changed. Raw scores from the LAC were recorded

for analysis.


Attention-Deficit/Hyperactivity Disorder


Subjects' parents were interviewed using a semi-structured interview for

symptoms of ADHD based on DSM-IV criteria. This questionnaire asked about each

symptom listed in the DSM-IV, and if the parent endorsed six or more symptoms of

either the inattention and/or hyperactivity/impulsivity cluster, follow-up questions about

age of onset, duration of symptoms, situations where symptoms are exhibited, and extent

of symptoms' disturbance on functioning were asked. Based on parents' response to this

questionnaire, subjects were categorized into ADHD or Non-ADHD groups. Because of

the high co-morbidity rate between dyslexia and ADHD, this interview allowed for the







description of ADHD rate in the current study groups. The form used during this

interview is presented in Appendix 2.


Experimental Measures


The aim of this study was to test the hypothesis that articulatory feedback

facilitates naming. According to this hypothesis, feedback from appropriate articulator

movements facilitates name retrieval. An interference paradigm was implemented to test

this hypothesis. While subjects attempted to name objects, they were asked to engage in

another task designed to interfere with articulatory movements appropriate to the naming

task.


Naming Assessed via Phoneme Match (NAPM)


During the experimental task, NAPM, subjects were required to look at two

pictures, name those pictures to themselves, and determine if those names end in the

same phoneme. They engaged in two interference conditions while performing this

naming task. During the Mouth Interference condition, subjects were asked to engage

their mouth in the following movement sequence: Lips together (as if making the /m/

sound)-tongue between teeth (as if making the /th/ sound). These movements were

demonstrated without accompanying phonemic sounds. Because subjects' articulators

were engaged in this interference movement, they could not orally name the objects seen.

Therefore naming was assessed by asking subjects to decide if the names of the two

objects seen during each trial terminated in the same sound. They indicated their






43
response by pressing designated buttons. In order to perform this task, subjects must first

name the two objects, then judge if those names have matching end phonemes.

During the Foot Interference condition, subjects were asked to move their left foot

in a rocking movement alternating between heel and toe, while naming pictures and

deciding if the names' end phoneme matched. This condition was implemented to control

for the attentional demands of engaging in an interference task. Subjects engaged in

Mouth Interference while performing the NAPM task during half of the trials and

engaged in Foot Interference during the other half of the trials. The order of the

interference condition was counterbalanced across subjects.

Each interference condition consisted of 32 trials. Half of the trials had word

pairs with matching end phonemes and the other half had word pairs with non-matching

end phonemes. The 64 word pairs were divided into two stimulus sets and are presented

in Appendix 3. The two stimulus sets were balanced on word frequency (Francis &

Kucera, 1982), number of syllables, and grade level by which the word is taught

(Thomdike & Lorge, 1972). Simple black and white line drawings eliciting each target

were drawn from the Snodgrass and Vanderwart picture set (1980), Peabody Picture

Vocabulary Test (Dunn & Dunn, 1981), and Boston Naming Test (Kaplan et al., 1983).

In some instances, a lack of available drawings necessitated the use of locally produced

drawings, which were produced to be of similar visual complexity level as pictures from

above mentioned sets. Each stimulus set was used during Mouth Interference half the

time and during Foot Interference half the time.

These stimuli were presented to subjects on a laptop computer via a program

written with PsychLab v.6.0.2. Each trial began with a fixation mark lasting 1000 msec.






44
Then an auditory cue alerted subjects to the onset of pictured stimuli, which remained on

the screen until subjects pressed one of two acceptable keys. The computer recorded

subjects' response and reaction time from the onset of the stimuli presentation to key

press. The examiner monitored subjects' interference movement and recorded the

number of movement cycles completed during each trial. One movement cycles during

the Mouth Interference was defined as lip closure followed by intrusion of the tongue

between teeth. One cycle during the Foot Interference was defined as toe touching the

floor followed by heel touching the floor. A different auditory cue followed subjects'

response and marked the end of a trial. The screen then remained blank until the

examiner pressed one of two keys marking that trial as valid or invalid. No time limit on

response time was imposed. Because the next trial did not begin until the examiner

pressed one of two keys, the examiner controlled the pace of the testing and implemented

breaks as appropriate for each subject.

The NAPM began with six practice trials, during which subjects performed the

NAPM task without any interference. Each interference condition (Mouth and Foot

Interference) began with a demonstration of the interference task, followed by four

practice trials with interference. Subjects were instructed to engage in the interference

movement before the onset of each trial. After the practice trials, subjects were informed

that testing will begin. The first two trials were used as buffer trials (i.e., data were not

recorded) without subjects' knowledge. The 32 experimental trials followed. Data

recorded during each trial included response reaction time, response (to calculate

accuracy percentage), and number of interfering movements produced (for calculating







45
average frequency of movement). Time taken to complete the NAPM ranged between 15

to 20 minutes.


Visual Match


A visual match task was implemented to control for potentially different

attentional demands of Mouth and Foot Interference. This was a nonverbal, visual match

task requiring subjects to determine if one of four pictures matched a target. Subjects

engage in Mouth and Foot Interference during this task as well. If subjects' performance

during the Mouth and Foot Interference conditions differed on this non-verbal task, that

would suggest that mouth movements and foot movements have different levels of

interfering effect.

Similar to the NAPM, Visual Match also had 32 trials for each interference

condition. Each trial composed of one target picture at the top of the screen and four

other pictures at the bottom of the screen. Subjects were instructed to press one of two

keys indicating whether there was a match between the four pictures on the bottom and

the target on top. Half of the trials had matching pictures and the other half had non-

matching pictures. Pictures were taken from the Test of Visual-Perceptual Skills (non-

motor)-Revised (Gardner, 1996), and were selected for their difficulty to be verbalized.

One set of stimuli was constructed first. The second set was constructed by changing the

target and/or the ordering of the four pictures on the bottom.

The Visual Match Test was presented to subjects on a laptop computer via a

program written with PsychLab v.6.0.2. Experimental parameters mirrored the NAPM

parameters as much as possible. Each trial began with a fixation mark lasting 1000 msec.







46
Then an auditory cue alerted subjects to the onset of pictured stimuli, which remained on

the screen until subjects pressed one of two acceptable keys. No time limit to response

was imposed. The computer recorded subjects' response and reaction time. The

examiner recorded the number of interference movement cycles completed during each

trial. A different auditory cue followed subjects' response and marked the end of a trial.

The screen then remained blank until the examiner pressed one of two keys marking that

trial as valid or invalid.

Similar to the NAPM, the Visual Match Test also began with six practice trials,

during which subjects performed the Visual Match task without any interference.

Interference conditions (Mouth and Foot Interference) then followed. Each condition

began with a demonstration of the interference task, followed by four practice trials with

interference. Subjects were instructed to engage in interference movement before the

onset of each trial. After the practice trials, subjects were informed that testing will

begin. The first two trials were used as buffer trials (i.e., data were not recorded) without

subjects' knowledge. Thirty-two experimental trials followed. The order of the

interference conditions was counterbalanced across subjects. Data recorded during each

trail included response reaction time, response accuracy, and the number of interfering

movements produced (for calculating average frequency of movement). Time taken to

complete the Visual Match Test ranged between 15 to 25 minutes.


Phoneme Match


A Phoneme Match Test was implemented to control for the potential difference in

subjects' ability to determine if the end phoneme of word pairs matched. This was a







necessary control given that subjects' naming performance during the NAPM was

measured via their ability to determine matching phonemes. Subjects completed this task

without any interference. Stimuli were two sets of word pairs used during the NAPM.

These stimuli were presented aurally to subjects on a laptop computer via a program

written with PsychLab v.6.0.2. Each trial began with the word "listen," which stayed on

the screen until the end of the trial. A pair of words was presented by the computer 1500

msec after the onset of the word "listen." Subjects pressed one of two keys to indicate if

the two words' last phonemes matched. The computer recorded subjects' response and

reaction time from the onset of stimulus presentation. The screen then remained blank

until the examiner pressed one of two keys marking that trial as valid or invalid. No time

limit on response time was imposed.

The Phoneme Match Test included four practice trials followed by the two

stimulus sets, which totaled 64 trials. The order of the two stimulus sets was

counterbalanced across subjects. Data recorded during each trial included response

reaction time and response accuracy. Time taken to complete the Phoneme Match Test

ranged between 5 to 10 minutes.


Naming Test


A visual confrontation naming test was administered after the completion of the

NAPM, Visual Match, and the Phoneme Match Test. This Naming Test composed of

black and white line drawings from the NAPM. Each subject was asked to say the name

that they assigned the pictured item when they saw it during the NAPM task. For cases

where subjects stated an acceptable alternative response for an item (e.g., "bunny" for







48
"rabbit"), the name that the subject gave was used to determine if the two items from that

NAPM trial had names with matching end phonemes, and the subject's response

accuracy for that trial was determined accordingly. For cases where subjects were not

able to produce a response because of unfamiliarity with the object, the NAPM trial

including that object was deleted. Thus response to objects for which subjects were

unfamiliar was not included in data analysis.


Procedures


The examiner first interviewed the parent over the telephone to obtain each

subject's background information and to screen for ADHD. Based on this information,

subjects were assigned a subject number using the chart represented in Table 4. This

chart counterbalanced the order of task presentation (i.e., NAPM or Visual Match), the

order of stimulus sets used, and the order of interference conditions. Each subject's order

of test presentation, stimulus set used, and order of interference condition was determined

based on his/her assigned subject number.

Subjects completed the testing in either one or two settings, totaling 1.5 hour for

older normal readers to 3 hours for younger subjects with reading problems. Factors

influencing whether testing was completed in one or two settings included each subject's

time availability and their performance during the first hour. For those subjects who

experienced difficulty, testing was completed in two sessions to minimize their

frustration. Subjects were encouraged and praised for their effort rather than for their

accuracy. In no instance were subjects given feedback about the accuracy of their







49
Table 4. Sample of the chart for determining the order of task, interference, and stimulus
set for each subject.
Controls Phonologically Impaired

Order of NAPM Visual Match NAPM Visual Match

stimulus set first first first first

AB 1001 1002 2001 2002 Mouth

BA 1003 1004 2003 2004 Interference

AB 1005 1006 2005 2006 first

BA 1007 1008 2007 2008

AB 1009 1010 2009 2010
BA 1011 1012 2011 2012 Foot

AB 1013 1014 2013 2014 Interference

BA 1015 1016 2015 2016 first

AB 1017 1018 2017 2018

BA 1019 1020 2019 2020



response. The order in which testing components were completed is represented in Table

5.

The Test of Phonological Awareness (Torgesen & Bryant, 1994) was used to train

subjects to match end phonemes of words, thus familiarizing them to the task demand of

the NAPM. Subjects' performance on this task was not scored. The examiner remained

with subjects throughout testing. During the NAPM and Visual Match tasks, the

examiner monitored subjects' interference movements (mouth or foot) and reminded







Table 5. Order of test administration.
Telephone interview:

Demographic Questionnaire

ADHD Questionnaire

Testing session:

Test of Phonological Awareness

NAPM and Visual Match

Mouth and Foot Interference

Phoneme Match Test

Naming Test

Boston Naming Test

Articulatory Awareness Test

Lindamood Auditory Conceptualization Test

Woodcock Reading Mastery Test:

Word Identification

Word Attack

Passage Comprehension

Rapid Color Naming and Rapid Object Naming



subjects to engage in these movements during instances when they stopped. These parts

of the session were videotaped.













RESULTS


The statistical program, SPSS v7.5.2 for Windows, was used to analyze the

following data. Both means and medians were examined as central tendency statistics for

reaction time data (i.e., NAPM, Visual Match, Phoneme Match). Because reaction times

are subject to floor effects and have unlimited ceilings, distribution of scores may be

skewed and means may be overly influenced by extremely slow reaction times. Thus

extreme scores were trimmed in the following way in calculating means for each subject.

For each subject's performance in each condition (i.e., NAPM Mouth Interference,

NAPM Foot Interference, Visual Match Mouth Interference, Visual Match Foot

Interference, Phoneme Match Set A, Phoneme Match Set B), mean reaction time and

standard deviation were calculated. Extreme scores that lay outside of two standard

deviations from the mean were excluded, yielding a trimmed mean for each condition for

each subject. There was no difference in the pattern of results using trimmed means and

medians. Thus results reported here were based on trimmed means.


Articulatorv Knowledge


The Articulatory Feedback Hypothesis of Naming stated that articulatory

feedback facilitates naming. This hypothesis implied that better articulatory knowledge

should be correlated with faster reaction time on a naming test. This was tested using a

regression analysis with performance on the Articulatory Awareness Test as the






52
independent variable and reaction time on experimental measures as dependent variable.

The experimental measures considered here included the NAPM and Visual Match,

Mouth and Foot Interference conditions. The Visual Match was included as a non-verbal

control task. The hypothesis predicted a significant correlation between Articulatory

Awareness Test scores and NAPM, a name retrieval task, but not with Visual Match, a

nonverbal control task. The two interference conditions of the NAPM were predicted to

have different correlations with articulatory knowledge because subjects were able to use

appropriate articulatory feedback in one condition (i.e., Foot Interference) but not in the

other (i.e., Mouth Interference). In the condition where subjects were able to use

appropriate articulatory feedback (i.e., Foot Interference), better articulatory knowledge

was expected to be associated with faster naming time. In the condition where subjects

were not able to use articulatory feedback (i.e., Mouth Interference), reaction time was

expected to be slow; thus a non-significant correlation between articulatory knowledge

and naming time may be seen.

The Pearson correlations between reaction time on experimental measures and

scores on the Articulatory Awareness Test (10-item version) and the Articulatory

Awareness Test-Revised (AAT-R, 20-item version) were reported in Table 6. Reaction

time during the two interference conditions of the NAPM were either significantly

correlated with or approaching significance with the AAT (Mouth F = 4.52,p = .04; Foot

F = 3.48,p .07) and the AAT-R scores (Mouth F = 5.64,p = .02; Foot F = 3.74,p =

.06), whereas there was no relationship between reaction time on the Visual Match Test

and articulatory knowledge (AAT: Mouth F = 0.21,p = .65; Foot F = 0.92,p = .34; AAT-

R: Mouth F = 0.23,p = .64; Foot F = 0.37,p = .55). This pattern indicated that






53
increasing articulatory knowledge was associated with faster reaction time during name

retrieval but not during a nonverbal visual match task. However, the prediction that

articulatory knowledge would be more highly correlated with naming latency during the

Foot Interference than during the Mouth Interference condition was not supported.


Table 6. Pearson correlations between the Articulatory Awareness Test (AAT) score and
reaction time on experimental measures.
AAT AAT-R

NAPM

Mouth Interference -.32* -.36

Foot Interference -.29 -.30+

Visual Match

Mouth Interference .07 .08

Foot Interference .15 .10

Note: p < .05, +p <.10.


Phonologicallv Impaired vs. Controls


Montgomery (1981) found a difference in articulatory knowledge between

dyslexic children and normal readers. To examine if that finding can be replicated

among the present subjects, the difference in articulatory knowledge between normal

readers and phonologically impaired readers was examined. The Phonologically

Impaired (PI) group was composed of the DPD and ARPP subjects.







Articulatory Awareness Test


Scores obtained by PI and CTRL groups were reported in Table 7. A t-test of

independent samples was utilized to test for significant differences between the two

groups. Unlike Montgomery's (1981) finding, there was no difference between PI and

CTRL groups on articulatory knowledge as assessed by the 10-item version of the AAT (t

= 0.80,p = .43) or by the 20-item version (AAT-R, t = 0.64,p =.53). -


Table 7. AAT and AAT-R scores obtained by Phonologically Impaired (PI) and Control
(CTRL) groups.
PI CTRL

AAT 6.19(2.27) 6.70(1.75)

AAT-R 12.14 (3.55) 12.85 (3.53)



Although no difference was found between groups on articulatory knowledge

measures, it was possible that subjects with phonological impairment may demonstrate a

different pattern of relationship between articulatory knowledge and name retrieval

compared to controls. Thus Pearson correlations between the AAT score and reaction

time during each experimental condition (i.e., NAPM and Visual Match Mouth and Foot

Interference conditions) were examined for each group. Because the AAT and AAT-R

scores yielded similar pattern of results, only AAT scores were reported.

Table 8 showed Pearson correlations between the AAT score and reaction time

during experimental conditions for each group. The correlation between the articulatory

knowledge score and naming latency (i.e., NAPM) seen in the analysis with all subjects

combined was driven by the control subjects (Mouth, F = 5.31,p =.03; Foot, F = 2.53,p






55
= .13). The speed by which PI subjects retrieved names was not related to their level of

articulatory knowledge (Mouth, F = 0.65,p = .43; Foot, F = 0.84,p = .37). Neither

groups' AAT score was correlated with their performance on the visual match control.

The control group's correlation between articulatory knowledge score and naming latency

during Mouth Interference differed from their correlation between articulatory knowledge

and visual match latency during Mouth Interference (-0.48 vs. 0.19; Z(2o,20) = -2.07, p <

.05; subscripts denote the sample size of groups being compared). No other pairs of

correlation differed from each other.


Table 8. Pearson correlations between the AAT score and reaction time on experimental
measures for PI and CTRL groups._
PI CTRL

NAPM

Mouth Interference -.18 -.48*

Foot Interference -.21 -.35

Visual Match

Mouth Interference -.03 .19

Foot Interference .06 .27

Note: PI = Phonologically Impaired; CTRL = Controls; p < .05.


Descriptive Measures


Subjects' performance on descriptive measures were reported in Table 9. Scores

were compared by t-test of independent samples. On measures of naming, the PI group

performed more poorly on the BNT (t = 2.05, p = .05), but not on Rapid Color Naming (t

= 1.54,p = .13), Rapid Object Naming (t = 1.47, p =. 15), or on the experimental Naming
Is>







Test (t = 1.58,p = 12). The PI subjects scored significantly lower than the CTRL

subjects on the LAC, our measure of phonological awareness (t = 3.12,p = .00). The

TONI-2 and reading achievement scores were also reported to contrast between PI and

CTRL groups. The PI did not differ from the CTRL group on intellectual aptitude as

measured by the TONI-2 (t = -0.14,p = .89), but their reading achievement scores were

all worse than their age- and intelligence-matched controls (Word Attack, t = 9.28, p =

.00; Word Identification, t = 7.04,p = .00; Passage Comprehension, t = 4.87,p =.00).


Table 9. PI and CTRL groups' performance on descriptive measures.
PI CTRL
BNT -1.96 (2.07)' -0.85 (1.30)

Rapid Color Naming' -0.58 (1.57) 0.02 (0.68)

Rapid Object Naminga -0.88 (2.36) -0.05 (0.80)

Naming Test 87(6) 90(6)

LACc 55(17)e 75(23)'

TONI-2d 106(11) 106(8)

WRMT Word Attackd 75 (11)e 104 (8)

WRMT Word Identificationd 78 (14): 105 (10)'

WRMT Passage Comprehensiond 83 (16)' 103 (10)f

a b
Note: PI = Phonologically Impaired; CTRL = Controls; Z-scores. b Percentage correct.
' Raw score. d Age-corrected standard scores. Within each row, numbers with different
superscripts were significantly different from each other.







Experimental Measures


Phoneme match. Before group differences on the NAPM were further examined,

subjects' ability to match phonemes was evaluated first. Name retrieval on the NAPM

was assessed via subjects' ability to match the end phoneme of words. Thus it was

important to know if groups differed from each other on this ability. A multivariate

analysis of variance (MANOVA) was performed on the reaction time data from the

Phoneme Match Test with Group (PI vs. CTRL) as a between-subject variable and

Stimulus Set (A and B) as a within-subject variable. This analysis also allowed for

examination of differences between stimulus sets. Mean reaction time in milliseconds

and standard deviations were presented in Table 10. The Group X Stimulus Set

interaction was significant (F = 5.86, p = 0.02). Within each group, reaction times for the

two stimulus sets were compared using dependent samples t-test. The CTRL group's

reaction time on stimulus sets did not differ (t = 0.59, p = .56) while PI subjects were

faster in responding to Set A than to Set B (t = -2.51, p = .02). The group reaction time

for each stimulus set was compared using independent samples t-test. The two groups'

reactions times did not differ for Set A (t = -0.87,p = .39). The CTRL group was faster

than the PI group on Set B (t = -2.08, p = .04). A similar analysis was conducted on

response accuracy. This revealed a marginally significant Group effect (F = 3.57,p =

.07). The CTRL group tended to be more accurate than the PI group.

The two stimulus sets did not differ for the CTRL group, but for the PI group, Set

B was responded to more slowly and therefore it may have been harder. The most

important finding here was that the two groups did not differ in their ability to perform

the Phoneme Match Test, as measured by both reaction time (F = 2.85,p =. 10) and
1%







Table 10. Means and standard deviations of reaction time (RT in milliseconds) and
accuracy (% Correct) on the Phoneme Match Test.
PI CTRL
R_.T % Correct RT % Correct

Stimulus Set A 2899 (579)' 90(13) 2758(445) 95(5)

Stimulus Set B 3224 (966)b 89(11) 2711(543)a 94(7)

Note: PI = Phonologically Impaired; CTRL = Controls. Numbers with different
superscripts were significantly different from each other.


accuracy (F = 3.57,p = .07). Because there was no statistically significant group effect,

and because the counterbalance measures taken to pair Set A with Mouth Interference

approximately half of the time and with Foot Interference the other half of the time, the

difference in difficulty level between Set A and Set B was considered to be equally

dispersed among interference conditions. No further attempt to examine Stimulus Sets'

interaction with other variables in subsequent analyses was taken because the number of

subjects in this study limited the power available to detect such high level interactions.

NAPM and visual match. Subjects' performance on the NAPM and Visual Match

were reported in Table 11. Separate MANOVAs were conducted for reaction time and

accuracy, with Group as a between-subject factor (PI vs. CTRL), and Task (NAPM and

Visual Match) and Interference (Mouth and Foot) as within-subject factors. The

MANOVA for reaction time revealed a Group X Task interaction (F = 6.67, p= .01) and

a Task main effect (F = 27.23, p = .00). Subjects responded to NAPM faster than to

Visual Match. Because reaction time to each task was not important theoretically,

separate MANOVAs were conducted to further examine group differences and potential

interference effects within each task. The MANOVA for NAPM revealed a marginal







Group effect (F = 3.91, p = .06), with the CTRL group responding faster than the PI

group. The MANOVA for Visual Match revealed no significant interactions or effects.


Table 11. Reaction time and accuracy on the NAPM and Visual Match Tests for PI and
CTRL erouos.


PI CTRL

RT % Correct RT % Correct

NAPM

Mouth 4353(1547) 81(15) 3688(1551) 89(10)

Foot 4591(1411) 83(8) 3653(1135) 89(10)

Visual Match

Mouth 4946(1788) 77(13)a 5333(1694) 86(13)

Foot 5004(1333) 84 (9)b 4984(1719) 84(13)

Note: PI = Phonologically Impaired; CTRL = Controls. Numbers w
superscripts were statistically different from each other.


ith different


The MANOVA on accuracy data revealed a Group X Interference interaction (F =

4.11, p = .05) and a Group effect (F = 4.09, p = .05). Dependent samples t-test to

compare the accuracy difference between interference conditions indicated that with the

NAPM and Visual Match tasks combined, the CTRL group's accuracy on Mouth and

Foot Interference conditions was the same (t = -0.72,p = .48), whereas the PI group

achieved marginally less accuracy on Mouth Interference compared to Foot Interference

(t = 1.94,p = .07). Independent samples t-test to compare accuracy between groups

showed that the PI group's accuracy on Mouth Interference was statistically less than the

CTRL group's accuracy during this condition (t = 2.38, p = .02). Because there were a

priori reasons to examine the difference between interference conditions separately for






60
each task, and to examine if groups differed in this difference, dependent samples t-tests

were conducted separately for the NAPM and the Visual Match Test. The only

difference was found in the PI group's accuracy performance on the Visual Match Test.

They were less accurate during the Mouth Interference condition than during the Foot

Interference condition (t = 2.30, p = .03). Because their reaction time was not different

between these conditions (t = 0.28,p = .78), a speed-accuracy trade off was not a likely

explanation for their worse accuracy during the Mouth Interference condition.

Block effect. The present study included 12 individuals with ADHD. Children

with ADHD may have decreased sustained attention span and/or slowed reaction time.

Thus trials were divided into two blocks to examine if performance during the first half

of each condition differed from performance during the second half. A MANOVA on

reaction time data with Group (PI vs. CTRL) as a between-subject factor and Task

(NAPM and Visual Match), Interference (Mouth and Foot), and Block (1 and 2) as

within-subject factors revealed a significant Task X Block interaction (F = 16. 15,p = .00)

and Block effect (F = 5.06,p = .03), in addition to the Task X Group interaction and

Task effect already reported above. Follow up MANOVAs were conducted for each

Task. No significant effects were found for NAPM, but for Visual Match, a Block X

Group interaction (F = 4.98, p = .03) was found as well as a Block effect (F = 20.11 ,p =

.00). Table 12 showed the reaction time on the NAPM and Visual Match tasks broken

down by Block. Dependent samples t-test indicated that the CTRL group's reaction time

during Block 1 of Visual Match was much faster than their reaction time during Block 2

(t = -4.49, p = .00), but such a difference was not found for the PI group (t =-l.69,p =

.11).







Table 12. Means and standard deviations of reaction time for each block.
PI CTRL

NAPM

Block 1 4582(1476) 3784(1462)

Block 2 4374(1277) 3581(1206)

Visual Match

Block 1 4828(1451) 4768(1387)a

Block2 5095(1606) 5564(1908)b

Note: PI = Phonologically Impaired; CTRL = Controls. Numbers with different
superscripts were significantly different from each other.


Similar analyses were conducted for response accuracy. The MANOVA revealed

a significant Task X Interference X Block interaction (F = 5.16, p = .03), Task X Block

interaction (F = 7.42, p = .01), and Block main effect (F = 4.0,p = .05), as well as an

Interference X Group interaction and Group main effect. The latter two were discussed

already in the section on accuracy and so were not discussed here. Of the three findings

involving Block, only the highest level interaction was examined because lower level

interactions were represented in the higher level interaction. Table 13 showed the

response accuracy pattern reflected by the Task X Interference X Block interaction.

Dependent samples t-tests were conducted to compare all possible pairs of scores.

Subjects as one group became less accurate during the second block of Visual Match

Mouth Interference (Block 1 vs. Block 2, t = 3.27, p = .00; Block 2, Mouth vs. Foot

Interference, t = 3.15, p = .00). No other pair of scores was statistically different from

each other.








Table 13. Response accuracy (percentage) reflecting the Task X Interference X Block
interaction.
Mouth Foot

NAPM

Block 1 83 86

Block 2 86 85

Visual Match

Block 1 85a 85

Block 2 77 83a

Note: Numbers with different superscripts were significantly different from each other.


Separate MANOVAs were conducted for Block 1 and Block 2 to further explore

how time influenced data. Table 14 summarized and compared the overall findings to

Block 1 and Block 2 findings. The reaction time data were fairly consistent across the

overall analysis and the two time blocks. The overall Group X Task interaction reflected

faster response to the NAPM task by the CTRL group (see Table 11), but there was no

difference between tasks in the PI group's reaction time. The increase in the Group X

Task interaction from non-significance in Block 1 to significance in Block 2 could be

understood by comparing Table 15 with Table 16. The CTRL group became faster on the

NAPM with practice, but they slowed down significantly on the Visual Match with time.

While such a pattern was also evident with the PI group, their reaction time difference

between Block 1 and Block 2 was not as dramatic.

Table 14 also shows differences in response accuracy findings between blocks.

While the Group X Interference interaction was not significant in Block 2, the pattern of







Table 14. Comparison of overall findings with Block 1 and Block 2 findings.
Overall Block 1 Block 2
F p F p F

Reaction Time

Group X Task 6.67 .01 2.89 .10 8.86 .00

Task 27.23 .01 8.05 .01 40.70 .00

Accuracy

Group X Interference 4.11 .05 4.49 .04 2.93 .10

Group 4.09 .05 4.75 .04 2.89 .10

Task X Interference .24 .63 .92 .34 5.71 .02

Task 1.90 .18 .06 .81 5.31 .03



results in Block 2 was consistent with the pattern in Block 1 such that the overall Group

X Interference interaction was significant. This interaction showed that the PI group was

less accurate during Mouth Interference than Foot Interference, while the CTRL group's

accuracy during both interference conditions was commensurate (see Table 11). The

Task X Interference interaction significant in Block 2 was not significant in the overall

analysis, suggesting a great degree of variability during Block 1. The Block 2 Task X

Interference interaction reflected worse accuracy during the Visual Match Mouth

Interference condition in comparison to the Visual Match Foot Interference condition and

the two NAPM interference conditions. As can be seen in Table 16, this effect was

mainly driven by the PI group's performance.

The Block effects found were not anticipated a priori. Fatigue cannot completely

explain differences between blocks because while reaction time slowed down for Visual






64
Table 15. Block I reaction time and accuracy on the NAPM and Visual Match Test for
PI and CTRL groups.
PI CTRL

RT % Correct RT % Correct

NAPM

Mouth 4453(1757) 78(16) 3874(1944) 88(10)

Foot 4710(1553) 84(9) 3695(1121) 88(12)

Visual Match

Mouth 4797(1651) 81(16) 4910(1454) 89(9)

Foot 4859(1408) 85(11) 4625(1474) 85(14)

Note: PI = Phonologically Impaired; CTRL = Controls.


Table 16. Block 2 reaction time and accuracy on the NAPM and Visual Match Test for
PI and CTRL groups.
PI CTRL

RT % Correct RT % Correct

NAPM

Mouth 4264(1461) 83(17) 3543(1362) 89(12)

Foot 4484(1366) 82(12) 3619(1292) 88(12)

Visual Match

Mouth 5035(1931) 72(16) 5768(2024) 82(19)

Foot 5156(1407) 83(10) 5361(2027) 83(13)

Note: PI = Phonologically Impaired; CTRL = Controls.


Match, it speeded up for NAPM (Table 12). That accuracy was worse just during Visual

Match Mouth Interference but not during Foot Interference also argued against an overall







65
fatigue effect (Table 13). Because the role of time was unclear, Block effects were also

examined in subsequent analyses.

Excluding ADHD subjects. To examine how ADHD subjects' reaction time

differed from subjects without ADHD, their reaction time and accuracy data were

contrasted from non-ADHD subjects in Table 17. Overall, subjects with ADHD tended

to be slower and less accurate than their non-ADHD counterparts. Thus subjects with

ADHD were excluded in a MANOVA to examine if ADHD subjects contributed to the

findings involving Block. Similar to above, Group (PI vs. CTRL) was the between-

subject factor, and Task (NAPM and Visual Match), Interference (Mouth and Foot), and

Block (1 and 2) were within-subject factors. This analysis was similar to results reported

with the ADHD subjects included. Significant interactions from reaction time data

included Task X Block (F = 11.66,p = .00) and Group X Block (F = 5.02,p = .03).

Significant main effects included Task (F = 20.47, p = .00) and Block (F = 7.32, p = .01).

Dependent samples t-test to follow up the Task X Block interaction indicated that non-

ADHD subjects were slower during Block 2 of Visual Match (t = -4.36,p = .00), but

their reaction times did not differ between NAPM Block 1 and Block 2 (t = 0.8 1,p -=

.42). Dependent samples t-test to follow up the Group X Block interaction indicated that

the non-ADHD CTRL subjects were slower during Block 2 than Block 1 (t = -4.10,p =

.00), but the non-ADHD PI subjects did not show a difference in reaction time between

blocks (t = -0.28,p -= .78). Excluding ADHD subjects did not change theoretically

important pattern of findings (i.e., Group X Interference interaction). Repeating this

MANOVA with accuracy data revealed no significant effects or interactions. Thus

ADHD subjects were included in all subsequent analyses.







66
Table 17. Reaction time and accuracy of the Non-ADHD and ADHD subgroups on the
NAPM and Visual Match Tests.
PI CTRL


Non-

ADHD

(n= 12)

RT


ADHD

(n=9)


Correct
Correc__._t


Non-

ADHD

(n= 17)

RT


Correct


ADHD

(n=3)


Correct
Correct


Correct
Correct_


NAPM


Mouth

Foot


4109

(1181)

4481

(1692)


84

(12)

86

(7)


4679

(1964)

4739

(999)


76

(18)

79

(8)


3432

(1107)

3587

(1109)


5141

(3055)

4028

(1467)


75

(15)

77

(17)


Visual

Match


5002 82 4871 71 5253

Mouth (1532) (7) (2179) (17) (1733)

Foot 4926 82 5108 85 5020

(1225) (7) (1536) (12) (1804)

Note: PI = Phonologically Impaired; CTRL = Controls.


87

(14)

85

(13)


5786

(1688)

4780

(1404)


81

(8)

78

(12)


Interference movements. The number of interference movements (mouth or foot)

subjects completed during each trial was recorded. The average number of movements

completed per trial within each condition (i.e., NAPM Mouth and Foot Interference,

Visual Match Mouth and Foot Interference) was divided by the average reaction time per







67
trial for that condition to yield a movement frequency index (i.e., number of movements

per second). Post hoc analyses were conducted with these data to determine if subjects

engaged in mouth and foot interfering movements with equal facility. A MANOVA with

Group (PI vs. CTRL) as the between-subject factor and Task (NAPM and Visual Match),

Interference (Mouth and Foot), and Block (1 and 2) as within-subject factors was

conducted on the interfering movement frequency data. This revealed a significant

Group X Task X Interference X Block interaction (F = 4.35, p = .04), as well as

Interference (F = 4.51, p =.04) and Block (F =4.24, p =.05) main effects. Descriptive

statistics were reported in Table 18. Perusal of these descriptive statistics revealed the

following: There was no difference between PI and CTRL groups on Visual Match

Mouth or Foot Interference, even when interference conditions were broken down by

Block. There was also no difference between PI and CTRL groups on NAPM Foot

Interference, even when broken down by Block. However, on NAPM Mouth

Interference, the PI group produced interfering mouth movements more slowly than the

CTRL group. This difference was more salient during Block 1 than Block 2.

To examine how interference movement frequency related to subjects'

articulatory knowledge, Pearson correlations between the AAT score and interfering

movement frequency indices were calculated and reported in Table 19. Significant

correlations were found between the PI group's AAT score and their facility in producing

interfering mouth movements during NAPM. The better the PI group's articulatory

knowledge, the faster they were able to produce interfering mouth movements while

engaging in a name retrieval task. Unexpectedly, the PI group's AAT score was also

correlated with their facility in producing interfering foot movements during the Visual






68
Table 18. Interfering movement frequency index (i.e., number of movements per second)
for the PI and CTRL roups. --
PI CTRL

NAPM

Mouth .85 (.24) .97 (.32)

Block 1 .84 (.25) .98 (.35)

Block 2 .86(.26) .95(.31)

Foot 1.00 (.25) 1.01 (.26)

Block 1 .98 (.25) .97 (.26)

Block 2 1.03 (.26) 1.05 (.27)

Visual Match

Mouth 1.00 (.23) .99 (.23)

Block 1 .99 (.24) .98 (.25)

Block 2 .99 (.22) 1.01 (.24)

Foot 1.04 (.25) 1.04 (.30)

Block 1 1.01 (.26) 1.05 (.32)

Block 2 1.06 (.26) 1.03 (.30)

Note: PI = Phonologically Impaired; CTRL = Controls.


Match Test. The better their articulatory knowledge, the more slowly PI subjects

produced interfering foot movements during a nonverbal visual task.


Predictors of Articulatorv Knowledge

As a post hoc exploration of variables that predict performance on the AAT, age,

BNT Z-score, Rapid Color Naming Z-score, Rapid Object Naming Z-score, LAC raw






69
Table 19. Pearson correlations between the AAT score and interfering movement index
for PI and CTRL groups.--
PI CTRL

NAPM

Mouth Interference .49* -.14

Foot Interference -.03 -.24

Visual Match

Mouth Interference .02 -.14

Foot Interference -.44 -.13

Note: PI = Phonologically Impaired; CTRL = Controls; *p <.05.


score, TONI-2 Quotient, and the three reading achievement standard scores (Word

Attack, Word Identification, and Passage Comprehension) were entered into a stepwise

regression analysis. No variables were selected as statistically significant predictor of

AAT performance. This stepwise regression analysis was repeated for each group

separately to determine if predictors of AAT performance differed by group. Again, no

variables were selected as significant predictors of AAT score. Pearson correlation

between the AAT score and each of the variables entered were presented in Table 20.

The only variable from Table 20 that correlated with the AAT score was the LAC raw

score, and this correlation was significant only for the CTRL group.

Pearson correlations between the AAT score and the Phoneme Match response

time and accuracy were also calculated to examine the relationship between articulatory

knowledge and another task requiring phonological skills. The AAT score was positively

correlated with Phoneme Match accuracy (Pearson's r = .64, p = .00). Subjects with

better articulatory knowledge were more accurate on the Phoneme Match Test. Dividing








Table 20. Pearson correlations between the AAT score and variables entered into
stepwise regression analysis for PI and CTRL groups.
Groups Combined PI CTRL
.. ^ ..~~~.. .. ,...-..- ..-........-. ---^ -. -
Age .01 -.19 .23

BNT .03 -.16 .35

Rapid Color Naming .09 -.02 .31

Rapid Object Naming .00 -.11 .28

LAC .30* .18 .40*

TONI-2 -.24 -.25 -.23

WRMT Word Attack .18 .23 -.00

WRMT Word Identification .15 .16 -.04

WRMT Passage Comprehension .11 .15 -.18

Note: PI = Phonologically Impaired; CTRL = Controls; p <= .05.


subjects into groups showed that this correlation was significant only for the

phonologically impaired group (Pearson's r = .74,p = .00).


Predictors of Phonological Awareness


Predictors of the LAC score were also explored with stepwise regression analysis.

Age, BNT Z-score, Rapid Color Naming Z-score, Rapid Object Naming Z-score, TONI-2

quotient, the three reading achievement standard scores (Word Attack, Word

Identification, and Passage Comprehension), and AAT raw score were entered. Table 21

reported Pearson correlations between the LAC and each of these variables for the groups

combined and for each group individually. Both groups' reading achievement scores

were correlated with their LAC scores. However, because the inter-correlations between







the three achievement tests were high, only the most significant reading achievement

score was selected to predict LAC performance in the regression analysis. For the CTRL

group, Word Attack and age were selected as variables of predictive value. Word Attack

alone contributed 27% (adjusted R2) of the variance to the LAC score (F = 7.51 ,p = .01).

Word Attack and Age combined contributed 44% (adjusted R2) of the variance (F = 7.93,

p = .00). For the PI group, Passage Comprehension and Rapid Object Naming were

selected as variables of predictive value by the stepwise regression analysis.

Surprisingly, the unique variance contributed by the BNT score was not significant once

Passage Comprehension was entered. Instead, the variance contributed by Rapid Object

Naming was deemed significant. The reverse correlation between Rapid Object Naming


Table 21. Pearson correlations between the LAC score and variables entered into
stepwise regression analysis for PI and CTRL groups.
Groups Combined PI CTRL
Age .39 .29 .48'

BNT .34* .43* .07

Rapid Color Naming .18 .07 .16

Rapid Object Naming .04 -.11 .01

TONI-2 .01 .27 -.24

WRMT Word Attack .61* .50* .55*

WRMT Word Identification .59* .49* .44*

WRMT Passage Comprehension .58' .55* .39*

AAT .30* .18 .40*

Note: PI = Phonologically Impaired; CTRL = Controls; 'p <= .05.







72
and the LAC indicated that the better PI subjects performed on the LAC, the slower they

completed the Rapid Object Naming. Passage Comprehension alone contributed 27%

(adjusted R2) of variance to the LAC score (F = 8.31,p = .01). Passage Comprehension

and Rapid Object Naming together contributed 42% (adjusted R2) of variance (F = 8.33,

p =.00).

As a check of LAC's validity as a measure of phonological awareness, the

correlation between the LAC raw score and Phoneme Match Test performance was

calculated. With all subjects combined, the correlation between the LAC score and the

Phoneme Match performance was statistically significant (RT, Pearson's r = -.37,p = .02;

accuracy, Pearson's r = .48,p = .00). Dividing subjects into groups revealed that the LAC

score and Phoneme Match performance was correlated for CTRLs (RT, Pearson's r = -

.48, p = .03; accuracy, Pearson's r = .69,p = .00) but not for the PI group (RT, Pearson's r

=. 18,p = .44; accuracy, Pearson's r = .29, p = .20).


Developmental Phonological Dyslexics vs.

Adequate Readers with Poor Phonology vs. Controls


The PI group can be categorized into two distinct subgroups. As shown in Table

1, the DPD group was characterized by impaired phonological processing, single-word

reading, and comprehension (WRMT Word Attack, Word Identification, and Passage

Comprehension respectively) in comparison to their expected achievement level based on

their intellectual aptitude. The ARPP group, while demonstrating impaired phonological

skills, actually has single-word reading and comprehension skills commensurate to their

expected achievement level. This subtype of children with impaired phonological







73
processing but adequate reading ability has been described (Masutto & Cornoldi, 1992),

but little is known about them, such as whether these children represent a distinct subtype

of dyslexia or a milder form of the disorder. To explore if the DPD and ARPP groups

differed in their presentation on cognitive measures, differences between these groups

were examined in this section. The CTRL group was also included in order to compare

these two phonologically impaired groups with normal readers.


Articulatory Awareness Test


Articulatory Awareness Test scores obtained by DPD, ARPP, and CTRL groups

were provided in Table 22. An ANOVA conducted with Group as the between-subject

variable revealed that the three groups did not differ from each other on their AAT score

(F = 0.41,p = 0.66).


Table 22. Means and standard deviations of AAT scores obtained by DPD, ARPP, and
CTRL groups.
DPD ARPP CTRL

AAT 6.o (2.72) 6.4 (1.78) 6.7 (1.75)

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.


To examine if DPD and ARPP groups demonstrate different patterns of

relationship between articulatory knowledge and name retrieval, Pearson correlations

between AAT and reaction time during experimental conditions (i.e., NAPM and Visual

Match interference conditions) were conducted for each group and reported in Table 23.

Although the DPD group's correlations between their AAT score and naming latency

appeared more similar to the CTRL group's and different from the ARPP group's, there







was no statistically significant difference between DPD and ARPP group's correlations

on the NAPM interference conditions (Mouth Interference, Z(I 1,1o) = -.83, p > .05; Foot

Interference, Z(i 1,10) = -.70,p > .05) or on the Visual Match interference conditions

(Mouth Interference, Z1 1,1o) = -1.51, p> .05; Foot Interference, Zij 1,1o) = -.52, p> .05).

Unlike the control group, whose correlation between naming latency and AAT score

differed significantly from its corresponding correlation on the Visual Match Test during

Mouth Interference (i.e., -.48 vs. .19, Z(20,0o) = -2.07, p < .05), no such corresponding

correlation pairs within the DPD and ARPP groups were statistically different (DPD:

NAPM Mouth vs. Visual Match Mouth, Z(H1,11) = -.06,p> .05; NAPM Foot vs. Visual

Match Foot, Z(l ,1) = -.60,p > .05; ARPP: NAPM Mouth vs. Visual Match Mouth,

Z(o10,io) = -.71,p > .05; NAPM Foot vs. Visual Match Foot, Z(,, .,,,i = -.39,p > .05).


Table 23. Pearson correlations between the AAT score and reaction time on
experimental measures for DPD, ARPP, and CTRL groups.
DPD ARPP CTRL
NAPM

Mouth Interference -.32 .10 -.48*

Foot Interference -.29 .06 -.35

Visual Match

Mouth Interference -.29 .45 .19

Foot Interference -.00 .26 .27

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; p < .05.







Descriptive Measures


Table 24 showed each group's performance on descriptive measures. One-way

analyses of variance indicated a group difference on the BNT (F = 4.66, p= .02) but not

on any of the other naming measures (Rapid Color Naming, F = 2.89, p= .07; Rapid

Object Naming, F = l.54,p = .23; Naming Test, F = 2.30,p = .11). On the BNT,

independent samples t-test showed that the DPD group scored lower than the CTRL

group (t = 2.74,p = .01). The ARPP group's BNT score did not differ from either of the

other groups (ARPP vs. CTRL, t= 0.60, p= .55; ARPP vs. DPD, t = 1.85,p = .08). One-

way analysis of variance on the LAC scores also indicated difference between groups (F

= 7.71 ,p = .00). Independent samples t-test showed that the DPD group's phonological

awareness score was lower than both other groups (DPD vs. CTRL, t = 3.76, p = .00;

DPD vs. ARPP, t = 2.84,p = .01), while the ARPP group's LAC score did not differ from

that of the CTRL group's (t = l.23,p = .23).


Table 24. Means and standard deviations on descriptive measures for the DPD, ARPP,
and CTRL groups.
DPD ARPP CTRL
BNT 2.71(2.52)- 1.13 (1.02) -0.85 (1.30)e

Rapid Color Naminga -1.02 (1.85) -0.08 (1.07) 0.02(0.68)

Rapid Object Naminga -1.25 (2.80) -0.49 (1.81) -0.05 (0.80)

Naming Testb 85(7) 89(5) 90(6)

LACc 46(13)f 65 (16)g 75(23f

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls. Z-scores. b Percentage correct. C Raw score.
Numbers in each row with different superscripts were significantly different from each
other.









Experimental Measures


Phoneme match. A MANOVA was conducted to see if groups differed in their

reaction time on matching the end phoneme of words. Group (DPD, ARPP, and CTRL)

was entered as the between-subject factor and Stimulus Set (A and B) was entered as the

within-subject factor. The Group X Stimulus Set interaction was only marginally

significant (F = 2.85, p = .07), but there was a significant Stimulus Set effect (F = 6.09, p

= .02). The Stimulus Set effect was the same as that reported under the PI vs. CTRL

section, where it was shown that subjects were faster in responding to Set A than to Set

B. Each group's response time to the Sets A and B were presented in Table 25. A

similar analysis was conducted with response accuracy data. Only a marginal effect of

group was found (F = 2.89,p = .07). As there were no statistically significant Group

effects on reaction time and response accuracy, it was assumed that the measures taken to

counterbalance Stimulus Set with interference conditions dispersed differences between

stimulus sets among different interference conditions, and no further attempt to examine

Stimulus Set's interaction with other variables was taken.


Table 25. Reaction time and accuracy on the Phoneme Match Test for DPD, ARPP, and
CTRL groups.
DPD ARPP CTRL

RT % Correct RT % Correct RT % Correct

Set A 2958(633) 87(16) 2834(540) 93(6) 2758(445) 95(5)

SetB 3285(1136) 87(14) 3156(793) 92(6) 2711(543) 94(7)

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.









NAPM and visual match. Descriptive statistics for the experimental measures,

NAPM and Visual Match, were presented in Table 26. Separate MANOVAs were

conducted for reaction time and response accuracy data, with Group as a between-subject

factor (DPD vs. ARPP vs. CTRL) and Task (NAPM and Visual Match), Interference

(Mouth and Foot) and Block (1 and 2) as within-subject factors. Because this analysis

was exactly the same as the analyses performed in the section on PI vs. CTRL, only

effects or interactions involving Group (DPD vs. ARPP vs. CTRL) were reported here to


Table 26. Reaction time and response accuracy on the NAPM and Visual Match Tests
for DPD, ARPP, and CTRL groups.
DPD ARPP CTRL

RT % Correct RT % Correct RT % Correct

NAPM

Mouth 4511 77 4179 84 3688 88

(1666) (17) (1472) (12) (1551) (10)

Foot 5117 82 4013 85 3653 89

(1510) (10) (1087) (5) (1135) (10)

Visual Match

Mouth 4936 76 4957 79 5333 86

(1902) (17) (1756) (7) (1694) (13)

Foot 5243 84 4742 83 4984 84

(1528) (11) (1100) (7) (1719) (13)

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.







reduce redundancy. The MANOVA with reaction time data revealed a Group X Task

interaction (F = 3.76, p =.03). Pairwise comparisons using dependent samples t-test to

follow up on the Group X Task interaction revealed that both ARPP and CTRL groups

were faster in responding the to NAPM in comparison to the Visual Match (ARPP, t = -

2.33, p = .04; CTRL, t = -4.58,p = .00), but the DPD group's reaction time on these two

tasks did not differ (t = -0.83,p =.42).

The MANOVA on accuracy data did not reveal any Group main effects or

interactions. Other interactions and effects were exactly the same as those reported in the

PI vs. CTRL section and were not repeated here.

ADHD subjects. To see whether ADHD differentially affected the performance

of the two phonologically impaired groups, reaction time and accuracy data of ADHD

subjects were contrasted to those of non-ADHD subjects in Table 27. Dividing the DPD

and ARPP groups into ADHD vs. Non-ADHD subgroups dramatically reduced the

number of subjects in each subgroup, rendering multivariate analyses looking at

differences between groups unrealistic due to low power. Nevertheless, perusing Table

27 suggested that ADHD subjects tended to be less accurate than their non-ADHD

counterparts.

Interfering movements. To examine if the two phonologically impaired groups

engaged in mouth and foot interfering movements with equal facility, a MANOVA was

conducted with the interfering movement frequency data. The CTRL group was included

in this analysis as a comparison group. Group (DPD vs. ARPP vs. CTRL) was entered as

a between-subject factor and Task (NAPM and Visual Match), Interference (Mouth and

Foot), and Block (1 and 2) were entered as within-subject factors. Table 28 showed each









Table 27. Reaction time and accuracy of the phonologically impaired Non-ADHD and
ADHD subgroups.
DPD ARPP


Non-

ADHD

(n=5)

RT


Correct


ADHD

(n=6)


Non-

ADHD

(n =7)

RT


Correct


Correct


ADHD

(n=3)


Correct


NAPM


Mouth

Foot


4581

(356)

5551

(1951)


80

(13)

87

(9)


4454

(2333)

4756

(1081)


75

(21)

77

(9)


3772

(1468)

3716

(1036)


87

(11)

86

(6)


5130

(1164)

4705

(1035)


78

(14)

84

(1)


Visual

Match


5145 83 4761 69 4899

Mouth (1285) (8) (2415) (21) (1782)

Foot 5290 84 5204 84 4667

(1212) (8) (1868) (14) (1258)

Note: DPD = Developmental Phonological Dyslexia; ARPP =
Poor Phonology.


81 5092

(8) (2076) (

81 4917

(8) (797) (

Adequate Reader with


group's interference movement frequency index. The result of this analysis was similar

to the analysis with the two phonologically impaired groups combined, with the

exception that the four-way interaction (Group X Task X Interference X Block) was now
ii>






80
only marginally significant (F = 2.61, p = .09). Because breaking the PI group down into

the DPD and ARPP groups reduced the number of subjects in these subgroups, the power

to detect a four-way interaction in the present analysis if one existed was reduced.

Interestingly, an examination of Table 28 revealed that ARPP subjects appeared to

produce interference movements with greater facility than DPD subjects. The exception

Table 28. Interfering movement frequency index for the DPD, ARPP, and CTRL groups.
DPD ARPP CTRL
"NAPM~ ~ ---------- -
NAPM

Mouth .83 (.25) .87 (.25) .97 (.32)

Block 1 .83 (.27) .85 (.23) .98 (.35)

Block 2 .83 (.25) .89 (.28) .95 (.31)

Foot .90 (.19) 1.11 (.27) 1.01 (.26)

Block 1 .88 (.20) 1.08 (.27) .97 (.26)

Block2 .91 (.17) 1.15(.30) 1.05(.27)

Visual Match

Mouth .94 (.20) 1.04 (.24) .99 (.23)

Block 1 .93 (.18) 1.05 (.29) .98 (.25)

Block 2 .95 (.23) 1.04 (.20) 1.01 (.24)

Foot .96 (.17) 1.11 (.31) 1.04 (.30)

Block 1 .95 (.18) 1.07 (.33) 1.05 (.32)

Block2 .97(.18) 1.16 (.30) 1.03 (.30)

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.







to this was during NAPM Mouth Interference, where both DPD and ARPP subjects

produced interfering movements with approximately equivalent facility, especially during

Block 1. Compared to the CTRL group, the ARPP subjects tended to produce interfering

movements with greater facility, except during the NAPM Mouth Interference condition,

whereas the DPD subjects tended to be slower in producing interfering movements than

the CTRL group. Comparing Mouth Interference to Foot Interference, the ARPP group

appeared to have the greatest discrepancy between these interference conditions during

NAPM, while neither of the other groups showed such discrepancy on either NAPM or

Visual Match.

To examine how interference movement frequency related to each group's

articulatory knowledge, Pearson correlations between the AAT score and interfering

movement frequency index for each group were calculated and reported in Table 29. The

positive correlation between the AAT score and the NAPM Mouth Interference

movement index was driven by the DPD group. The better the DPD group's articulatory

knowledge, the faster they were able to produce interfering mouth movements during a

name retrieval task. The negative correlation between the AAT score and the Visual

Match Foot Interference movement index was driven by the ARPP group. The better the

ARPP group's articulatory knowledge, the more slowly they produced foot interference

movements during a nonverbal visual task.


Predictors of Articulatory Knowledge


To explore if variables that predicted the AAT score differed for the DPD and

ARPP groups, a stepwise regression analysis was conducted with age, BNT Z-score,







Table 29. Pearson correlations between the AAT score and interfering movement
frequency index for DPD, ARPP, and CTRL groups.
DPD ARPP CTRL

NAPM

Mouth Interference .74* .06 -.14

Foot Interference .28 -.52 -.24

Visual Match

Mouth Interference .16 -.24 -.14

Foot Interference -.47 -.64 -.13

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL =Controls. p < .05.


Rapid Color Naming Z-score, Rapid Object Naming Z-score, LAC raw score, TONI-2

Quotient, and the three reading achievement standard scores (Word Attack, Word

Identification, Passage Comprehension) entered as independent factors. Pearson

correlations between the AAT score and each of these variables for DPD and ARPP

groups were presented in Table 30. The CTRL group's correlation between AAT and

each independent variable were also provided for comparison. For the DPD group, no

variable was correlated with the AAT. Consequently no variable was selected by the

stepwise regression as a predictor of the DPD group's AAT score. For the ARPP group,

the three reading achievement measures, which were highly correlated with each other,

were positively correlated with AAT score. A perusal of scatter plots of reading

achievement scores as a function of AAT scores indicated that these significant

correlations were valid and not due to the presence of extreme scores. The Passage

Comprehension standard score was selected by the stepwise regression analysis and







accounted for 52% (adjusted R2) of the variance to the ARPP group's AAT score (F =

10.59,p = .01). The correlation between the ARPP group's AAT score and reading

achievement measures indicated that the better their articulatory knowledge, the higher

reading attainment ARPP subjects were able to achieve.


Table 30. Pearson correlations between the AAT score and variables entered into
stepwise regression analysis for DPD and ARPP groups.
DPD ARPP CTRL
Age -.13 -.32 .23

BNT -.24 -.13 .35

Rapid Color Naming -.22 .45 .31

Rapid Object Naming -.28 .29 .28

LAC .20 .12 .40*

TONI-2 -.47 .25 -.23

WRMT Word Attack .02 .67* -.00

WRMT Word Identification -.04 .66* -.04

WRMT Passage Comprehension -.03 .76* -.18

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; p <= .05.


Other than the variables examined in Table 30, Pearson correlations between the

AAT score and Phoneme Match performance were also examined. Both DPD and ARPP

groups' AAT scores were significantly correlated with their accuracy on the Phoneme

Match Test (DPD: r= .77,p= .01; ARPP: r= .76,p= .01). The better their articulatory

knowledge, the more accurate they were in deciding if phonemes matched.







Predictors of Phonological Awareness


Predictors of the LAC score for DPD and ARPP groups were also explored with

stepwise regression analysis. Age, BNT Z-score, Rapid Color Naming Z-score, Rapid

Object Naming Z-score, TONI-2 quotient, the three reading achievement standard scores

(Word Attack, Word Identification, Passage Comprehension), and AAT raw score were

entered as independent factors. Table 31 showed Pearson correlations between the LAC

score and each of these variables for DPD and ARPP groups. The CTRL group's

correlations were also listed for comparison. When the phonologically impaired group

was divided into DPD and ARPP groups, power to detect correlations was decreased such

that the previously significant correlations between the LAC score and reading

achievement measures were no longer significant for either the DPD or ARPP group.

The negative correlation between the LAC and Rapid Color Naming was significant for

the ARPP group only. The better their phonological awareness, the more slowly ARPP

subjects were able to complete rapid naming, especially of colors. No variables were

selected as variables of predictive value by stepwise regression analysis for either DPD or

ARPP groups.

Correlation between the LAC raw score and response time on the Phoneme Match

Test was calculated for the two phonologically impaired groups. Neither the DPD nor

ARPP group's LAC score was correlated with their performance on the Phoneme Match

Test (DPD: RT, Pearson's r = -.24, p =.49; accuracy, Pearson's r = .28, p = .41; ARPP:

RT, Pearson's r = -.08,p = .84; accuracy, Pearson's r = .08,p = .84).








Table 31. Pearson correlations between the LAC score and variables entered into
stepwise regression analysis for DPD and ARPP groups.
DPD ARPP CTRL
................Age -.07 .44 .48* .

BNT .24 .50 .07

Rapid Color Naming .18 -.61 .16

Rapid Object Naming -.08 -.50 .01

TONI-2 .14 .09 -.24

WRMT Word Attack .42 .18 .55*

WRMT Word Identification .26 .01 .44*

WRMT Passage Comprehension .24 .24 .39*

AAT .20 .12 .40*

Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; p <= .05.


Poor vs. Adequate Articulatory Knowledge


The articulatory feedback hypothesis of naming hypothesized that articulatory

feedback facilitates name retrieval. This hypothesis makes the assumption of the

presence of articulatory knowledge. There was theoretical interest in examining the

name retrieval process of those subjects with adequate articulatory knowledge and those

with inadequate articulatory knowledge. Presumably, the hypothesis may hold true for

those with adequate articulatory knowledge but not for those with poor articulatory

knowledge. To group subjects into poor vs. adequate articulatory knowledge groups, the

mean AAT score for the entire population of subjects was calculated, and one standard

deviation below the mean, which corresponded to a Z-score of-1, was selected as the
%*







86
cutoff score for grouping criterion. The mean AAT score of all 41 subjects was 6.44 with

a standard deviation of 2.03. Thus subjects with an AAT score of 4 or below were

grouped into the Poor Articulatory Knowledge group (PAK), and those with an AAT

score of 5 or above were grouped into the Adequate Articulatory Knowledge group

(AAK). Table 32 reported some descriptive statistics about each subject group.

Independent samples t-test indicated that the two groups did not differ in age (t = -0. 16,p

=.88) or intellectual aptitude as estimated by the TONI-2 (t= -1.46,p = .15). All of the

subjects in the PAK group were males, and the majority of this group was composed of

children with ADHD. Note there was unequal distribution of the number of subjects in

each group, with only seven subjects falling into the PAK group.


Table 32. Demographics of the Poor Articulatory Knowledge (PAK) and Adequate
Articulatory Knowledge (AAK) groups.
PAK AAK

(n=7) (n=34)

Age 9(2) 9(2)

TONI-2 111(7) 105(10)

M:F Ratio 7:0 20:14

ADHD 5 7



Descriptive Measures


The performance of PAK and AAK groups on descriptive measures was

examined for group differences. Table 33 summarized the groups' performance on these

measures. There was no difference between groups on any of the naming measures when






87
group differences were tested using independent samples t-test (BNT, t = -0.18,p = .86;

Rapid Color Naming, t = 0.84,p = .41; Rapid Object Naming, t = 0.23,p = .82; Naming

Test, t = 1.15, p = .26). Independent samples t-test indicated that the PAK group scored

more poorly on the LAC than the AAK group (t = 2.43, p = .02), not surprisingly as LAC

score was significantly correlated with AAT score (Table 20). Comparison of mean

standard scores between groups using independent samples t-test revealed that the PAK

group scored lower on Word Attack (t = 2.76,p = .01) and Word Identification (t = 2.27,

p = .03) compared to AAK, but not on Passage Comprehension (t = 1.77,p = .08).


Table 33. Means and standard deviations on descriptive measures for the PAK and AAK
groups.
PAK AAK
BNT -1.31 (1.83) -1.44(1.83)

Rapid Color Naming' -.65 (.72) -.21 (1.33)

Rapid Object Naminga -.63 (.87) -.46 (1.97)

Naming Testb 86 (5) 89 (6)

LACc 47(22)Y 68 (21

WRMT Word Attackd 74 (12)Y 92 (17)b

WRMT Word Identificationd 77 (13)8 94 (19)b

WRMT Passage Comprehensiond 83 (14) 95 (17)

Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.
"Z-scores. b Percentage correct. 'Raw score. d Age-corrected standard scores. Within
each row, numbers with different superscripts were significantly different from each
other.







Experimental Measures


Phoneme match. Performance on the Phoneme Match Test was examined to see

if PAK and AAK groups differed in their ability to match phonemes. Separate ANOVAs

were conducted for reaction time and response accuracy with Group (PAK vs. AAK) as

the between-subject variable. Table 34 showed each group's performance on the

Phoneme Match Test. The PAK group was both slower in reaction time (F = 5.45, p=

.02) and less accurate (F = 66.91,p = .00) than the AAK group. The overall AAT score

and Phoneme Match reaction time was not correlated (i.e., all subjects combined;

Pearson's r = -.26, p =. 10). However, the AAT score did correlate positively with

Phoneme Match accuracy (Pearson's r= .64,p =.00). Because the PAK group's ability

to match phonemes was remarkably worse, their reaction time and accuracy on the

Phoneme Match Task were used as covariates in analyses involving NAPM because the

NAPM required phoneme match as an integral part of the task.


Table 34. Reaction time and accuracy on the Phoneme Match Test for the PAK and
AAK groups.
PAK AAK

Reaction time 3385 (1018) 2802 (490)

% Correct 76(11) 95(4)

Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.


NAPM and visual match. Subjects' reaction time and accuracy on the NAPM

were reported in Table 35, and their Visual Match performance were reported in Table

36. Separate analyses were conducted with NAPM and Visual Match because Phoneme

Match performance was used as a covariate in analyzing NAPM data while the use of this
Ot







covariate would be inappropriate for Visual Match because phoneme match was not

required as part of the Visual Match Test. Table 35 represented subjects' scores without

the covariate extracted.


Table 35. Reaction time and accuracy on the NAPM for PAK and AAK groups.
Numbers represent data without the covariate extracted.
PAK AAK

RT % Correct RT % Correct

Mouth

Block 1 5795(2460) 70(17) 3836(1541) 86(12)

Block 2 5142(2113) 79(18) 3659(1151) 87(14)

Foot

Block 1 5838(1671) 82(12) 3881(1151) 87(10)

Block 2 4904(1687) 85(10) 3888(1275) 85(12)

Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.


On the NAPM (Table 35), separate MANCOVAs were conducted for reaction

time and response accuracy data. Group (PAK vs. AAK) was the between-subject factor,

Interference (Mouth and Foot) and Block (1 and 2) were within-subject factors, and

Phoneme Test reaction time and response accuracy were covariates in MANCOVAs

analyzing reaction time and response accuracy, respectively. The reaction time

MANCOVA revealed a significant Group X Interference X Block interaction (F = 4.33,p

= .04), an Interference X Block X Covariate interaction (F = 6.65,p = .01), an

Interference X Block interaction (F = 6.57, p= .01), and a Group X Block interaction (F

= 4.43,p = .04). The Group X Interference X Block interaction was explored by




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ARTICULATOR INVOLVEMENT IN NAMING:
A TEST OF THE ARTICULATORY FEEDBACK HYPOTHESIS OF NAMING
By
LISA HSIAO-JUNG LU
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
2000

ACKNOWLEDGMENT
I would like to thank each of my committee members for their guidance: Eileen
Fennell for her clinical wisdom, conceptual inquiry, and generous support; Ken Heilman
for the opportunity to approach neuropsychology through clinical hypothesis testing;
Bruce Crosson for his belief in my abilities; Duane Dede for helping me think broadly;
and Jamie Algina for his patient and thorough teaching of statistics. I have been very
lucky in crossing the paths of mentors who have such vigor and integrity both
professionally and personally. I would also like to thank Ann Alexander, Linda
Lombardino, Tim Conway, and the American Psychological Association for their support
of this project. To my family, especially to my husband, I would like to say thank you
for your undying support through these most challenging years.
11

TABLE OF CONTENTS
Page
ACKNOWLEDGMENT ii
LIST OF TABLES vi
LIST OF FIGURES ix
ABSTRACT x
INTRODUCTION 1
Development of the Hypothesis 1
Liberman's Motor Theory of Speech Perception 1
Heilman's Motor-Articulatory Feedback Hypothesis 3
Anatomy of the Articulatory Feedback System 4
Proposed Hypothesis: Articulatory Feedback Hypothesis of Naming 6
Developmental Phonological Dyslexia 8
Definition of Developmental Phonological Dyslexia 8
Nature and Extent of Naming Deficit 14
Role of Phonological Awareness 18
Anatomical Evidence of Anomalies 20
Co-morbidity with Attention-Deficit/Hyperactivity Disorder 26
Research Questions 27
What Is the Correlation Between Articulatory Knowledge and
Naming? 28
Do Dyslexics Have Worse Articulatory Knowledge? 28
Is There Support for the Articulatory Feedback Hypothesis of
Naming? 29
What Is the Relationship Between Articulatory Knowledge and
Phonological Awareness? 30
METHODS 32
Subjects 32
Descriptive Measures 38
Articulatory Awareness Test 38
Naming 40
iii

Phonological Awareness 41
Attention-Deficit/Hyperactivity Disorder 41
Experimental Measures 42
Naming Assessed via Phoneme Match (NAPM) 42
Visual Match 45
Phoneme Match 46
Naming Test 47
Procedures 48
RESULTS 51
Articulatory Knowledge 51
Phonologically Impaired vs. Controls 53
Articulatory Awareness Test 54
Descriptive Measures 55
Experimental Measures 57
Predictors of Articulatory Knowledge 68
Predictors of Phonological Awareness 70
Developmental Phonological Dyslexics vs. Adequate Readers with
Poor Phonology vs. Controls 72
Articulatory Awareness Test 73
Descriptive Measures 75
Experimental Measures 76
Predictors of Articulatory Knowledge 81
Predictors of Phonological Awareness 84
Poor vs. Adequate Articulatory Knowledge 85
Descriptive Measures 86
Experimental Measures 88
DISCUSSION 97
Review of Hypothesis 97
Correlation Between Articulatory Knowledge and Name Retrieval 100
Group Differences in Articulatory Knowledge 103
Group Differences on Naming Measures 104
Reaction Time and Response Accuracy 106
Interference Movement Frequency 110
Attention-Deficit/Hyperactivity Disorder 113
Relationship Between Articulatory Knowledge and Phonological
Awareness 114
Articulatory Feedback Hypothesis of Naming 118
Limitations 121
Summary of Findings 123
Correlation Between Articulatory Knowledge and Naming 123
Dyslexics Do Not Have Worse Articulatory Knowledge 123
Relationship Between Articulatory Knowledge and Phonological
■«>
IV

Awareness 124
Modification of the Articulatory Feedback Hypothesis of Naming .... 124
APPENDIX 1 ARTICULATORY AWARENESS TEST 125
APPENDIX 2 ATTENTION-DEFICIT/HYPERACTIVITY DISORDER
INTERVIEW 135
APPENDIX 3 NAPM STIMULI 139
REFERENCES 141
BIOGRAPHICAL SKETCH 146
\
V

LIST OF TABLES
Table Page
1. Summary of grouping criteria 36
2. Summary of demographics and grouping criteria scores 38
3. AAT scores of the Morris Center population 40
4. Sample of the chart for determining the order of task, interference,
and stimulus set for each subject 49
5. Order of test administration 50
6. Pearson correlations between the Articulatory Awareness Test (AAT)
score and reaction time on experimental measures 53
7. AAT and AAT-R scores obtained by Phonologically Impaired (PI)
and Control (CTRL) groups 54
8. Pearson correlations between the AAT score and reaction time on
experimental measures for PI and CTRL groups 55
9. PI and CTRL groups' performance on descriptive measures 56
10. Means and standard deviations of reaction time (RT in milliseconds)
and accuracy (% Correct) on the Phoneme Match Test 58
11. Reaction time and accuracy on the NAPM and Visual Match Tests
for PI and CTRL groups 59
12. Means and standard deviations of reaction time for each block 61
13. Response accuracy (percentage) reflecting the
Task X Interference X Block interaction 62
14. Comparison of overall findings with Block 1 and Block 2 findings 63
»!
VI

15. Block 1 reaction time and accuracy on the NAPM and Visual
Match Tests for PI and CTRL groups 64
16. Block 2 reaction time and accuracy on the NAPM and Visual
Match Tests for PI and CTRL groups 64
17. Reaction time and accuracy of the Non-ADHD and ADHD subgroups
on the NAPM and Visual Match Tests 66
18. Interfering movement frequency index (i.e., number of movements
per second) for the PI and CTRL groups 68
19. Pearson correlations between the AAT score and interfering movement
index for PI and CTRL groups 69
20. Pearson correlations between the AAT score and variables entered
into stepwise regression analysis for PI and CTRL groups 70
21. Pearson correlations between the LAC score and variables entered
into stepwise regression analysis for PI and CTRL groups 71
22. Means and standard deviations of AAT scores obtained by DPD,
ARPP, and CTRL groups 73
23. Pearson correlations between the AAT score and reaction time on
experimental measures for DPD, ARPP, and CTRL groups 74
24. Means and standard deviations on descriptive measures for the
DPD, ARPP, and CTRL groups 75
25. Reaction time and accuracy on the Phoneme Match Test for
DPD, ARPP, and CTRL groups 76
26. Reaction time and accuracy on the NAPM and Visual Match Tests
for DPD, ARPP, and CTRL groups 77
27. Reaction time and accuracy of the phonologically impaired
Non-ADHD and ADHD subgroups 79
28. Interfering movement frequency index for the DPD, ARPP, and
CTRL groups 80
29. Pearson correlations between the AAT score and interfering movement
frequency index for DPD, ARPP, and CTRL groups 82
â– *>
vii

30. Pearson correlations between the AAT score and variables entered
into stepwise regression analysis for DPD and ARPP groups 83
31. Pearson correlations between the LAC score and variables entered
into stepwise regression analysis for DPD and ARPP groups 85
32. Demographics of the Poor Articulatory Knowledge (PAK) and
Adequate Articulatory Knowledge (AAK) groups 86
33. Means and standard deviations on descriptive measures for the
PAK and AAK groups 87
34. Reaction time and accuracy on the Phoneme Match Test for the
PAK and AAK groups 88
35. Reaction time and accuracy on the NAPM for PAK and AAK groups.
Numbers represent data without the covariate extracted 89
36. Reaction time and accuracy on the Visual Match Test for PAK
and AAK groups 93
37. Interfering movement frequency index for the PAK and AAK groups 95
»>
viii

LIST OF FIGURES
Figure Page
1. A simplified model of reading from Ellis and Young (1988) 11
2. Block 1 NAPM reaction time, plotted against the ability to match end
phonemes 91
3. Block 2 NAPM reaction time, plotted against the ability to match end
phonemes 92
4. Formula for calculating the effect size reflecting the Group X Task X
Interference interaction 96
•«>
IX

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
ARTICULATOR INVOLVEMENT IN NAMING:
A TEST OF THE ARTICULATORY FEEDBACK HYPOTHESIS OF NAMING
By
LISA HSIAO-JUNG LU
August, 2000
Chair: Eileen B. Fennell
Major Department: Clinical and Health Psychology
The articulatory feedback hypothesis of naming posited that articulatory feedback
facilitates name retrieval. This was tested using an interference paradigm. Naming
performance during a condition that allowed for articulatory feedback was contrasted
with a condition that interfered with articulatory feedback by providing inappropriate
articulatory feedback. Because Montgomery found that dyslexic children had impaired
articulatory knowledge, performance of phonologically impaired readers was contrasted
with those of normal readers. Subjects were also grouped by their level of articulatory
knowledge, and performance between knowledgeable and unknowledgeable groups was
compared. One assumption of the hypothesis was that those with adequate articulatory
knowledge would benefit from articulatory feedback while those with poor articulatory
knowledge would not. The hypothesis predicted that interfering with articulatory
feedback would affect subjects who have articulatory knowledge by removing the
x

facilitation effects provided by articulatory feedback. Results did not directly support the
hypothesis. For individuals with articulatory knowledge, naming latency during the
condition that allowed for articulatory feedback was not better than the condition that
interfered with feedback. Subjects did not spontaneously use articulatory feedback to
assist name retrieval. However, other data did suggest a relationship between articulatory
knowledge and name retrieval. Among individuals with poor articulatory knowledge,
inappropriate articulatory feedback and name retrieval interfered with each other and
competed for neural resources. This suggested a neural connectivity between articulatory
knowledge and name retrieval that was not evident between articulatory knowledge and a
nonverbal control task. Those with articulatory knowledge appeared to have processed
name retrieval automatically and efficiently, and they had sufficient extra neural
resources to process extraneous information such as interfering feedback. In contrast,
those with poor articulatory knowledge retrieved names less efficiently. They had
limited capacity to simultaneously process interfering information while engaging in
name retrieval. It was also found that articulatory knowledge and phonological
awareness were dissociable phenomena. Both normal and phonologically impaired
readers demonstrated a wide range of articulatory knowledge, and dyslexic children did
not have worse articulatory knowledge.
â– s
XI

INTRODUCTION
The problem of name retrieval is one that has received extensive attention in the
neuropsychology literature. By focusing on this limited aspect of language, researchers
hope to generalize the knowledge learned here to other aspects of linguistic functioning.
The term name retrieval has been defined differently by different research groups. Here,
name retrieval refers only to the activation of phonological representation of a word and
does not include activation of motor patterns to produce such a representation. The
current project proposes to test a new hypothesis of name retrieval, the articulatory
feedback hypothesis of naming. First, the development of this hypothesis from
Liberman's motor theory of speech perception and Heilman's theory of motor-articulatory
feedback will be presented. Then the articulatory feedback hypothesis of naming will be
proposed. This hypothesis was tested with a population of children who have
phonological dyslexia of the developmental type. Therefore a discussion of dyslexia,
related name retrieval issues, neurological anatomy of this population, and co-morbidity
with Attention-Deficit/Hyperactivity Disorder (ADHD) will follow.
Development of the Hypothesis
Liberman's Motor Theory of Speech Perception
Liberman, Cooper, Shankweiler, and Studdert-Kennedy first proposed their motor
theory of speech perception in 1967. Extensive research following the first proposal of
1

2
their theory has led to subsequent revisions of their theory, the most recent of which was
presented in Liberman and Mattingly, 1985. Their current theory on speech perception
was based on two tenets: 1) The object of speech perception is the intended gestures of
the speaker; 2) speech perception and speech production are innately (i.e., biologically)
linked, not learned.
The first tenet of their theory speaks to why this theory is a "motor" one instead of
a "sensory" one. Unlike auditory theories, which posit that perception of speech depends
on an analysis of auditory signals, Liberman and Mattingly (1985) proposed that the goal
of speech perception is not to uncode the auditory signals, but to infer the intended
gestures of the speaker's vocal system. They argued that the uncoding of auditory cues
cannot be sufficient for speech perception because there is no correspondence between
acoustic signals and phonemic categories. Acoustic signals for the same phonemic
category vary by speaker, prosodic tone, and context. Though acoustic signals are
different in these different conditions, the same phonemic percept is perceived.
Conversely, the exact same acoustic signal under different contexts can yield different
phonemic percepts. The lack of a relationship between acoustic signals and phonemic
categories suggests that acoustic signal by itself is not sufficient for the perception of
speech. Furthermore, that visual feedback of oral gestures can influence the perception
of a speech sound (McGurk & MacDonald, 1976; MacDonald & McGurk, 1978)
suggests that both visual and acoustic signals are merely cues for the object of perception.
Liberman and Mattingly (1985) argued that the object of speech perception is the
intended gesture, or the actual motor movement, of the speaker. The object is the

3
intended gesture because much of the gesture takes place inside the speaker's oral cavity
and out of sight of the perceiver.
These motor theorists propose that the speech perception system is able to decode
intended gestures from auditory signals because speech perception system is a specialized
neural module evolved to perform such linguistic functions. They assumed that the
development of motor control over the vocal tract preceded the evolution of speech.
Adaptations made coarticulation of rapid phonetic gestures possible. A perceiving
system developed concomitantly, and this system is specialized to take into account
complex acoustic consequences. Because the perception system developed
concomitantly with the production system, they are biologically linked. The calculations
necessary to perceive speech are done automatically via hardwired neural structures that
connect the production and the perception parts of the system. This system is one
specialized for linguistic functions. It is a linguistic module that operates independently
from the general auditory system that processes other non-linguistic signals.
Heilman's Motor-Articulatory Feedback Hypothesis
Heilman, Voeller, and Alexander (1996) elaborated on Liberman's theory and
proposed a motor-articulatory feedback hypothesis of speech perception. They
emphasized that the perception of spoken words is associated with the production of
intended articulatory gestures. As an infant learns to perceive words, s/he imitates the
sounds heard by replicating intended articulatory gestures of the speaker with his/her own
articulators. As the infant fine-tunes this imitation of sounds, s/he associates each
phoneme with a movement of his/her articulators. Feedback from the articulators to the

neural module is essential for the individual to be aware of this relationship between
phoneme and articulatory gesture. Understanding of this relationship constitutes
articulatory knowledge, which facilitates parsing spoken words down into phonemic
parts.
Heilman et al. (1996) applied their theory to the problem of grapheme-to-
phoneme conversion in reading. They pointed out that when first learning to read, one
needs to break words down into letters clusters and associate them with their respective
phonemes. The motor-articulatory' feedback hypothesis posits that children use the
articulatory apparatus when learning to associate specific graphic representations with
phonemic representations. Learning to read includes using the articulatory knowledge
learned earlier to associate phonemes with graphemes. Without this articulatoiy
knowledge as a mediating tool, the means by which written words are broken down into
letter clusters may seem arbitrary. Reading thus involves associating already established
phoneme-articulatory relationships to graphemic representations. Beginning readers
often move their lips and tongue even when reading silently. Adults also engage the
articulators when reading novel or hard-to-pronounce words. These findings suggest that
engaging the articulators facilitates reading. The motor-articulatory feedback hypothesis
proposed that it is the feedback that articulators provide which facilitates the learning of
grapheme-to-phoneme conversion.
Anatomy of the Articulatory Feedback System
Central to the human language system are two major neural regions, Wernicke’s
and Broca's areas in the left cerebral hemisphere of most right-handed individuals (Kolb

5
& Whishaw, 1990). Wernicke's area, which is located in the left posterior portion of the
superior temporal gyrus, and surrounding regions are sometimes referred to as posterior
language areas that process the perception of language. Classically, a lesion in
Wernicke's area results in fluent aphasia, characterized by fluent speech but impaired
comprehension, repetition, and naming. Information from these posterior perisylvian
regions travels anteriorly to Broca's area via the arcuate fasciculus. Broca's area, located
in the left inferior frontal gyrus, is conceptualized as an area specialized for motoric
programming of speech. Lesion of Broca's area results in nonfluent aphasia characterized
by intact comprehension but effortful, nonfluent speech.
The motor-articulatory hypothesis proposed by Heilman et al. (1996) emphasizes
the learned association between a phoneme and an articulatory gesture. When an infant
hears a novel word, his/her auditory and auditory association cortex (Wernicke's area)
analyze the sounds in the word. Pars opercularis, triangularis, and the foot of the motor
cortex (Broca's area) execute a complex motor program to approximate the heard word.
Primary motor cortex activates the articulators in the oral cavity. During word imitation,
as the articulators move, they send sensory feedback (i.e., proprioceptive and tactile) to
the primary sensory cortex and sensory association cortex. These sensory cortices
presumably connect with the frontal areas involved in motor planning (e.g., Broca's area),
thus providing linkage back to the articulatory system. The sensory areas also project to
polymodel sensory cortex in the temporal-parietal region that stores auditory
representations of words (e.g., Wernicke's area). Eventually, through this connectivity,
the infant learns that each phoneme is associated with one motor pattern for articulation,
and that words are associated with a series of articulatory patterns.

6
Proposed Hypothesis: Articulatory Feedback Hypothesis of Naminu
Articulatory knowledge assists reading presumably because it facilitates retrieval
of information. In the case of reading novel words where it is necessary to use
grapheme-to-phoneme conversion, the subject sees a grapheme, and this grapheme
triggers the phoneme associated with it and the articulatory motor pattern used to produce
that phoneme; execution of this motor pattern results in the production of the phoneme
associated with the target grapheme. Movement of the articulators may not be necessary
for the retrieval of the phoneme, and feedback may even be intracerebral.
If articulatory feedback assists reading, it is likely that it also assists name
retrieval, a more basic language function that came into use much earlier in the
evolutionary process than reading. The articulatory feedback hypothesis of naming states
that articulatory feedback facilitates name retrieval. It does not state that articulatory
feedback is necessary for naming or sufficient for naming. Rather, articulatory feedback
assists other retrieval systems, making the process faster and more efficient.
Many of our everyday experiences suggest that activation of motor patterns can
facilitate retrieval. The tip-of-the-tongue phenomenon, where one experiences problems
retrieving a word, may sometimes be overcome by articulating the beginning sound.
When one cannot recall a phone number, pretending that one is dialing, thereby engaging
the motor system controlling the fingers, sometimes assist the recall of phone numbers.
Whereas retrieval may not need motor activation, if some part of the retrieval system is
compromised, motor activation may provide the extra input necessary to activate a
representation.

7
Heilman et al. (1996) posited that in order to benefit from articulator feedback,
one must have articulatory knowledge. Articulatory knowledge refers to the ability to
locate the position of the articulatory structures, such as the tongue and the lips, during
phoneme production. This knowledge could be conceptualized as a neural association
between the articulatory system, the sensory/proprioceptive system, and the phonemic
representation system. Without this link, activation of the articulators may not coactivate
neural patterns representing phonemic percepts, which would limit the articulatory
system's ability to facilitate retrieval. The spontaneous use of articulatory knowledge will
be referred to as articulatory awareness.
Montgomery (1981) showed that children with dyslexia have impaired articulator
knowledge compared to children without dyslexia. She presented cartoons of sagittal
drawings through the oral cavity that illustrate the position of tongue, teeth, and lips, then
asked which of the cartoons matched phonemes that she produced. She encouraged the
subjects to repeat the phonemes as much as they want and to think about the location of
their tongue, teeth, and lips. The non-dyslexic children were able to correctly identify
their articulatory positions better than dyslexic children. All children were able to repeat
the phonemes; thus the dyslexic children's deficit cannot be explained by an auditory
perceptual deficit. They appeared to be unknowledgeable about the position and
movement of their articulators. They lacked articulatory knowledge.
Montgomery's (1981) work suggested that dyslexic children lack sensory
feedback about their articulators’ position and movement. This population could be
instrumental in testing the articulatory feedback hypothesis of naming. The hypothesis
stated appropriate articulator movements (and the sensory feedback concomitant with

8
those movements) facilitate lexical retrieval in individuals with articulatory knowledge.
In the normal population, inhibiting appropriate articulator movements while asking
subjects to name objects should impede their naming. This was done in this study by
introducing an interference task. Subjects were asked to engage in mouth movements
that interfered with the articulation of object names. Thus the articulatory system could
send articulatory feedback to the central nervous system that was appropriate to and
facilitated name retrieval. Individuals with impaired articulatory knowledge should
respond differently. They could differ in one of two ways. First, because these
individuals may not be as knowledgeable of their articulator movements as normal
individuals, they may not use an articulatory strategy to assist naming. Therefore
interfering with the articulators during a naming task may not impede the naming process
of these individuals as much as it does in controls. However, individuals with impaired
articulatory feedback may have no other alternative strategy to assist naming. Because
they already have impaired articulatory feedback, adding an interference could impair
their naming ability even more, making them less capable of retrieving names than
controls.
Developmental Phonological Dyslexia
Definition of Developmental Phonological Dyslexia
The terms dyslexia, learning disability in reading, and reading disability have
been used interchangeably in the literature. All three refers to problems with reading, but
a clarification of terms is in order. The term dyslexia has been used in research
attempting to understand the neurological or neuropsychological deficits underlying the

9
disorder. Dyslexia can be categorized into developmental versus acquired. Acquired
dyslexia refers to those individuals who acquired the disorder through insults to the
central nervous system after a period of normal reading development (Coslett, 1997).
Developmental dyslexia refers to those individuals who demonstrate problems in the
development of reading. Researchers have posited the existence of many types of
dyslexia, including phonological, surface, and deep dyslexia, to name a few (Ellis &
Young, 1988). The Orton Dyslexia Society Research Committee has defined dyslexia as:
one of several distinct learning disabilities. It is a specific language-based
disorder of constitutional origin characterized by difficulties in single word
decoding, usually reflecting insufficient phonological processing abilities. These
difficulties in single word decoding are often unexpected in relation to age and
other cognitive and academic abilities; they are not the result of generalized
developmental disability or sensory impairment. Dyslexia is manifest by variable
difficulty with different forms of language, often including, in addition to
problems reading, a conspicuous problem with acquiring proficiency in writing
and spelling. (Shaywitz, Fletcher, & Shaywitz, 1995, p. S51)
This committee, composed of representatives from the National Institute of Child Health
and Human Development, defined dyslexia as a subtype of learning disability. Thus
learning disability is an umbrella term encompassing many types of problems with
learning, including reading and math. Reading disability is an abbreviated term for
learning disability in reading. This is a legal term used to identify individuals who meet
legal criteria to receive special education services. Dyslexia is used interchangeably with
reading disability, but it is a theoretical/research term, and its use implies neurological
abnormalities within the language system that underlie difficulty with reading processes.
Dyslexia constitutes 80% of children diagnosed with learning disability
(Shaywitz, Fletcher, & Shaywitz, 1995). Of the different types of dyslexia, phonological
dyslexia is the most common form. Phonological dyslexia refers to reading problems

10
secondary to phonological processing deficits. Specifically, impairment in the
grapheme-to-phoneme conversion system has been implicated. A simplified version of
Ellis and Young's (1988) model for oral reading is depicted in Figure 1. According to
this model, written words are processed initially by the visual system, which processes
visual stimuli by analyzing each individual letter component. After visual analysis,
reading can be achieved by three mechanisms or routes: 1) Results of the visual analysis
system enter into the orthographic input lexicon, which contains visual representations of
words an individual has learned. The selected lexical representation enters into the
semantic system and activates the meaning of the word. From the semantic system, the
appropriate auditory representation of the word is activated in the speech output lexicon.
Then speech is produced by activation of motor patterns. Most proficient readers are
thought to use this lexical system because of its efficiency and completeness compared to
the other two systems. 2) The second method for reading is similar to the first except that
the semantic system is bypassed, so that words can be read without accessing the
meaning of words. These two lexical, or whole-word reading routes are important for
reading irregular and ambiguous words, and cannot be used to read nonwords or
pseudowords. 3) The third method, the phonological route, is a labor-intensive system
used when reading novel words (i.e., words without representation in the orthographic
input lexicon). The visual system analyzes words and parses words into letter
components. The letter or letters (grapheme) are converted to the sounds they represent
(phoneme). The phonemes are blended to produce the phonological sequence for the
entire word. Phonological dyslexia results when this grapheme-to-phoneme conversion
link is defective.

11
Written word
V
Visual
Analysis
System
Phoneme
Level
y
Speech
Figure 1. A simplified model of oral reading from Ellis and Young (1988).

12
One problem with dyslexia research is that research groups often do not specify
the type of dyslexia subjects demonstrate. This is especially true with treatment-focused
research or service, where the primary goal is to improve patients' reading skills,
regardless of whether patients meet criteria for certain theoretical subtype of dyslexia.
This was true for Montgomery's (1981) work, which reported that subjects were
"dyslexic" without specification of subtype. The present study, in an attempt to strive for
theoretical clarity, will limit dyslexic participants to those with developmental
phonological dyslexia, defined as individuals who have impaired grapheme-to-phoneme
conversion, because this is the largest, most common subtype of dyslexia. Thus an
assumption of the present study is that a large percentage of participants in the
Montgomery (1981) study were phonological dyslexics, and that phonological dyslexics
have decreased awareness of their articulator position and movement.
The identification of developmental phonological dyslexia is problematic for two
reasons. First, dyslexia falls under the broad category of specific learning disability
under Individuals with Disabilities Education Act (IDEA, 1997; Public Law 105-17),
which requires that educational institutions provide special services to meet the
educational needs of individuals with disabilities. Because the law does not require the
specification of the subtype of reading disability, most clinical organizations do not
specify if a patient's reading disability fits the phonological subtype in diagnostic
evaluations. Second, the IDEA specified that reading ability should be discrepant from
intellectual aptitude, but it did not state how such discrepancy should be measured.
Different groups have used intelligence quotient (IQ)-achievement discrepancy
(Ackerman & Dykman, 1993; Cornwall, 1992), chronological age-reading age

13
discrepancy (Fawcett & Nicolson, 1994; Felton, Naylor, & Wood, 1990; Felton, Wood,
Brown, Campbell, & Harter, 1987; Wolf & Goodglass, 1986; Wolf & Obregon, 1992),
arbitrary cutoff scores on tests of achievement or based on teacher/school referrals
(Bowers & Swanson, 1991; Denckla & Rudel, 1976; Korhonen, 1995; Manis,
Seidenberg, Doi, McBride-Chang, & Petersen, 1996; Mattis, French, & Rapin, 1975;
Swan & Goswami, 1997), or regression approaches that control for the intercorrelation
between achievement and IQ measures (Fletcher, Francis, Rourke, Shaywitz, &
Shaywitz, 1992; Fletcher, Schaywitz, Shankweiler, Katz, Liberman, Stuebing, Francis,
Fowler, & Shaywitz, 1994; Pennington, Gilger, Olson, & DeFries, 1992; Shaywitz,
Escobar, Shaywitz, Fletcher, & Makuch, 1992; Shaywitz, Fletcher, Holahan, & Shaywitz,
1992; Shaywitz, Shaywitz, Fletcher, & Escobar, 1990). Consequently, the literature on
this population is fraught with inconsistent diagnostic criteria for reading disability.
The IQ-achievement discrepancy method has been shown to be problematic in
diagnosing reading disability among minority populations. Duckworth (1999) showed
that among a sample of college students referred for evaluation of learning disability,
African Americans score on average 12 points lower on the Wechsler Adult Intelligence
Scale-Revised (WAIS-R) intelligence quotients than their European-American
counterparts. Although psychologists have attempted to design culture-free intelligence
tests in recent years, Duckworth's data suggest that a commonly used intelligence
measure, WAIS-R, is still biased against minority populations. The IDEA specified that
learning disability cannot be due to mental retardation, which is defined as IQ scores of
below 70. If the normal distribution of African Americans' IQ scores is downshifted by
12 points, then the difference between the mean IQ score of the African-American

14
population and the mental retardation cutoff of 70 is decreased by 12 points, which in
effect, decreases the potential number of African Americans who can meet diagnosis for
learning disability using a simple difference discrepancy method.
An alternative to this problem is to calculate expected achievements scores based
on intellectual aptitude via a regression method. This method controls for the inter¬
correlation between achievement and intelligence measures and the regression of
achievement scores toward the mean intelligence score, and minimizes problems of over¬
identifying high-IQ subjects and under-identifying of low-IQ subjects as learning
disabled. Using the regression formula,
V = [Pcy (Sx / Sy )(IQ - X)] + Y
(where Y' is the expected achievement score for a given IQ, rxy is the correlation between
the IQ and the achievement test, Sx is the standard deviation of the achievement test, Sy is
the standard deviation of the intelligence test, IQ is the achieved intelligence score, X is
the mean of the intelligence test, and Y is the mean for the achievement test), Duckworth
showed that among those referred for a learning disability evaluation, more African
Americans would be classified as learning disabled than using a simple discrepancy
difference method (51% vs. 28%, respectively), while the method of classification used
does not significantly affect the number of European Americans classified as learning
disabled (27% vs. 30%, respectively).
Nature and Extent of Naming Deficit
A basic question relates to the existence of bona fide naming deficits in the
dyslexic population. Because dyslexia is a reading disability, could their naming deficits

15
be attributable to a lack of vocabulary? If they do have bona fide naming problems, do
they have problems retrieving names of symbols that compose written language? Or do
they have a general retrieval problem that implicates naming of other targets? Fawcett
and Nicolson (1994) examined naming performance of 35 dyslexic children (defined as
having at least an 18 month discrepancy between chronological and reading age, with full
scale IQ of at least 90) and 32 chronological age controls (CA). They found that
dyslexics have impaired naming of letters, digits, colors, and pictures compared to the
CA group, with the picture naming task being the most robust measure differentiating
groups. The dyslexics' discrepant performance on color and picture naming suggested
that their deficit was not limited to grapheme-to-phoneme translation. They have actual
problems with name retrieval.
A critique of Fawcett and Nicolson's (1994) study was that their groups were not
matched on intellectual aptitude and therefore retrieval differences may be explained by
intelligence differences between dyslexics and controls. Swan and Goswami (1997)
recruited a dyslexia group (n = 16), a CA control group (n = 16), a reading age (RA)
control group (n = 16), and a garden-variety poor reader control group (GV; n = 16). All
groups had matching IQ scores except the GV (101-105 vs. 79), and all groups had
matching reading age except the CA (112-116 mo. vs. 139 mo.). Swan and Goswami
(1997) found the following pattern of performance on a picture naming test (percentage
correct score based on the total number of items familiar to each subject):
CA>RA>GV=dyslexics. Dyslexics performed as well as GV. Both groups' naming
scores were worse than younger but reading age matched controls (RA). All of these
groups performed worse than the CA controls. However, dyslexics were more accurate
*>

16
than any other group in correctly recognizing targets on a follow-up multiple choice task
composing of items they failed to name spontaneously. (Subjects' scores on this test were
coded as a proportion of their total error score, so scores were not inflated for those with
more errors). Dyslexics' ability to correctly identify targets in a recognition paradigm
argued against a vocabulary deficit. Rather, it supported that they have problems of
retrieval. In contrast, GV were found to have poorer vocabulary on a test of receptive
vocabulary. Swan and Goswami (1997) concluded that while GV's poor picture naming
performance was due to poor vocabulary, dyslexics' was due to problems with name
retrieval.
Wolf and Obregon (1992) found similar results using a multiple-choice paradigm
with items on the Boston Naming Test (BNT) that were missed. Their selection criterion
for dyslexia was better defined than the Swan and Goswami (1997) study: Dyslexics
were 2 or more years below expected reading level as assessed by the Gray Oral Reading
Test. Compared to an average reader control group (n = 42), dyslexics' (n = 8) naming
was worse, but dyslexics were more accurate on identifying the correct target in a
multiple-choice format compared to controls. They also concluded that dyslexics'
naming errors were reflective of a retrieval deficit.
These studies showed that dyslexics have lexical retrieval problems on formal
neuropsychological tests. Murphy, Pollatsek, and Well (1988) questioned 1) if dyslexics’
retrieval problem was one of general processing deficit or was it specific to language, and
2) whether dyslexics' retrieval deficit can be seen in their natural/spontaneous use of
language. They reasoned that if dyslexics have a general processing deficit, they should
be slower on tasks not involving the explicit use of language, such as a simple reaction

17
time task requiring them to move their finger to the side where a visual target appeared,
and on a picture categorization task requiring them to indicate if a picture is an exemplar
of a target category. If their retrieval deficit was specific to language, they should show
deficient performance on tasks of oral expressive and receptive language as well as on
formal neuropsychological measures. They tested dyslexics identified by poor Rapid
Automatized Naming (RAN) performance and who were at least two years below their
expected reading level (n = 14). Controls were matched for age and IQ (n = 14). They
found no difference between groups on basic motor reaction time and picture
categorization, which ruled out the general processing deficit hypothesis. Dyslexics
performed worse than controls on both formal (BNT) and informal language measures.
On informal, expressive language measures, dyslexics generated fewer words in retelling
stories and had slower verbal output. On informal, receptive language measures, they
were slower at categorizing spoken words. The authors concluded that dyslexics' name
retrieval problem reflected a specific linguistic deficit, and not a general processing
deficit, and their name retrieval problem manifested in their oral language as well as on a
formal neuropsychological measure.
The retrieval problems that dyslexic children demonstrate in childhood have been
shown to persist into adulthood. Korhonen (1995) followed a small group (n = 8) of
children who had problems in rapid automatic naming and in word retrieval, and tested
them approximately 9 years later at 18 years of age to examine the persistence of naming
deficits identified during childhood. These children were originally identified by their
teachers as learning disabled children who demonstrated special problems in reading.
Korhonen comparing these individuals' performance to controls matched on age, sex, IQ,

18
parent SES at nine years of age, and education level at 18 years of age (n = 10).
Korhonen found that learning disabled individuals were slower and made more errors on
rapid color naming and rapid object naming, and on another test of rapid alternating
stimulus naming. The findings were not as robust as at nine years of age; nevertheless
they were present. Fawcett and Nicolson (1994) tested dvslexics from eight to 17 years
of age and also found naming deficits in their 17-years old dyslexic group (n = 13).
Felton, Naylor, and Wood (1990) followed 115 children with dyslexia into adulthood.
They defined dyslexia as a discrepancy of 1.5 years between chronological and reading
age. They found persistent problems in rapid naming, nonword reading, and
phonological awareness. These findings of persistent naming problems suggested a
deficit model of dyslexia, which conceptualized dyslexia as a deficit that does not “catch
up’ with maturation.
Role of Phonological Awareness
A hypothesized deficit underlying dyslexia is an impaired sense of phonological
awareness (Liberman & Shankweiler, 1985). Swan and Goswami (1997) used a picture
naming paradigm to study the role of phonological processing in dyslexics. They
hypothesized that if a phonological deficit underlies dyslexia, dyslexics' naming
performance would be worse for longer words of low frequency. Longer words have
more phonemes to encode and retrieve, and thus were more demanding on the
phonological system. Low frequency words occur less often in language, making them
less familiar to the phonological system. They found a Group X Frequency X Length
interaction, where with frequency controlled, dyslexics (n = 16) named short words better

19
than long words. This pattern was not seen in the CA, RA, or GV controls. Lower level
interactions also showed expected findings: Dyslexics named short words better than
GV, but their naming of long words was worse than GV and RA. Dyslexics named high
frequency words better than GV, but their naming of low frequency words was worse
than RA. Swan and Goswami (1997) further posited that dyslexics' picture naming
would be worse than word naming because in word naming, letters were available to
assist the phonological system. In picture naming, no cues were present to assist the
phonological system. They did find impaired picture naming compared to word naming
for dyslexics but not for RA and CA.
A natural question that arose with evidence of phonological and naming deficits
in dyslexia regards the relationship between these processes. Two studies have addressed
this issue but with incongruent results. Cornwall (1992) used a regression analysis to
examine if phonological awareness and rapid automatized naming contributed unique
variances to reading disabled children's scores on academic achievement (n = 54; reading
disability was defined by >= 16 standard point discrepancy between Wide Range
Achievement Test, Revised Reading subtest and WISC-R FSIQ, with WISC-R FSIQ >=
90). If phonological awareness and rapid automatized naming contributed unique
variances, then they were likely independent processes affecting the dyslexic population.
With age, SES, behavioral, and intelligence factors controlled, she found that
phonological awareness (as assessed by Auditory Analysis Test [AAT], a phonemic
deletion test) and rapid naming did contribute unique shares of variance to achievement
scores. Phonological awareness contributed to nonword reading, spelling, and
comprehension. Naming contributed to single-word reading and passage reading speed.

20
Bowers and Swanson (1991) also conducted regression analyses to examine the same
issue. They found that most variance on nonword reading (after controlling for the
W1SC-R Vocabulary score) was explained by the score on the Auditory Analysis Test,
and most variance on a single-word reading test was explained by the score on Odd Word
Out, another phonological awareness measure (a multiple-choice test requiring
identification of the non-rhyming word). In contrast, most variance on comprehension
was explained by rapid automatized naming. A problem with Bowers and Swanson's
(1991) study was that they combined poor readers (n = 19; defined by Woodcock
Reading Mastery Test, Word Identification subtest standard score at or below the 25th
percentile for age) with average readers (n = 19) in their regression analyses. It was
possible that subjects with different reading abilities have different patterns of
relationship between phonological awareness and naming. That is, phonological
processes and naming abilities may contribute differently to the reading achievement
scores of average and disabled readers. The lack of well-controlled studies in this area
rendered the contributions of phonological awareness and naming to reading achievement
equivocal.
Anatomical Evidence of Anomalies
Galaburda and colleagues (Galaburda, Sherman, Rosen, Aboitiz, and Geschwind,
1985; Galaburda, 1989) examined eight post-mortem brains of individuals identified by
the Orton Society as dyslexic. They found abnormal symmetry of the planum temporale
in these eight brains as well as ectopic neurons in the molecular layer of the perisylvian
cortex. The planum temporale lies just posterior to the Heschl's gyrus on the superior
-s

21
surface of the temporal lobe. These and other structures surrounding the Sylvian fissure
compose the language system, which includes reading. The findings of Galaburda et al.
(1985) suggested that neurodevelopmental abnormalities may contribute to the
symptomatology of dyslexia.
Geschwind and Levitsky (1968) found that among 100 post-mortem samples,
approximately 65% showed a left greater than right plana difference. Approximately
25% had symmetrical plana, and only 10% had a right planum that was larger than the
left. Rumsey, Dorwart, Vermess, Denckla, Kruesi, and Rapoport (1986) measured
temporal lobe volume from magnetic resonance (MR) images and found data consistent
with the results of Galaburda et al. (1985). Nine of the ten men with documented reading
disability in psvchoeducational evaluations from their childhood demonstrated
symmetrical temporal lobes. However, Rumsey et al. (1986) did not measure the planum
temporale specifically. Given the extent of the temporal lobe, it was possible that other
aspects of the temporal lobe contributed to the symmetry rather than the planum
temporale.
In 1990, two independent groups reported on the symmetry of the planum
temporale among dyslexics. Hynd, Semrud-Clikeman, Lorys, Novey, and Eliopulos
(1990) compared plana length and insular length of 10 developmental dyslexic children
with 10 non-dvslexic controls. The average age of their dyslexic children was 10 years,
and dyslexia was defined by normal or better intellectual ability (WISC-R Full Scale IQ
>= 85), reading achievement significantly below their FSIQ (>= 20 standard score points
lower than FSIQ on Woodcock Reading Mastery Test-Revised, Word Attack and
Passage Comprehension subtests), and no co-morbid diagnosis of ADHD. The average

22
age of their normal controls was 12 years, and they must have normal or better
intellectual ability (WISC-R FSIQ >= 85), no reportedly family history of learning
problems, no significant deficit in achievement, and no reported or observed medical,
educational, social, or emotional difficulties. From MR images, this research group
found that dyslexics have bilaterally shorter insula compared to non-dyslexic controls,
and that 90% of dyslexics have a left planum length that was shorter than their right
planum length. There was no difference between dyslexic and control groups on right
planum length. Dyslexics' overall left planum was shorter than controls' left planum.
They suggested that the nature of plana symmetry in dyslexia was due to a smaller left
planum temporale. Larsen, Hoien, Lundberg, and Odegaard (1990) also examined plana
length from MR images and found symmetrical plana among dyslexic children (n = 19;
dyslexic subjects were identified from a school psychology service and had poor word
recognition in the presence of normal intelligence). However, comparing dyslexics' MR
images to those of age- and intelligence-matched controls' (n = 19), they found that plana
symmetry among dyslexics was due to increased right planum length rather than the
decreased left planum that Hynd et al. (1990) reported.
The above studies did not differentiate between the temporal and parietal banks of
the planum temporale. Leonard, Voeller, Lombardino, Morris, Hynd, Alexander,
Andersen, Garofalakis, Honeyman, Mao, Agee, and Staab, (1993) suggested that
examining the different banks of the planum may explain some of the contradictions in
the literature. They measured the length of these two banks from MR images. Their
subjects were adults previously diagnosed with dyslexia by pediatrician, pediatric
neurologist, or learning disability specialists (n = 9), the dyslexics' biological relatives (n

23
= 10), and normal controls (n = 12). In contrast to the previous studies, Leonard et al.
(1993) did not find abnormal symmetrical plana in the dyslexic population. All groups
demonstrated a greater left temporal bank compared to the right temporal bank and a
greater right parietal bank compared to the left parietal bank. When only the left
hemisphere was considered, all subjects except two dyslexics had longer temporal bank
than the parietal bank. When only the right hemisphere was considered, the controls also
had longer temporal bank than the parietal bank, but 55% of dyslexics and 40% of
relatives had longer parietal bank compared to the temporal bank. They suggested that
dyslexics had reduced right intrahemispheric asymmetry (between temporal and parietal
banks) compared to controls due to the transfer of planar tissue from the temporal to the
parietal bank.
The same group also examined the structure of the Sylvian fissure. Among
controls, the left Sylvian fissure usually ended in a bifurcation into small ascending and
descending branches. Variations to this typical pattern included no bifurcation and/or
extra gyri in the parietal operculum anterior to the termination of the Sylvian fissure.
There were also variations in the number of Heschl's gyri present. Normally, there was
one Heschl's gyrus in the left hemisphere that was visible on a mesial section of the MR
image. On more lateral sections, Heschl's gyrus moved anteriorly and dissolved into a
number of convolutions in the superior temporal gyrus. Leonard et al. (1993) found that
every subject with dyslexia showed at least one of the above anomalies. Six (66%) had
bilateral anomalies. Biological relatives had the next highest number of anomalies;
seventy percent had at least one anomaly while 20% had bilateral anomalies. In contrast,
only 17% of control subjects had one anomaly and none had bilateral anomalies. These

24
findings suggested a genetic etiology for dyslexia. The greatest number of anomalies was
found among dyslexics, the group with the next greatest number of anomalies was
biological relatives of the dyslexics, and normal controls without family histories of
dyslexia had the fewest number of anomalies. These anatomical studies indicated that
reading difficulties experienced by dyslexics may have an anatomical basis. However, a
word of caution regarding the Leonard et al. (1993) study is in order. Their subjects were
either professionals or from high functioning professional families. They described their
dyslexic subjects as "recovered dyslexics" who have been able to compensate so well that
there was much overlap between dyslexic and control groups on a measure of
phonological awareness. Thus their dyslexic subjects may not be a representative sample
of the dyslexic population.
Hynd et al. (1990), in addition to finding shorter left planum length among
dyslexic children, also compared the MR images of dyslexics (n = 10) with MR images
of children with attention deficit/hyperactivity disorder (ADHD; n = 10). Their ADHD
subjects had average or better intellectual ability (W1SC-R FSIQ >= 85), no reported
family history of learning problems, no significant deficit in reported or measured
achievement, documented behavioral deficits consistent with a Diagnostic and Statistical
Manual of Mental Disorders, Revised Third Edition (DSM-III-R) diagnosis of ADHD,
who responded favorably to stimulant medication. They found that dyslexics and
ADHDs both have smaller right frontal width compared to controls. However, dyslexics'
planum temporale was shorter on the left while ADHDs showed the typical pattern of left
greater than right planum. These findings showed that while frontal anomalies may be

25
implicated in both groups, anomalies of the planum temporale may be specific to
dyslexia.
Imaging data from cerebral blood flow studies also supported the evidence of
structural anomalies in the dyslexic population. Rumsey, Andreason, Zametkin, Aquino,
King, Hamburger, Pikus, Rapoport, and Cohen (1992) examined cerebral blood flow
differences between dyslexic adults and normal subjects during a rhyming task. Their
dyslexic subjects all had Wechsler Adult Intelligence Scale-Revised (WAIS-R) Verbal
or Performance IQ scores of at least 89 and met DSM-I1I-R criteria for developmental
reading disorder. All received some special education service while in school. Subjects
were presented word pairs aurally and pressed a button if the word pair rhymed. They
found that dyslexics had decreased activation of left temporal-parietal and midtemporal
areas that corresponded to the angular gyrus and Wernicke's area. This finding of
hypometabolism in temporal parietal and midtemporal areas corresponded well to the
structural abnormalities of planum temporal and Heschl's gyrus reported by Leonard et al.
(1993).
Paulesu, Frith, Snowling, Gallagher, Morton, Frackowiak, and Frith, (1996)
conducted a different rhyming task during a positron emission tomography (PET) study
and found similar results. Their rhyming task involved visual presentation of letters.
Subjects moved a joystick to letters that rhymed with the letter "B." Their subjects were
five dyslexic adults who were university students, postgraduates, or self-employed
entrepreneur, identified from records of a dyslexia clinic, and five education-matched
controls. The non-dyslexic subjects activated left Broca's and Wernicke's areas and the
left insula. Dyslexics showed decreased activation in the left Wernicke's area and a

26
greater decrease in the left insula. On a short-term memory task where subjects judged if
a target letter was present in a previous sequence of English letters, the normals activated
the above areas plus the left supramarginal gyrus. The dyslexics activated the same areas
as the controls except for the left insula. On these two tasks, dyslexics activated the same
major language areas as controls (i.e., Broca’s and Wernicke's) while attending to and
judging phonological stimuli. However, they did not activate these areas in concert as
controls. Dyslexics' lack of activation of the insular cortex suggested that the insula was
not necessary for phonological processing. The authors suggested that perhaps the insula
acted as a "bridge" between the Broca's area and the supramarginal gyrus. Though it may
not be necessary for the processing of phonological information, it provided the
connection between posterior and anterior regions. Dyslexics' anatomical anomalies and
their lack of activation of this region during phonological analysis tasks suggested that
disconnection between important regions for phonological analysis may underlie their
problems with phonological processing.
Co-morbiditv with Attention-DeficitHvperactivitv Disorder
While reading disability and ADHD have very different symptoms, they do
overlap much more than one would expect from independent random distributions of
these disorders. Approximately 30-50% of individuals with reading disability have a co-
morbid diagnosis of ADHD (Felton et al., 1987). This high co-morbidity rate has led
researchers to speculate if attentional problems limit a child's ability to develop
automated processing skills necessary for reading. Felton et al. (1987) aimed to
disentangle the neuropsychological deficits contributed by attention deficit disorder

27
(ADD) and by reading disability (RD). They formed four groups from two factors, RD
and ADD: RD with ADD, RD with non-ADD, non-RD with ADD, and non-RD with
non-ADD. Using age and receptive vocabulary score as covariates and controlling for
family-wise error rates, Felton et al. (1987) found that RD and non-RD groups differed
on a visual confrontation naming test (BNT) and on a rapid automatized naming test.
There was no main effect of ADD on these measures. The ADD and non-ADD groups
did differ from each other on a test of supraspan verbal memory (RAVLT). In contrast,
there was no main effect of RD on this task. These findings showed that RD and ADD
contributed to different aspects of neuropsychological deficits. If ADD contributed to
impaired reading skills among the RD children, one would expect some overlap of
impaired areas. The findings of Felton et al. (1987) provided indirect support for the
independence of reading disability and attention deficit disorder.
Research Questions
The literature on the dyslexic population indicated that 1) dyslexic individuals
have problems with name retrieval; 2) dyslexic individuals have problems with
phonological processing; and 3) their language difficulties likely have an anatomical
basis. This combination of findings rendered the phonological dyslexic population to be
of special interest to this study, because the anatomical areas identified to be abnormal
(i.e., Wernicke’s area) were also implicated by the articulatory feedback hypothesis of
naming. This hypothesis posited that articulatory awareness facilitates naming.
Presumably, sensory feedback received by the primary sensory cortex from articulators
has connectivity with both the Wernicke's area and Broca's area. This connectivity
-s

28
allows for articulatory feedback to trigger phonological representations of object names,
and to trigger motor patterns to execute the articulation of those names. Phonological
dyslexic subjects and normal readers should yield a range of naming abilities by which to
examine articulatory knowledge and the relationship between name retrieval and
articulatory knowledge. The aim of this study was to test the articulatory feedback
hypothesis of naming. To achieve this aim, the following questions were asked:
What Is the Correlation Between Articulatory Knowledge and Naming?
If articulatory knowledge and naming are related, better articulatory knowledge
should be associated with either faster name retrieval latency or better name retrieval
accuracy. This relationship should hold for all subjects. Reading achievement status
may put subjects at the lower end of the continuum of naming ability. If articulatory
feedback facilitates naming for all subjects, dyslexics' naming ability will be correlated
with their articulatory knowledge in the same manner as normal readers.
Do Dyslexics Have Worse Articulatory Knowledge?
A secondary aim of this study was to replicate Montgomery's (1981) finding that
dyslexics have impaired articulatory knowledge. This study differed from Montgomery's
(1981) study in some respects. One, it was unclear how Montgomery's dyslexic subjects
were defined. This study included only those who have impaired phonological skills as
measured by impaired grapheme-to-phoneme conversion. Second, because
Montgomery's (1981) version of the articulatory awareness test was unavailable, the

29
present study used an alternative but similar version of the test, which was based on
Montgomery's task.
Is There Support for the Articulatory Feedback Hypothesis of Naming?
Prediction for individuals with adequate articulatory knowledge. The articulatory
feedback hypothesis of naming stated that having articulatory feedback appropriate to
naming facilitates name retrieval. This study tested this via an interference experimental
design. If having articulatory feedback appropriate to naming facilitates name retrieval,
interfering with that appropriate articulatory feedback should reduce facilitation effects.
Prediction for individuals with impaired articulatory knowledge. The hypothesis
implied that those with poor articulatory knowledge will retrieve names less efficiently
than those with good articulatory knowledge. In an interference paradigm, where
subjects were asked to engage in another task that produced articulatory feedback
incompatible with the naming task at hand, those with poor articulatory knowledge were
predicted to perform differently than controls. Whereas the controls' naming should be
de-facilitated, those with poor articulatory knowledge may respond in one of two ways.
One, because they may not rely in the articulatory feedback system to facilitate name
retrieval in the first place, interfering with articulatory feedback may not produce de¬
facilitation effects as expected with controls. Or possibly, because their articulatory
feedback system was already poor, adding another task with demands on the articulatory
feedback system may exacerbate the difficulty these subjects experience, leading to even
worse naming performance than controls' de-facilitated naming performance.
»>

30
Group differences. If Montgomery's (1981) finding is supported and those with
phonological impairments have worse articulatory knowledge compared to controls, then
performance of phonologicallv impaired subjects can be compared to the performance of
controls. Even if Montgomery's finding is not supported, there is theoretical interest in
comparing the articulatory knowledge of these two groups as it has been well
documented that dyslexic individuals have name retrieval difficulties.
Those with phonological impairment can be further divided into two subgroups:
those with impaired reading skills and those with adequate reading skills. Performance of
these two subgroups can be compared to examine if these subtypes show different
patterns on articulatory knowledge and name retrieval, or if they differ only in the degree
of severity.
To test the hypothesis most directly, subjects can be grouped according to their
performance on a measure of articulatory knowledge. These three ways of grouping
subjects (i.e., phonologically impaired vs. controls; phonologicallv and reading impaired
vs. phonologically impaired with adequate reading vs. controls: poor articulatory
knowledge vs. adequate articulatory knowledge) may yield performance patterns that
further elucidate the relationship between articulatory knowledge and name retrieval.
What Is the Relationship Between Articulatory Knowledge and Phonological Awareness?
Another secondary aim of this study was to elucidate the relationship between
articulatory knowledge and phonological awareness. Much about phonological
awareness among the dyslexic population has been studied, but little is known about
articulatory knowledge. Are they related or independent of one another? What are the

factors that relate to or predict the level of articulatory knowledge and phonological
awareness?

METHODS
Subjects
Three groups of subjects totaling 41 children were recruited from the Gainesville,
Florida and Chicago, Illinois metropolitan areas. Subjects were recruited from offices of
psychologists, speech pathologists, and neurologists, and from flyers distributed
throughout the community. All subjects’ parents gave written informed consent and all
subjects gave oral assent to participate in this study in accordance with the requirements
of the Institutional Review Board of the Health Science Center of the University of
Florida and of the University of Chicago Hospitals.
Inclusionary criteria for subjects included:
• Age 7-12
• Right-handed
• Intelligence quotient between 70 and 130
• English is first and primary language
A lower limit of 7 years of age was selected because reading disability is often not
apparent until school age; a large percentage of children have age-appropriate, limited
reading skills before that time. An upper age limit of 12 was selected because beyond
this age, children with developmental phonological dyslexia have had several years of
struggling with reading. They may have received special services or developed other
skills on their own in order to compensate for their impaired reading. While older
32

33
phonological dyslexic children may still demonstrate naming problems, their retrieval
deficit is often mitigated by late adolescence (Korhonen, 1995; Fawcett & Nicolson,
1994; Felton et al., 1990). Forms of compensation may confound the contribution of
articulatory feedback to name retrieval. The age range was limited between 7-0 and 12-
11 in order to include individuals in the early years of developmental phonological
dyslexia. Right-handedness was selected as a predictor of typical language organization
so that results from subjects in this study can be generalized to the population.
Approximately 98% of right-handed individuals are left hemisphere dominant for
language. The intelligence criterion was constrained to the middle 96% of the
population. Individuals at the extremes of the continuum may not process linguistic
information in the same way as most individuals. The intelligence criterion was set so
that results can be generalized to the population. English was required as subjects’ first
and primary language in order to rule out reading problems due to socio-cultural or
environmental factors.
Exclusionary criteria included:
• History of neurological disorders
• Previous treatment in Lindamood or Orton-Gillingham programs
• Family history of learning disability
A history of neurological disorders, such as cerebral palsy, epilepsy, or Tourette’s
Syndrome, increases the probability of atypical brain organization. Therefore individuals
with neurological histories were excluded. Individuals who have participated in reading
treatment programs described as or based on the Lindamood or Orton-Gillingham
programs were also excluded. These treatment programs include direct or indirect

34
training of articulatory awareness via training of articulatory gestures. A history of
participation in these programs may confound results because subjects’ articulatory
feedback system may no longer reflect its naturalistic connectivity. Family members of
individuals with a learning disability were also excluded because of anatomical studies
suggesting a genetic basis to learning disability (Leonard et al., 1993).
Subjects meeting criteria for the following three groups were recruited:
• Developmental phonological dyslexia (DPD)
• Adequate reader with poor phonology (ARPP)
• Normal reader controls (CTRL)
Group membership was distinguished by performance on three reading
achievement subtests of the Woodcock Reading Mastery Test (WRMT; Woodcock,
1987): Word Identification, Passage Comprehension, and Word Attack. The Word
Identification subtest consisted of single English words that subjects were asked to read.
This subtest assessed subjects’ oral reading of real words, but no comprehension of word
meaning was required. The Passage Comprehension subtest consisted of a short sentence
or paragraph with one missing word. Subjects were required to read the entire passage
and come up with one word that would fill the missing blank. This subtest assessed
subjects’ comprehension of written material. The Word Attack subtest consisted of
nonwords that followed the rules of pronunciation in the English language. Subjects
were required to read these words aloud. This subtest required subjects to use the
grapheme-to-phoneme conversion route to read. Raw scores were converted to age-
corrected standard scores for each of these three measures.

35
Subjects in the DPD group had impaired reading achievement scores on all three
subtests in comparison to that expected given their intellectual aptitude, as assessed with
the Test of Nonverbal Intelligence, 2nd Edition (TONI-2; Brown, Sherbenou, & Johnsen,
1990). Impairment was operationalized as actual achievement score falling at least one
standard deviation (i.e., 15 standard score points) below the expected score, with the
expected score calculated using the formula
Y’= [rXT (Sx / Sy )(IQ - X)] + Y
(Y’ = expected achievement score, r^ = estimated correlation between the TONI-2 and
the WRMT, Sx = standard deviation of WRMT [15], Sy = standard deviation of TONI-2
[15], IQ = obtained TONI-2 Quotient, X = mean of TONI-2 [100], and Y = mean of
WRMT [100]).
Subjects in the ARPP group had impaired phonological skills, operationalized by
impaired actual Word Attack score in comparison to the expected score based on TONI-2
Quotient, but non-impaired reading skills as defined by commensurate actual and
expected Word Identification and Passage Comprehension scores. These subjects, as
subjects in the DPD group, were recruited as poor readers. The categorization into DPD
or ARPP groups was done after each subject's completion of participation.
Subjects in the CTRL group were matched to the other two groups on age and
intelligence. The CTRL group's expected reading achievement scores based on
intellectual aptitude were all commensurate with actual achievement scores. The CTRL
group was not matched to the other two groups on reading age because the primary
purpose of this study was to evaluate name retrieval ability. The ability to retrieve names

36
may be affected by age and intelligence. Thus all three groups were matched on age and
intelligence. Table 1 summarized the grouping criteria.
Table 1. Summary of grouping criteria.
Group
Word Attack
Word Identification
Passage Comprehension
DPD
ASS < ESS
ASS < ESS
ASS < ESS
ARPP
ASS ASS=ESS
ASS = ESS
CTRL
ASS = ESS
ASS = ESS
ASS = ESS
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; ASS = actual standard score; ESS = expected
standard score based on the formula, Y’ = [r^ (Sx / Sy )(IQ - X)] + Y; IQ = TONI-2
Quotient.
The TONI-2 was selected as the measure of intellectual ability for this study
because of its relative lack of dependence on verbal abilities. A problem with measuring
dyslexic children’s general intelligence is that most common measures of intelligence
rely heavily on verbal abilities. Because dyslexic children have impaired reading skills,
they may obtain intelligence scores that are lower than their “true” intellectual capability.
To minimize this problem, intelligence quotient of the TONI-2 was used as the measure
of intelligence for this study. The TONI-2 stimuli consisted of visual patterns with a
missing piece. Subjects were required to choose a pattern to fit the visual sequence from
multiple choices. While good performance on this test may still benefit from
verbalization, the TONI-2 has less verbal demands than most other measures of
intelligence. The TONI-2 can be used with subjects from age 5 to 85. Normative data
was collected from over 2,700 subjects in these age ranges. The TONI-2 Form B's
correlation with the WISC-R FSIQ ranged from .75 to .94. Its correlation with WISC-R

37
VIQ ranged from .63 to .73, and its correlation with WISC-R PIQ ranged from .60 to .87.
Form B was selected to be used in this study because of its relatively more stable
correlation with WISC-R indices compared to Form A. Raw scores on the TONI-2 were
converted to age-corrected TONI-2 Quotients, which have a mean of 100 and a standard
deviation of 15.
Table 2 summarized each group’s demographic data and grouping criteria scores.
All three groups were matched on age (F = 035,p = 0.71), intelligence score (F = 1.50,/?
= 0.24), and grade (F = 0.74, p = 0.48). More males were represented in the DPD group
in comparison to the other two groups. The three groups did differ from each other on
the three reading achievement subtests (F = 7.30, p = 0.00). For each of the three reading
achievement subtests, the DPD group scored uniformly lower than the other groups
(Word Attack: DPD vs. ARPP, t = 2.67, p = 0.02, DPD vs. CTRL, t = 9.65, p = 0.00;
Word Identification: DPD vs. ARPP, t = 4.86,/? = 0.00, DPD vs. CTRL, t = 9.36,/? =
0.00; Passage Comprehension: DPD vs. ARPP, t = 6.48, p = 0.00, DPD vs. CTRL, t =
8. \0,p = 0.00). The ARPP group scored lower than the CTRL group on Word Attack (t
= 7.14,p = 0.00) and Word Identification (t = 4.60,p = 0.00), but not on Passage
Comprehension (t = 1.99,p = 0.06). Within the DPD and CTRL groups, there was no
difference between any of the three achievement scores (DPD, F = 2.82,/? = 0.11; CTRL,
F = 1.24, p = 0.32). The ARPP group, however, demonstrated better Passage
Comprehension compared to Word Identification (t = 7.40,/? = 0.00), which in turn was
better than Word Attack (t = -8.71,/? = 0.00).

38
Table 2. Summary of demographics and grouping criteria scores.
DPD ARPP CTRL
N
11
10
20
Age
9.0(1.1)
9.4 (1.9)
9.5 (1.7)
Grade
3(1)
4(2)
4(2)
M:F Ratio
9:2
6:4
12:8
ADHD M:F Ratio
5:1
3:0
3:0
TONI-2 IQ
103(13)
110(7)
106(8)
Word Attack SS
70 (11 )a
81 (8)b
104 (8)c
Word Identification SS
68 (12)a
89 (7)d
105 (10)c
Passage Comprehension SS
70 (12)a
96 (5)c
103 (10)c
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls. Scores with different superscripts indicate
statistically significant difference.
Descriptive Measures
Subjects were administered a battery of relevant tests to compare performance
between groups and for comparison with other findings in the literature. Data from the
following descriptive measures were used to describe the groups on relevant
neuropsychological variables.
Articulatory Awareness Test (AATi
The Articulatory Awareness Test (AAT) is a non-published, experimental
instrument modeled after Montgomery’s (1981) task. The AAT stimuli consisted of eight
picture cards with cartoon drawings of the sagittal view of the oral cavity (see Appendix

39
1). Each picture represented one or more phoneme. The examiner produced a target
phoneme with her mouth obstructed from subject’s view, then asked the subject to
identify one of out three pictures that corresponds to subject’s articulatory gesture as s/he
produced that phoneme. Subjects were encouraged to repeat the target phoneme as many
times as necessary, and no time limit to responding was imposed. Three practice items
were given before test items were administered, and the examiner went over a sagittal
cartoon drawing to identify each articulator (i.e., tongue, teeth, lips) at the introduction of
the task. In the event that subject produced an atypical articulatory gesture in producing a
phoneme, the subject’s gesture was used in scoring accuracy.
The AAT was produced by the Morris Center of Gainesville, Florida and used as
part of their standard evaluation for reading disability. The AAT consisted of 10 trials,
with 10 additional trials added and piloted during this study (AAT-R). Data on the
original 10 trials were available from 93 patients from the Morris Center. Seventy
percent of these subjects were male (n = 65). Thirty percent were female (n = 28).
Patients’ ages ranged from 6 to 22, with an average of 11 years (standard deviation = 4).
Of these 93 subjects, 86 were classified as dyslexic, 7 were classified as borderline
dyslexic; 81 of these subjects were diagnosed with co-morbid ADHD. Table 3 shows
group means on the AAT (out of possible 10 trials). Dyslexic subjects performed more
poorly on the AAT than borderline dyslexics (t = 2.03, p = 0.04). AAT scores did not
differ between ADHD and non-ADHD groups (t = 1.34, p = 0.18). Because the Morris
Center population did not include normal readers, it was not known whether including
normal readers would yield a bimodal distribution of AAT scores, as Montgomery (1981)
showed with her version of this task. The data available from the Morris Center

40
suggested a normal distribution of scores on the AAT from the dyslexic population. This
test was administered to all subjects in the present study to estimate subjects’ level of
knowledge about articulator position during phoneme production. The number of
accurate responses in 10 trials (AAT) and in 20 trials (AAT-R) was recorded for analysis.
Table 3, AAT scores of the Morris Center population.
n Mean AAT score (SD)
Dyslexic
86
7(2)
Borderline Dyslexic
7
8(1)
ADHD
81
7(2)
Non-ADHD
12
8(1)
Total
93
7(2)
Naming
Subjects were administered the Boston Naming Test (BNT; Kaplan, Goodglass,
Weintraub, & Segal, 1983) as a measure of visual confrontation naming. Z-scores
calculated from the norms published by Spreen and Strauss (1998) were recorded for
analysis. Subjects were also administered the Rapid Color Naming and Rapid Object
Naming subtests of the 1997 experimental version of the Comprehensive Test of
Phonological Processing, which was same as the 1999 published version of the same test
(Wagner, Torgesen, & Rashotte, 1999). Z-scores based on normative data collected in
1997 by the research group developing this battery were calculated and recorded for

41
analysis. These measures assessed subjects’ rapid naming ability and allowed for
comparison of data from the present subject groups to other findings reported in the
literature.
Phonological Awareness
The Lindamood Auditory Conceptualization Test (LAC; Lindamood &
Lindamood, 1979) was administered to all subjects to yield an index of phonological
awareness. This test assessed subjects’ phonological awareness by asking subjects to
manipulate color blocks, with each color representing one phoneme. Phoneme patterns
changed in degrees of difficulty, and subjects manipulated blocks to demonstrate their
perception of how phoneme patterns changed. Raw scores from the LAC were recorded
for analysis.
Attention-Deficit/Hyperactivitv Disorder
Subjects’ parents were interviewed using a semi-structured interview for
symptoms of ADHD based on DSM-IV criteria. This questionnaire asked about each
symptom listed in the DSM-IV, and if the parent endorsed six or more symptoms of
either the inattention and/or hyperactivity/impulsivity cluster, follow-up questions about
age of onset, duration of symptoms, situations where symptoms are exhibited, and extent
of symptoms’ disturbance on functioning were asked. Based on parents’ response to this
questionnaire, subjects were categorized into ADHD or Non-ADHD groups. Because of
the high co-morbidity rate between dyslexia and ADHD, this interview allowed for the

42
description of ADHD rate in the current study groups. The form used during this
interview is presented in Appendix 2.
Experimental Measures
The aim of this study was to test the hypothesis that articulatory feedback
facilitates naming. According to this hypothesis, feedback from appropriate articulator
movements facilitates name retrieval. An interference paradigm was implemented to test
this hypothesis. While subjects attempted to name objects, they were asked to engage in
another task designed to interfere with articulatory movements appropriate to the naming
task.
Naming Assessed via Phoneme Match (NAPM)
During the experimental task, NAPM, subjects were required to look at two
pictures, name those pictures to themselves, and determine if those names end in the
same phoneme. They engaged in two interference conditions while performing this
naming task. During the Mouth Interference condition, subjects were asked to engage
their mouth in the following movement sequence: Lips together (as if making the /m/
soundj-tongue between teeth (as if making the /th/ sound). These movements were
demonstrated without accompanying phonemic sounds. Because subjects’ articulators
were engaged in this interference movement, they could not orally name the objects seen.
Therefore naming was assessed by asking subjects to decide if the names of the two
objects seen during each trial terminated in the same sound. They indicated their

43
response by pressing designated buttons. In order to perform this task, subjects must first
name the two objects, then judge if those names have matching end phonemes.
During the Foot Interference condition, subjects were asked to move their left foot
in a rocking movement alternating between heel and toe, while naming pictures and
deciding if the names' end phoneme matched. This condition was implemented to control
for the attentional demands of engaging in an interference task. Subjects engaged in
Mouth Interference while performing the NAPM task during half of the trials and
engaged in Foot Interference during the other half of the trials. The order of the
interference condition was counterbalanced across subjects.
Each interference condition consisted of 32 trials. Half of the trials had word
pairs with matching end phonemes and the other half had word pairs with non-matching
end phonemes. The 64 word pairs were divided into two stimulus sets and are presented
in Appendix 3. The two stimulus sets were balanced on word frequency (Francis &
Kucera, 1982), number of syllables, and grade level by which the word is taught
(Thorndike & Lorge, 1972), Simple black and white line drawings eliciting each target
were drawn from the Snodgrass and Vanderwart picture set (1980), Peabody Picture
Vocabulary Test (Dunn & Dunn, 1981), and Boston Naming Test (Kaplan et al„ 1983).
In some instances, a lack of available drawings necessitated the use of locally produced
drawings, which were produced to be of similar visual complexity level as pictures from
above mentioned sets. Each stimulus set was used during Mouth Interference half the
time and during Foot Interference half the time.
These stimuli were presented to subjects on a laptop computer via a program
written with PsychLab v.6.0.2. Each trial began with a fixation mark lasting 1000 msec.

44
Then an auditory cue alerted subjects to the onset of pictured stimuli, which remained on
the screen until subjects pressed one of two acceptable keys. The computer recorded
subjects’ response and reaction time from the onset of the stimuli presentation to key
press. The examiner monitored subjects’ interference movement and recorded the
number of movement cycles completed during each trial. One movement cycles during
the Mouth Interference was defined as lip closure followed by intrusion of the tongue
between teeth. One cycle during the Foot Interference was defined as toe touching the
floor followed by heel touching the floor. A different auditory cue followed subjects’
response and marked the end of a trial. The screen then remained blank until the
examiner pressed one of two keys marking that trial as valid or invalid. No time limit on
response time was imposed. Because the next trial did not begin until the examiner
pressed one of two keys, the examiner controlled the pace of the testing and implemented
breaks as appropriate for each subject.
The NAPM began with six practice trials, during which subjects performed the
NAPM task without any interference. Each interference condition (Mouth and Foot
Interference) began with a demonstration of the interference task, followed by four
practice trials with interference. Subjects were instructed to engage in the interference
movement before the onset of each trial. After the practice trials, subjects were informed
that testing will begin. The first two trials were used as buffer trials (i.e., data were not
recorded) without subjects’ knowledge. The 32 experimental trials followed. Data
recorded during each trial included response reaction time, response (to calculate
accuracy percentage), and number of interfering movements produced (for calculating

45
average frequency of movement). Time taken to complete the NAPM ranged between 15
to 20 minutes.
Visual Match
A visual match task was implemented to control for potentially different
attentional demands of Mouth and Foot Interference. This was a nonverbal, visual match
task requiring subjects to determine if one of four pictures matched a target. Subjects
engage in Mouth and Foot Interference during this task as well. If subjects’ performance
during the Mouth and Foot Interference conditions differed on this non-verbal task, that
would suggest that mouth movements and foot movements have different levels of
interfering effect.
Similar to the NAPM, Visual Match also had 32 trials for each interference
condition. Each trial composed of one target picture at the top of the screen and four
other pictures at the bottom of the screen. Subjects were instructed to press one of two
keys indicating whether there was a match between the four pictures on the bottom and
the target on top. Half of the trials had matching pictures and the other half had non¬
matching pictures. Pictures were taken from the Test of Visual-Perceptual Skills (non-
motorj-Revised (Gardner, 1996), and were selected for their difficulty to be verbalized.
One set of stimuli was constructed first. The second set was constructed by changing the
target and/or the ordering of the four pictures on the bottom.
The Visual Match Test was presented to subjects on a laptop computer via a
program written with PsychLab v.6.0.2. Experimental parameters mirrored the NAPM
parameters as much as possible. Each trial began with a fixation mark lasting 1000 msec.

46
Then an auditory cue alerted subjects to the onset of pictured stimuli, which remained on
the screen until subjects pressed one of two acceptable keys. No time limit to response
was imposed. The computer recorded subjects’ response and reaction time. The
examiner recorded the number of interference movement cycles completed during each
trial. A different auditory cue followed subjects’ response and marked the end of a trial.
The screen then remained blank until the examiner pressed one of two keys marking that
trial as valid or invalid.
Similar to the NAPM, the Visual Match Test also began with six practice trials,
during which subjects performed the Visual Match task without any interference.
Interference conditions (Mouth and Foot Interference) then followed. Each condition
began with a demonstration of the interference task, followed by four practice trials with
interference. Subjects were instructed to engage in interference movement before the
onset of each trial. After the practice trials, subjects were informed that testing will
begin. The first two trials were used as buffer trials (i.e., data were not recorded) without
subjects’ knowledge. Thirty-two experimental trials followed. The order of the
interference conditions was counterbalanced across subjects. Data recorded during each
trail included response reaction time, response accuracy, and the number of interfering
movements produced (for calculating average frequency of movement). Time taken to
complete the Visual Match Test ranged between 15 to 25 minutes.
Phoneme Match
A Phoneme Match Test was implemented to control for the potential difference in
subjects" ability to determine if the end phoneme of word pairs matched. This was a

47
necessary control given that subjects’ naming performance during the NAPM was
measured via their ability to determine matching phonemes. Subjects completed this task
without any interference. Stimuli were two sets of word pairs used during the NAPM.
These stimuli were presented aurally to subjects on a laptop computer via a program
written with PsychLab v.6.0.2. Each trial began with the word “listen,” which stayed on
the screen until the end of the trial. A pair of words was presented by the computer 1500
msec after the onset of the word “listen.” Subjects pressed one of two keys to indicate if
the two words’ last phonemes matched. The computer recorded subjects’ response and
reaction time from the onset of stimulus presentation. The screen then remained blank
until the examiner pressed one of two keys marking that trial as valid or invalid. No time
limit on response time was imposed.
The Phoneme Match Test included four practice trials followed by the two
stimulus sets, which totaled 64 trials. The order of the two stimulus sets was
counterbalanced across subjects. Data recorded during each trial included response
reaction time and response accuracy. Time taken to complete the Phoneme Match Test
ranged between 5 to 10 minutes.
Naming Test
A visual confrontation naming test was administered after the completion of the
NAPM, Visual Match, and the Phoneme Match Test. This Naming Test composed of
black and white line drawings from the NAPM. Each subject was asked to say the name
that they assigned the pictured item when they saw it during the NAPM task. For cases
where subjects stated an acceptable alternative response for an item (e.g., “bunny” for

48
“rabbit’’), the name that the subject gave was used to determine if the two items from that
NAPM trial had names with matching end phonemes, and the subject’s response
accuracy for that trial was determined accordingly. For cases where subjects were not
able to produce a response because of unfamiliarity with the object, the NAPM trial
including that object was deleted. Thus response to objects for which subjects were
unfamiliar was not included in data analysis.
Procedures
The examiner first interviewed the parent over the telephone to obtain each
subject’s background information and to screen for ADHD. Based on this information,
subjects were assigned a subject number using the chart represented in Table 4. This
chart counterbalanced the order of task presentation (i.e., NAPM or Visual Match), the
order of stimulus sets used, and the order of interference conditions. Each subject’s order
of test presentation, stimulus set used, and order of interference condition was determined
based on his/her assigned subject number.
Subjects completed the testing in either one or two settings, totaling 1.5 hour for
older normal readers to 3 hours for younger subjects with reading problems. Factors
influencing whether testing was completed in one or two settings included each subject’s
time availability and their performance during the first hour. For those subjects who
experienced difficulty, testing was completed in two sessions to minimize their
frustration. Subjects were encouraged and praised for their effort rather than for their
accuracy. In no instance were subjects given feedback about the accuracy of their

49
Table 4. Sample of the chart for determining the order of task, interference, and stimulus
set for each subject.
Controls
Phonologically
Impaired
Order of
NAPM
Visual Match
NAPM
Visual Match
stimulus set
first
first
first
first
AB
1001
1002
2001
2002
Mouth
BA
1003
1004
2003
2004
Interference
AB
1005
1006
2005
2006
first
BA
1007
1008
2007
2008
AB
1009
1010
2009
2010
BA
1011
1012
2011
2012
Foot
AB
1013
1014
2013
2014
Interference
BA
1015
1016
2015
2016
first
AB
1017
1018
2017
2018
BA
1019
1020
2019
2020
response. The order in which testing components were completed is represented in Table
5.
The Test of Phonological Awareness (Torgesen & Bryant, 1994) was used to train
subjects to match end phonemes of words, thus familiarizing them to the task demand of
the NAPM. Subjects’ performance on this task was not scored. The examiner remained
with subjects throughout testing. During the NAPM and Visual Match tasks, the
examiner monitored subjects’ interference movements (mouth or foot) and reminded

Table 5. Order of test administration.
Telephone interview:
50
Demographic Questionnaire
ADHD Questionnaire
Testing session:
Test of Phonological Awareness
NAPM and Visual Match
Mouth and Foot Interference
Phoneme Match Test
Naming Test
Boston Naming Test
Articulatory Awareness Test
Lindamood Auditory Conceptualization Test
Woodcock Reading Mastery Test:
Word Identification
Word Attack
Passage Comprehension
Rapid Color Naming and Rapid Object Naming
subjects to engage in these movements during instances when they stopped. These parts
of the session were videotaped.

RESULTS
The statistical program, SPSS v7.5.2 for Windows, was used to analyze the
following data. Both means and medians were examined as central tendency statistics for
reaction time data (i.e., NAPM, Visual Match, Phoneme Match). Because reaction times
are subject to floor effects and have unlimited ceilings, distribution of scores may be
skewed and means may be overly influenced by extremely slow reaction times. Thus
extreme scores were trimmed in the following way in calculating means for each subject.
For each subject’s performance in each condition (i.e., NAPM Mouth Interference,
NAPM Foot Interference, Visual Match Mouth Interference, Visual Match Foot
Interference, Phoneme Match Set A, Phoneme Match Set B), mean reaction time and
standard deviation were calculated. Extreme scores that lay outside of two standard
deviations from the mean were excluded, yielding a trimmed mean for each condition for
each subject. There was no difference in the pattern of results using trimmed means and
medians. Thus results reported here were based on trimmed means.
Articulatory Knowledge
The Articulatory Feedback Hypothesis of Naming stated that articulatory
feedback facilitates naming. This hypothesis implied that better articulatory knowledge
should be correlated with faster reaction time on a naming test. This was tested using a
regression analysis with performance on the Articulatory Awareness Test as the
51

52
independent variable and reaction time on experimental measures as dependent variable.
The experimental measures considered here included the NAPM and Visual Match,
Mouth and Foot Interference conditions. The Visual Match was included as a non-verbal
control task. The hypothesis predicted a significant correlation between Articulatory
Awareness Test scores and NAPM, a name retrieval task, but not with Visual Match, a
nonverbal control task. The two interference conditions of the NAPM were predicted to
have different correlations with articulatory knowledge because subjects were able to use
appropriate articulatory feedback in one condition (i.e., Foot Interference) but not in the
other (i.e., Mouth Interference). In the condition where subjects were able to use
appropriate articulatory feedback (i.e., Foot Interference), better articulatory knowledge
was expected to be associated with faster naming time. In the condition where subjects
were not able to use articulatory feedback (i.e., Mouth Interference), reaction time was
expected to be slow; thus a non-significant correlation between articulatory knowledge
and naming time may be seen.
The Pearson correlations between reaction time on experimental measures and
scores on the Articulatory Awareness Test (10-item version) and the Articulatory
Awareness Test-Revised (AAT-R, 20-item version) were reported in Table 6. Reaction
time during the two interference conditions of the NAPM were either significantly
correlated with or approaching significance with the AAT (Mouth F = 4.52, p = .04; Foot
F = 3.48,/? = .07) and the AAT-R scores (Mouth F = 5.64,/? = .02; Foot F = 3.74,/? =
.06), whereas there was no relationship between reaction time on the Visual Match Test
and articulatory knowledge (AAT: Mouth F = 0.21,/? = .65; Foot F = 0.92,/? = .34; AAT-
R: Mouth F = 0.23, p = .64; Foot F = 0.37,/? = .55). This pattern indicated that

53
increasing articulatory knowledge was associated with faster reaction time during name
retrieval but not during a nonverbal visual match task. However, the prediction that
articulatory knowledge would be more highly correlated with naming latency during the
Foot Interference than during the Mouth Interference condition was not supported.
Table 6. Pearson correlations betw een the Articulatory Awareness Test (AAT) score and
AAT
AAT-R
NAPM
Mouth Interference
-.32*
-.36*
Foot Interference
-,29+
I
o
+
Visual Match
Mouth Interference
.07
.08
Foot Interference
.15
.10
Note: * p< .05, + p<
.10.
Phonolouicallv Impaired vs. Controls
Montgomery (1981) found a difference in articulatory knowledge between
dyslexic children and normal readers. To examine if that finding can be replicated
among the present subjects, the difference in articulatory knowledge between normal
readers and phonologically impaired readers was examined. The Phonologically
Impaired (PI) group was composed of the DPD and ARPP subjects.
â– s

54
Articulatory Awareness Test
Scores obtained by PI and CTRL groups were reported in Table 7. A t-test of
independent samples was utilized to test for significant differences between the two
groups. Unlike Montgomery’s (1981) finding, there was no difference between PI and
CTRL groups on articulatory knowledge as assessed by the 10-item version of the AAT (t
= 0.80, p = .43) or by the 20-item version (AAT-R, t = 0.64,/? = .53).
Table 7. AAT and AAT-R scores obtained by Phonologically Impaired (PI) and Control
(CTRL) groups.
PI
CTRL
AAT
6.19(2.27)
6.70(1.75)
AAT-R
12.14(3.55)
12.85 (3.53)
Although no difference was found between groups on articulatory knowledge
measures, it was possible that subjects with phonological impairment may demonstrate a
different pattern of relationship between articulatory knowledge and name retrieval
compared to controls. Thus Pearson correlations between the AAT score and reaction
time during each experimental condition (i.e., NAPM and Visual Match Mouth and Foot
Interference conditions) were examined for each group. Because the AAT and AAT-R
scores yielded similar pattern of results, only AAT scores were reported.
Table 8 showed Pearson correlations between the AAT score and reaction time
during experimental conditions for each group. The correlation between the articulatory
knowledge score and naming latency (i.e., NAPM) seen in the analysis with all subjects
combined was driven by the control subjects (Mouth, F = 5.31,/? = .03; Foot, F = 2.53,/?

55
= .13). The speed by which PI subjects retrieved names was not related to their level of
articulatory knowledge (Mouth, F = 0.65,p = .43; Foot, F = 0.84,/? = .37). Neither
groups’ AAT score was correlated with their performance on the visual match control.
The control group's correlation between articulatory knowledge score and naming latency
during Mouth Interference differed from their correlation between articulatory knowledge
and visual match latency during Mouth Interference (-0.48 vs. 0.19; Z(2o,2o>= -2.07,p<
.05; subscripts denote the sample size of groups being compared). No other pairs of
correlation differed from each other.
Table 8. Pearson correlations between the AAT score and reaction time on experimental
measures for PI and CTRL groups.
PI
CTRL
NAPM
Mouth Interference
-.18
*
OO
r
Foot Interference
-.21
-.35
Visual Match
Mouth Interference
-.03
.19
Foot Interference
.06
.27
Note: PI = Phonologicallv Impaired; CTRL = Controls;' p < .05.
Descriptive Measures
Subjects’ performance on descriptive measures were reported in Table 9. Scores
were compared by t-test of independent samples. On measures of naming, the PI group
performed more poorly on the BNT (t = 2.05, p = .05), but not on Rapid Color Naming (t
= 1.54,p = .13), Rapid Object Naming (t = 1.47,/? = .15), or on the experimental Naming

56
Test (t = 1.58,p = .12). The PI subjects scored significantly lower than the CTRL
subjects on the LAC, our measure of phonological awareness (t = 3.12, p = .00). The
TONI-2 and reading achievement scores were also reported to contrast between PI and
CTRL groups. The PI did not differ from the CTRL group on intellectual aptitude as
measured by the TONI-2 (t = -0.14, p = .89), but their reading achievement scores were
all worse than their age- and intelligence-matched controls (Word Attack, t = 9.28, p =
.00; Word Identification, t = 7.04, p = .00; Passage Comprehension, t = 4.87, p = .00).
Table 9. PI and CTRL groups’ performance on descriptive measures.
PI
CTRL
BNTa
-1.96 (2.07)e
-0.85 (1.30)r
Rapid Color Naming3
-0.58(1.57)
0.02 (0.68)
Rapid Object Naming3
-0.88 (2.36)
-0.05 (0.80)
Naming Testh
87(6)
90 (6)
LACC
55 (17)e
75 (23)f
TONI-2d
106(11)
106(8)
WRMT Word Attackd
75 (1 l)e
104 (8)f
WRMT Word Identification11
78 (14)e
105(10/
WRMT Passage Comprehension11
83 (16)e
103(10/
Note: PI = Phonologically Impaired; CTRL = Controls;a Z-scores. b Percentage correct.
c Raw score. d Age-corrected standard scores. Within each row, numbers with different
superscripts were significantly different from each other.

Experimental Measures
57
Phoneme match. Before group differences on the NAPM were further examined,
subjects’ ability to match phonemes was evaluated first. Name retrieval on the NAPM
was assessed via subjects’ ability to match the end phoneme of words. Thus it was
important to know if groups differed from each other on this ability. A multivariate
analysis of variance (MANOVA) was performed on the reaction time data from the
Phoneme Match Test with Group (PI vs. CTRL) as a between-subject variable and
Stimulus Set (A and B) as a within-subject variable. This analysis also allowed for
examination of differences between stimulus sets. Mean reaction time in milliseconds
and standard deviations were presented in Table 10. The Group X Stimulus Set
interaction was significant (F = 5.86, p = 0.02). Within each group, reaction times for the
two stimulus sets were compared using dependent samples t-test. The CTRL group’s
reaction time on stimulus sets did not differ (t = 0.59, p = .56) while PI subjects were
faster in responding to Set A than to Set B (t = -2.51, p = .02). The group reaction time
for each stimulus set was compared using independent samples t-test. The two groups’
reactions times did not differ for Set A (t = -0.87,p = .39). The CTRL group was faster
than the PI group on Set B (t = -2.08,p = .04). A similar analysis was conducted on
response accuracy. This revealed a marginally significant Group effect (F = 3.57, p =
.07). The CTRL group tended to be more accurate than the PI group.
The two stimulus sets did not differ for the CTRL group, but for the PI group, Set
B was responded to more slowly and therefore it may have been harder. The most
important finding here was that the two groups did not differ in their ability to perform
the Phoneme Match Test, as measured by both reaction time (F = 2.85, /? = .10) and

58
Table 10. Means and standard deviations of reaction time (RT in milliseconds) and
accuracy (% Correct) on the Phoneme Match Test.
PI CTRL
RT % Correct RT % Correct
Stimulus Set A 2899(579)a 90(13) 2758(445) 95 (5)
Stimulus Set B 3224 (966)b 89 (11) 2711 (543)a 94(7)
Note: PI = Phonologically Impaired: CTRL = Controls. Numbers with different
superscripts were significantly different from each other.
accuracy (F = 3.57, p = .07). Because there was no statistically significant group effect,
and because the counterbalance measures taken to pair Set A with Mouth Interference
approximately half of the time and with Foot Interference the other half of the time, the
difference in difficulty level between Set A and Set B was considered to be equally
dispersed among interference conditions. No further attempt to examine Stimulus Sets'
interaction with other variables in subsequent analyses was taken because the number of
subjects in this study limited the power available to detect such high level interactions.
NAPM and visual match. Subjects’ performance on the NAPM and Visual Match
were reported in Table 11. Separate MANOVAs were conducted for reaction time and
accuracy, with Group as a between-subject factor (PI vs. CTRL), and Task (NAPM and
Visual Match) and Interference (Mouth and Foot) as within-subject factors. The
MANOVA for reaction time revealed a Group X Task interaction (F = 6.67,/? = .01) and
a Task main effect (F = 27.23, p = .00). Subjects responded to NAPM faster than to
Visual Match. Because reaction time to each task was not important theoretically,
separate MANOVAs were conducted to further examine group differences and potential
interference effects within each task. The MANOVA for NAPM revealed a marginal

59
Group effect (F = 3.91, p = .06), with the CTRL group responding faster than the PI
group. The MANOVA for Visual Match revealed no significant interactions or effects.
Table 11. Reaction time and accuracy on the NAPM and Visual Match Tests for PI and
PI
RT
% Correct
CTRL
RT
% Correct
NAPM
Mouth
4353 (1547)
81 (15)
3688(1551)
89(10)
Foot
4591 (141 1)
83 (8)
3653 (1135)
89(10)
Visual Match
Mouth
4946(1788)
77 (13)a
5333 (1694)
86(13)
Foot
5004(1333)
84 (9)b
4984(1719)
84(13)
Note: PI = Phonologically Impaired; CTRL = Controls. Numbers with different
superscripts were statistically different from each other.
The MANOVA on accuracy data revealed a Group X Interference interaction (F =
4.11 ,p = .05) and a Group effect (F = 4.09, p = .05). Dependent samples t-test to
compare the accuracy difference between interference conditions indicated that with the
NAPM and Visual Match tasks combined, the CTRL group’s accuracy on Mouth and
Foot Interference conditions was the same (t = -0.72,/? = .48), whereas the PI group
achieved marginally less accuracy on Mouth Interference compared to Foot Interference
(t = 1.94,/? = .07). Independent samples t-test to compare accuracy between groups
showed that the PI group’s accuracy on Mouth Interference was statistically less than the
CTRL group’s accuracy during this condition (t = 2.38, p = .02). Because there were a
priori reasons to examine the difference between interference conditions separately for

60
each task, and to examine if groups differed in this difference, dependent samples t-tests
were conducted separately for the NAPM and the Visual Match Test. The only
difference was found in the PI group's accuracy performance on the Visual Match Test.
They were less accurate during the Mouth Interference condition than during the Foot
Interference condition (t = 2.30, p = .03). Because their reaction time was not different
between these conditions (t = 0.28,p = .78), a speed-accuracy trade off was not a likely
explanation for their worse accuracy during the Mouth Interference condition.
Block effect. The present study included 12 individuals with ADHD. Children
with ADHD may have decreased sustained attention span and/or slowed reaction time.
Thus trials were divided into two blocks to examine if performance during the first half
of each condition differed from performance during the second half. A MANOVA on
reaction time data with Group (PI vs. CTRL) as a between-subject factor and Task
(NAPM and Visual Match), Interference (Mouth and Foot), and Block (1 and 2) as
within-subject factors revealed a significant Task X Block interaction (F = 16.15, p = .00)
and Block effect (F = 5.06,/? = .03), in addition to the Task X Group interaction and
Task effect already reported above. Follow up MANOVAs were conducted for each
Task. No significant effects were found for NAPM, but for Visual Match, a Block X
Group interaction (F = 4.98,/? = .03) was found as well as a Block effect (F = 20.11,/? =
.00). Table 12 showed the reaction time on the NAPM and Visual Match tasks broken
down by Block. Dependent samples t-test indicated that the CTRL group’s reaction time
during Block 1 of Visual Match was much faster than their reaction time during Block 2
(t = -4.49,/? = .00), but such a difference was not found for the PI group (t = -1.69,/? =
.11).

Table 12. Means and standard deviations of reaction time for each block.
61
PI
CTRL
NAPM
Block 1
4582 (1476)
3784(1462)
Block 2
4374(1277)
3581(1206)
Visual Match
Block 1
4828(1451)
4768 (1387)a
Block 2
5095 (1606)
5564 (1908)b
Note: PI = Phonologically Impaired; CTRL = Controls. Numbers with different
superscripts were significantly different from each other.
Similar analyses were conducted for response accuracy. The MANOVA revealed
a significant Task X Interference X Block interaction (F = 5.16,/? = .03), Task X Block
interaction (F = 7.42,/? = .01), and Block main effect (F = 4.0,/? = .05), as well as an
Interference X Group interaction and Group main effect. The latter two were discussed
already in the section on accuracy and so were not discussed here. Of the three findings
involving Block, only the highest level interaction was examined because lower level
interactions were represented in the higher level interaction. Table 13 showed the
response accuracy pattern reflected by the Task X Interference X Block interaction.
Dependent samples t-tests were conducted to compare all possible pairs of scores.
Subjects as one group became less accurate during the second block of Visual Match
Mouth Interference (Block 1 vs. Block 2, t = 3.27,/? = .00; Block 2, Mouth vs. Foot
Interference, t = 3.15,/? = .00). No other pair of scores was statistically different from
each other.

62
Table 13. Response accuracy (percentage) reflecting the Task X Interference X Block
interaction.
Mouth
Foot
NAPM
Block 1
83
86
Block 2
86
85
Visual Match
Block 1
85a
85
Block 2
77b
83a
Note: Numbers with different superscripts were significantly different from each other.
Separate MANOVAs were conducted for Block 1 and Block 2 to further explore
how time influenced data. Table 14 summarized and compared the overall findings to
Block 1 and Block 2 findings. The reaction time data were fairly consistent across the
overall analysis and the two time blocks. The overall Group X Task interaction reflected
faster response to the NAPM task by the CTRL group (see Table 11), but there was no
difference between tasks in the PI group’s reaction time. The increase in the Group X
Task interaction from non-significance in Block 1 to significance in Block 2 could be
understood by comparing Table 15 with Table 16. The CTRL group became faster on the
NAPM with practice, but they slowed down significantly on the Visual Match with time.
While such a pattern was also evident with the PI group, their reaction time difference
between Block 1 and Block 2 was not as dramatic.
Table 14 also shows differences in response accuracy findings between blocks.
While the Group X Interference interaction was not significant in Block 2, the pattern of

63
Table 14. Comparison of overall findings with Block 1 and Block 2 findings.
Overall Block 1 Block 2
F p E U E R
Reaction Time
Group X Task
Task
Accuracy
Group X Interference
Group
Task X Interference
Task
6.67
.01
2.89
27.23
.01
8.05
4.11
.05
4.49
4.09
.05
4.75
.24
.63
.92
1.90
.18
.06
.10
8.86
.00
.01
40.70
.00
.04
2.93
.10
.04
2.89
.10
.34
5.71
.02
.81
5.31
.03
results in Block 2 was consistent with the pattern in Block 1 such that the overall Group
X Interference interaction was significant. This interaction showed that the PI group was
less accurate during Mouth Interference than Foot Interference, while the CTRL group’s
accuracy during both interference conditions was commensurate (see Table 11). The
Task X Interference interaction significant in Block 2 was not significant in the overall
analysis, suggesting a great degree of variability during Block 1. The Block 2 Task X
Interference interaction reflected worse accuracy during the Visual Match Mouth
Interference condition in comparison to the Visual Match Foot Interference condition and
the two NAPM interference conditions. As can be seen in Table 16, this effect was
mainly driven by the PI group’s performance.
The Block effects found were not anticipated a priori. Fatigue cannot completely
explain differences between blocks because while reaction time slowed down for Visual
■»>

64
Table 15. Block 1 reaction time and accuracy on the NAPM and Visual Match Test for
PI
CTRL
RT
% Correct
RT
% Correct
NAPM
Mouth
4453 (1757)
78(16)
3874(1944)
88(10)
Foot
4710(1553)
84 (9)
3695 (1121)
88(12)
Visual Match
Mouth
4797(1651)
81(16)
4910(1454)
89 (9)
Foot
4859(1408)
85(11)
4625(1474)
85(14)
Note: PI =
: Phonologically Impaired; CTRL = Controls.
Table 16. Block 2 reaction time and accuracy on the NAPM and Vis
PI and CTRL groups.
PI
CTRL
RT
% Correct
RT
% Correct
NAPM
Mouth
4264(1461)
83(17)
3543 (1362)
89(12)
Foot
4484(1366)
82(12)
3619(1292)
88(12)
Visual Match
Mouth
5035 (1931)
72(16)
5768(2024)
82(19)
Foot
5156(1407)
83(10)
5361(2027)
83(13)
Note: PI = Phonologically Impaired; CTRL = Controls.
Match, it speeded up for NAPM (Table 12). That accuracy was worse just during Visual
Match Mouth Interference but not during Foot Interference also argued against an overall
â– s

65
fatigue effect (Table 13). Because the role of time was unclear. Block effects were also
examined in subsequent analyses.
Excluding ADHD subjects. To examine how ADHD subjects’ reaction time
differed from subjects without ADHD, their reaction time and accuracy data were
contrasted from non-ADHD subjects in Table 17. Overall, subjects with ADHD tended
to be slower and less accurate than their non-ADHD counterparts. Thus subjects with
ADHD were excluded in a MANOVA to examine if ADHD subjects contributed to the
findings involving Block. Similar to above, Group (PI vs. CTRL) was the between-
subject factor, and Task (NAPM and Visual Match), Interference (Mouth and Foot), and
Block (1 and 2) were within-subject factors. This analysis was similar to results reported
with the ADHD subjects included. Significant interactions from reaction time data
included Task X Block (F = 11.66, p = .00) and Group X Block (F = 5.02, p = .03).
Significant main effects included Task (F = 20.47,/? = .00) and Block (F = 7.32, p = .01).
Dependent samples t-test to follow up the Task X Block interaction indicated that non-
ADHD subjects were slower during Block 2 of Visual Match (t = -4.36,p = .00), but
their reaction times did not differ between NAPM Block 1 and Block 2 (t = 0.81,/? =
.42). Dependent samples t-test to follow up the Group X Block interaction indicated that
the non-ADHD CTRL subjects were slower during Block 2 than Block 1 (t = -4.10,/? =
.00), but the non-ADHD PI subjects did not show a difference in reaction time between
blocks (t = -0.28,/? = .78). Excluding ADHD subjects did not change theoretically
important pattern of findings (i.e., Group X Interference interaction). Repeating this
MANOVA with accuracy data revealed no significant effects or interactions. Thus
ADHD subjects were included in all subsequent analyses.

66
Table 17. Reaction time and accuracy of the Non-ADHD and ADHD subgroups on the
NAPM and Visual Match Tests.
Non-
ADHD
(n-12)
PI
ADHD
(n = 9)
Non-
ADHD
(n-17)
CTRL
ADHD
(n-3)
RT
%
RT
%
RT
%
RT
%
Correct
Correct
Correct
Correct
NAPM
4109
84
4679
76
3432
91
5141
75
Mouth
(1181)
(12)
(1964)
(18)
(1107)
(7)
(3055)
(15)
Foot
4481
86
4739
79
3587
91
4028
77
(1692)
(7)
(999)
(8)
(1109)
(8)
(1467)
(17)
Visual
Match
5002
82
4871
71
5253
87
5786
81
Mouth
(1532)
(7)
(2179)
(17)
(1733)
(14)
(1688)
(8)
Foot
4926
82
5108
85
5020
85
4780
78
(1225)
(7)
(1536)
(12)
(1804)
(13)
(1404)
(12)
Note: PI = Phonologically Impaired; CTRL = Controls.
Interference movements. The number of interference movements (mouth or foot)
subjects completed during each trial was recorded. The average number of movements
completed per trial within each condition (i.e., NAPM Mouth and Foot Interference,
Visual Match Mouth and Foot Interference) was divided by the average reaction time per
-s

67
trial for that condition to yield a movement frequency index (i.e., number of movements
per second). Post hoc analyses were conducted with these data to determine if subjects
engaged in mouth and foot interfering movements with equal facility. A MANOVA with
Group (PI vs. CTRL) as the between-subject factor and Task (NAPM and Visual Match),
Interference (Mouth and Foot), and Block (1 and 2) as within-subject factors was
conducted on the interfering movement frequency data. This revealed a significant
Group X Task X Interference X Block interaction (F = 4.35, p = .04), as well as
Interference (F = 4.51, p = .04) and Block (F = 4.24, p = .05) main effects. Descriptive
statistics were reported in Table 18. Perusal of these descriptive statistics revealed the
following: There was no difference between PI and CTRL groups on Visual Match
Mouth or Foot Interference, even when interference conditions were broken down by
Block. There was also no difference between PI and CTRL groups on NAPM Foot
Interference, even when broken down by Block. However, on NAPM Mouth
Interference, the PI group produced interfering mouth movements more slowly than the
CTRL group. This difference was more salient during Block 1 than Block 2.
To examine how interference movement frequency related to subjects’
articulatory knowledge, Pearson correlations between the AAT score and interfering
movement frequency indices were calculated and reported in Table 19. Significant
correlations were found between the PI group's AAT score and their facility in producing
interfering mouth movements during NAPM. The better the PI group’s articulatory
knowledge, the faster they were able to produce interfering mouth movements while
engaging in a name retrieval task. Unexpectedly, the PI group’s AAT score was also
correlated with their facility in producing interfering foot movements during the Visual

68
Table 18. Interfering movement frequency index (i.e., number of movements per second)
for the PI and CTRL groups.
PI
CTRL
NAPM
Mouth
.85 (.24)
.97 (.32)
Block 1
.84 (.25)
.98 (.35)
Block 2
.86 (.26)
.95 (.31)
Foot
1.00 (.25)
1.01 (.26)
Block 1
.98 (.25)
.97 (.26)
Block 2
1.03 (.26)
1.05 (.27)
Visual Match
Mouth
1.00 (.23)
.99 (.23)
Block 1
.99 (.24)
.98 (.25)
Block 2
.99 (.22)
1.01 (.24)
Foot
1.04 (.25)
1.04 (.30)
Block 1
1.01 (.26)
1.05 (.32)
Block 2
1.06 (.26)
1.03 (.30)
Note: PI = Phonologically Impaired; CTRL = Controls.
Match Test. The better their articulatory knowledge, the more slowly PI subjects
produced interfering foot movements during a nonverbal visual task.
Predictors of Articulatory' Knowledge
As a post hoc exploration of variables that predict performance on the AAT, age,
BNT Z-score, Rapid Color Naming Z-score, Rapid Object Naming Z-score, LAC raw

69
Table 19. Pearson correlations between the AAT score and interfering movement index
for PI and CTRL groups.
PI
CTRL
NAPM
Mouth Interference
.49*
-.14
Foot Interference
-.03
-.24
Visual Match
Mouth Interference
.02
-.14
Foot Interference
-.44*
-.13
Note: PI = Phonologically Impaired; CTRL = Controls;* p < .05.
score, TONI-2 Quotient, and the three reading achievement standard scores (Word
Attack, Word Identification, and Passage Comprehension) were entered into a stepwise
regression analysis. No variables were selected as statistically significant predictor of
AAT performance. This stepwise regression analysis was repeated for each group
separately to determine if predictors of AAT performance differed by group. Again, no
variables were selected as significant predictors of AAT score. Pearson correlation
between the AAT score and each of the variables entered were presented in Table 20.
The only variable from Table 20 that correlated with the AAT score was the LAC raw
score, and this correlation was significant only for the CTRL group.
Pearson correlations between the AAT score and the Phoneme Match response
time and accuracy were also calculated to examine the relationship between articulatory
knowledge and another task requiring phonological skills. The AAT score was positively
correlated with Phoneme Match accuracy (Pearson's r = .64, p = .00). Subjects with
better articulatory knowledge were more accurate on the Phoneme Match Test. Dividing

70
Table 20. Pearson correlations between the AAT score and variables entered into
stepwise regression analysis for PI and CTRL groups.
Groups Combined
PI
CTRL
Age
.01
-.19
.23
BNT
.03
-.16
.35
Rapid Color Naming
.09
-.02
.31
Rapid Object Naming
.00
-.11
.28
LAC
.30*
.18
.40*
TONI-2
-.24
-.25
-.23
WRMT Word Attack
.18
.23
-.00
WRMT Word Identification
.15
.16
-.04
WRMT Passage Comprehension
.11
.15
-.18
Note: PI = Phonologically Impaired; CTRL = Controls; *
P<=
05.
subjects into groups showed that this correlation was significant only for the
phonologically impaired group (Pearson's r = .74, p - .00).
Predictors of Phonological Awareness
Predictors of the LAC score were also explored with stepwise regression analysis.
Age, BNT Z-score, Rapid Color Naming Z-score, Rapid Object Naming Z-score, TONI-2
quotient, the three reading achievement standard scores (Word Attack, Word
Identification, and Passage Comprehension), and AAT raw score were entered. Table 21
reported Pearson correlations between the LAC and each of these variables for the groups
combined and for each group individually. Both groups reading achievement scores
were correlated with their LAC scores. However, because the inter-correlations between

71
the three achievement tests were high, only the most significant reading achievement
score was selected to predict LAC performance in the regression analysis. For the CTRL
group, Word Attack and age were selected as variables of predictive value. Word Attack
alone contributed 27% (adjusted R:) of the variance to the LAC score (F = 7.51,/? = .01).
Word Attack and Age combined contributed 44% (adjusted R") of the variance (F = 7.93,
p = .00). For the PI group, Passage Comprehension and Rapid Object Naming were
selected as variables of predictive value by the stepwise regression analysis.
Surprisingly, the unique variance contributed by the BNT score was not significant once
Passage Comprehension was entered. Instead, the variance contributed by Rapid Object
Naming was deemed significant. The reverse correlation between Rapid Object Naming
Table 21. Pearson correlations between the LAC score and variables entered into
stepwise regression analysis for PI and CTRL groups.
Groups Combined PI CTRL
Age
.39’
.29
.48’
BNT
.34*
.43*
.07
Rapid Color Naming
.18
.07
.16
Rapid Object Naming
.04
-.11
.01
TONI-2
.01
.27
-.24
WRMT Word Attack
.61*
.50*
.55*
WRMT Word Identification
.59*
.49*
.44*
WRMT Passage Comprehension
.58*
.55*
.39*
AAT
.30*
.18
.40*
Note: PI - Phonologically Impaired; CTRL = Controls;*p <= .05.

72
and the LAC indicated that the better PI subjects performed on the LAC, the slower they
completed the Rapid Object Naming. Passage Comprehension alone contributed 27%
(adjusted R2) of variance to the LAC score (F = 8.31,/? = .01). Passage Comprehension
•y
and Rapid Object Naming together contributed 42% (adjusted R ) of variance (F = 8.33,
P = 00).
As a check of LAC's validity as a measure of phonological awareness, the
correlation between the LAC raw score and Phoneme Match Test performance was
calculated. With all subjects combined, the correlation between the LAC score and the
Phoneme Match performance was statistically significant (RT, Pearson's r = -.37,p = .02;
accuracy, Pearson's r = .48,/? = .00). Dividing subjects into groups revealed that the LAC
score and Phoneme Match performance was correlated for CTRLs (RT, Pearson's r = -
.48,/? = .03; accuracy, Pearson's r = .69, p = .00) but not for the PI group (RT, Pearson's r
= .18,/? = .44; accuracy, Pearson's r = .29,/? = .20).
Developmental Phonological Dvslexics vs.
Adequate Readers with Poor Phonology vs. Controls
The PI group can be categorized into two distinct subgroups. As shown in Table
1, the DPD group was characterized by impaired phonological processing, single-word
reading, and comprehension (WRMT Word Attack, Word Identification, and Passage
Comprehension respectively) in comparison to their expected achievement level based on
their intellectual aptitude. The ARPP group, while demonstrating impaired phonological
skills, actually has single-word reading and comprehension skills commensurate to their
expected achievement level. This subtype of children with impaired phonological

73
processing but adequate reading ability has been described (Masutto & Comoldi, 1992),
but little is known about them, such as whether these children represent a distinct subtype
of dyslexia or a milder form of the disorder. To explore if the DPD and ARPP groups
differed in their presentation on cognitive measures, differences between these groups
were examined in this section. The CTRL group was also included in order to compare
these two phonologically impaired groups with normal readers.
Articulatory Awareness Test
Articulatory Awareness Test scores obtained by DPD, ARPP, and CTRL groups
were provided in Table 22. An ANOVA conducted with Group as the between-subject
variable revealed that the three groups did not differ from each other on their AAT score
(F = 0.41,/? = 0.66).
Table 22. Means and standard deviations of AAT scores obtained by DPD, ARPP, and
CTRL groups.
DPD ARPP
CTRL
AAT 6.0(2.72) 6.4(1.78)
6.7(1.75)
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.
To examine if DPD and ARPP groups demonstrate different patterns of
relationship between articulatory knowledge and name retrieval, Pearson correlations
between AAT and reaction time during experimental conditions (i.e., NAPM and Visual
Match interference conditions) were conducted for each group and reported in Table 23.
Although the DPD group’s correlations between their AAT score and naming latency
appeared more similar to the CTRL group’s and different from the ARPP group’s, there

74
was no statistically significant difference between DPD and ARPP group’s correlations
on the NAPM interference conditions (Mouth Interference, Z(n.io)= -.83,p> .05; Foot
Interference, Z(n,io)= -.70 ,p> .05) or on the Visual Match interference conditions
(Mouth Interference, Z(n,io> = -1.51,/?> .05; Foot Interference, Z(n,io> = --52,p> .05).
Unlike the control group, whose correlation between naming latency and AAT score
differed significantly from its corresponding correlation on the Visual Match Test during
Mouth Interference (i.e., -.48 vs. .19, Z^o,20) ~ -2.07,p< .05), no such corresponding
correlation pairs within the DPD and ARPP groups were statistically different (DPD:
NAPM Mouth vs. Visual Match Mouth,Zpui> = -.06,p> .05; NAPM Foot vs. Visual
Match Foot, Z(n n) = -.60,p> .05; ARPP: NAPM Mouth vs. Visual Match Mouth,
Z(io,io) = -.71,p > .05; NAPM Foot vs. Visual Match Foot, Z(io,io>= -.39,p > .05).
Table 23. Pearson correlations between the AAT score and reaction time on
DPD
ARPP
CTRL
NAPM
Mouth Interference
-.32
.10
l
oo
*
Foot Interference
-.29
.06
-.35
Visual Match
Mouth Interference
-.29
.45
.19
Foot Interference
i
©
o
.26
.27
Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; * p < .05.
â– s

Descriptive Measures
75
Table 24 showed each group’s performance on descriptive measures. One-way
analyses of variance indicated a group difference on the BNT (F = 4.66, p = .02) but not
on any of the other naming measures (Rapid Color Naming, F = 2.89,p = .07; Rapid
Object Naming, F = 1.54,/?= .23; Naming Test, F = 2.30,/? = .11). On the BNT,
independent samples t-test showed that the DPD group scored lower than the CTRL
group (t = 2.74, p = .01). The ARPP group’s BNT score did not differ from either of the
other groups (ARPP vs. CTRL, t = 0.60,/? = .55; ARPP vs. DPD, t = 1.85,/? = .08). One¬
way analysis of variance on the LAC scores also indicated difference between groups (F
= 7.71,/? = .00). Independent samples t-test showed that the DPD group’s phonological
awareness score was lower than both other groups (DPD vs. CTRL, t = 3.76,/? = .00;
DPD vs. ARPP, t = 2.84,/? = .01), while the ARPP group’s LAC score did not differ from
that of the CTRL group’s (t = 1.23,/? = .23).
Table 24. Means and standard deviations on descriptive measures for the DPD, ARPP,
and CTRL groups.
DPD
ARPP
CTRL
BNTa
-2.71 (2.52/
-1.13(1.02/*
-0.85 (1.30/
Rapid Color Naming3
-1.02(1.85)
-0.08(1.07)
0.02 (0.68)
Rapid Object Naming3
-1.25 (2.80)
-0.49(1.81)
-0.05 (0.80)
Naming Testb
85 (7)
89 (5)
90 (6)
LACc
46(13/
65 (16)g
75 (23/
Poor Phonology; CTRL = Controls. a Z-scores. b Percentage correct. c Raw score.
Numbers in each row with different superscripts were significantly different from each
other.

76
Experimental Measures
Phoneme match. A MANOVA was conducted to see if groups differed in their
reaction time on matching the end phoneme of words. Group (DPD, ARPP, and CTRL)
was entered as the between-subject factor and Stimulus Set (A and B) was entered as the
within-subject factor. The Group X Stimulus Set interaction was only marginally
significant (F = 2.85,p = .07), but there was a significant Stimulus Set effect (F = 6.09,/?
= .02). The Stimulus Set effect was the same as that reported under the PI vs. CTRL
section, where it was shown that subjects were faster in responding to Set A than to Set
B. Each group's response time to the Sets A and B were presented in Table 25. A
similar analysis was conducted with response accuracy data. Only a marginal effect of
group was found (F = 2.89,/? = .07). As there were no statistically significant Group
effects on reaction time and response accuracy, it was assumed that the measures taken to
counterbalance Stimulus Set with interference conditions dispersed differences between
stimulus sets among different interference conditions, and no further attempt to examine
Stimulus Set’s interaction with other variables was taken.
Table 25. Reaction time and accuracy on the Phoneme Match Test for DPD, ARPP, and
CTRL eroups.
DPD
ARPP
CTRL
RI
% Correct
RI
% Correct
El
% Correct
Set A 2958 (633)
87(16)
2834 (540)
93 (6)
2758 (445)
95 (5)
Set B 3285 (1136)
87(14)
3156 (793)
92(6)
2711(543)
94(7)
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.

77
NAPM and visual match. Descriptive statistics for the experimental measures,
NAPM and Visual Match, were presented in Table 26. Separate MANOVAs were
conducted for reaction time and response accuracy data, with Group as a between-subject
factor (DPD vs. ARPP vs. CTRL) and Task (NAPM and Visual Match), Interference
(Mouth and Foot) and Block (1 and 2) as within-subject factors. Because this analysis
was exactly the same as the analyses performed in the section on PI vs. CTRL, only
effects or interactions involving Group (DPD vs. ARPP vs. CTRL) were reported here to
Table 26. Reaction time and response accuracy on the NAPM and Visual Match Tests
for DPD, ARPP, and CTRL groups.
DPD
RT
% Correct
ARPP
RT
% Correct
CTRL
RT
% Correct
NAPM
Mouth
4511
77
4179
84
3688
88
(1666)
(17)
(1472)
(12)
(1551)
(10)
Foot
5117
82
4013
85
3653
89
(1510)
(10)
(1087)
(5)
(1135)
(10)
Visual Match
Mouth
4936
76
4957
79
5333
86
(1902)
(17)
(1756)
(7)
(1694)
(13)
Foot
5243
84
4742
83
4984
84
(1528)
(11)
(1100)
(7)
(1719)
(13)
Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.

78
reduce redundancy. The MANOVA with reaction time data revealed a Group X Task
interaction (F = 3.76, p = .03). Pairwise comparisons using dependent samples t-test to
follow up on the Group X Task interaction revealed that both ARPP and CTRL groups
were faster in responding the to NAPM in comparison to the Visual Match (ARPP, t = -
2.33,p = .04; CTRL, t = -4.58,p = .00), but the DPD group’s reaction time on these two
tasks did not differ (t = -0.83,p = .42).
The MANOVA on accuracy data did not reveal any Group main effects or
interactions. Other interactions and effects were exactly the same as those reported in the
PI vs. CTRL section and were not repeated here.
ADHD subjects. To see whether ADHD differentially affected the performance
of the two phonologically impaired groups, reaction time and accuracy data of ADHD
subjects were contrasted to those of non-ADHD subjects in Table 27. Dividing the DPD
and ARPP groups into ADHD vs. Non-ADHD subgroups dramatically reduced the
number of subjects in each subgroup, rendering multivariate analyses looking at
differences between groups unrealistic due to low power. Nevertheless, perusing Table
27 suggested that ADHD subjects tended to be less accurate than their non-ADHD
counterparts.
Interfering movements. To examine if the two phonologically impaired groups
engaged in mouth and foot interfering movements with equal facility, a MANOVA was
conducted with the interfering movement frequency data. The CTRL group was included
in this analysis as a comparison group. Group (DPD vs. ARPP vs. CTRL) was entered as
a between-subject factor and Task (NAPM and Visual Match), Interference (Mouth and
Foot), and Block (1 and 2) were entered as within-subject factors. Table 28 showed each

79
Table 27. Reaction time and accuracy of the phonologically impaired Non-ADHD and
ADHD subgroups.
Non-
ADHD
(n = 5)
DPD
ADHD
(n-6)
Non-
ADHD
(n = 7)
ARPP
ADHD
(n = 3)
RT
%
RT
%
RT
%
RT
%
Correct
Correct
Correct
Correct
NAPM
4581
80
4454
75
3772
87
5130
78
Mouth
(356)
(13)
(2333)
(21)
(1468)
(11)
(1164)
(14)
Foot
5551
87
4756
77
3716
86
4705
84
(1951)
(9)
(1081)
(9)
(1036)
(6)
(1035)
(1)
Visual
Match
5145
83
4761
69
4899
81
5092
75
Mouth
(1285)
(8)
(2415)
(21)
(1782)
(8)
(2076)
(3)
Foot
5290
84
5204
84
4667
81
4917
88
(1212)
(8)
(1868)
(14)
(1258)
(8)
(797)
(7)
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology.
group’s interference movement frequency index. The result of this analysis was similar
to the analysis with the two phonologically impaired groups combined, with the
exception that the four-way interaction (Group X Task X Interference X Block) was now
“S

80
only marginally significant (F = 2.61, p = .09). Because breaking the PI group down into
the DPD and ARPP groups reduced the number of subjects in these subgroups, the power
to detect a four-way interaction in the present analysis if one existed was reduced.
Interestingly, an examination of Table 28 revealed that ARPP subjects appeared to
produce interference movements with greater facility than DPD subjects. The exception
Table 28. Interfering movement frequency index for the DPD, ARPP, and CTRL groups.
DPD
ARPP
CTRL
NAPM
Mouth
.83 (.25)
.87 (.25)
.97 (.32)
Block 1
.83 (.27)
.85 (.23)
.98 (.35)
Block 2
.83 (.25)
.89 (.28)
.95 (.31)
Foot
.90 (.19)
1.11 (.27)
1.01 (.26)
Block 1
.88 (.20)
1.08 (.27)
.97 (.26)
Block 2
.91 (.17)
1.15 (.30)
1.05 (.27)
Visual Match
Mouth
.94 (.20)
1.04 (.24)
.99 (.23)
Block 1
.93 (.18)
1.05 (.29)
.98 (.25)
Block 2
.95 (.23)
1.04 (.20)
1.01 (.24)
Foot
.96 (.17)
1.11 (.31)
1.04 (.30)
Block 1
.95 (.18)
1.07 (.33)
1.05 (.32)
Block 2
.97 (.18)
1.16 (.30)
1.03 (.30)
Note. DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls.
*>

81
to this was during NAPM Mouth Interference, where both DPD and ARPP subjects
produced interfering movements with approximately equivalent facility, especially during
Block 1. Compared to the CTRL group, the ARPP subjects tended to produce interfering
movements with greater facility, except during the NAPM Mouth Interference condition,
whereas the DPD subjects tended to be slower in producing interfering movements than
the CTRL group. Comparing Mouth Interference to Foot Interference, the ARPP group
appeared to have the greatest discrepancy between these interference conditions during
NAPM, while neither of the other groups showed such discrepancy on either NAPM or
Visual Match.
To examine how interference movement frequency related to each group's
articulatory knowledge, Pearson correlations between the AAT score and interfering
movement frequency index for each group were calculated and reported in Table 29. The
positive correlation between the AAT score and the NAPM Mouth Interference
movement index was driven by the DPD group. The better the DPD group's articulatory
knowledge, the faster they were able to produce interfering mouth movements during a
name retrieval task. The negative correlation between the AAT score and the Visual
Match Foot Interference movement index was driven by the ARPP group. The better the
ARPP group's articulatory knowledge, the more slowly they produced foot interference
movements during a nonverbal visual task.
Predictors of Articulatory Knowledge
To explore if variables that predicted the AAT score differed for the DPD and
ARPP groups, a stepwise regression analysis was conducted with age, BNT Z-score,

82
Table 29. Pearson correlations between the AAT score and interfering movement
DPD
ARPP
CTRL
NAPM
Mouth Interference
.74*
.06
-.14
Foot Interference
.28
-.52
-.24
Visual Match
Mouth Interference
.16
-.24
-.14
Foot Interference
-.47
-.64*
-.13
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls. * p < .05.
Rapid Color Naming Z-score, Rapid Object Naming Z-score, LAC raw score, TONI-2
Quotient, and the three reading achievement standard scores (Word Attack, Word
Identification, Passage Comprehension) entered as independent factors. Pearson
correlations between the AAT score and each of these variables for DPD and ARPP
groups were presented in Table 30. The CTRL group's correlation between AAT and
each independent variable were also provided for comparison. For the DPD group, no
variable was correlated with the AAT. Consequently no variable was selected by the
stepwise regression as a predictor of the DPD group's AAT score. For the ARPP group,
the three reading achievement measures, which were highly correlated with each other,
were positively correlated with AAT score. A perusal of scatter plots of reading
achievement scores as a function of AAT scores indicated that these significant
correlations were valid and not due to the presence of extreme scores. The Passage
Comprehension standard score was selected by the stepwise regression analysis and

83
accounted for 52% (adjusted R2) of the variance to the ARPP group's AAT score (F =
10.59, p = .01). The correlation between the ARPP group's AAT score and reading
achievement measures indicated that the better their articulatory knowledge, the higher
reading attainment ARPP subjects were able to achieve.
Table 30. Pearson correlations between the AAT score and variables entered into
stepwise regression analysis for DPP and ARPP groups.
DPD
ARPP
CTRL
Age
-.13
-.32
.23
BNT
-.24
-.13
.35
Rapid Color Naming
-.22
.45
.31
Rapid Object Naming
-.28
.29
.28
LAC
.20
.12
4^
O
#
TONI-2
-.47
.25
-.23
WRMT Word Attack
.02
.67*
-.00
WRMT Word Identification
-.04
.66*
-.04
WRMT Passage Comprehension
-.03
.76*
-.18
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; * p <= .05.
Other than the variables examined in Table 30, Pearson correlations between the
AAT score and Phoneme Match performance were also examined. Both DPD and ARPP
groups' AAT scores were significantly correlated with their accuracy on the Phoneme
Match Test (DPD: r = .77, p = .01; ARPP: r = .16, p = .01). The better their articulatory
knowledge, the more accurate they were in deciding if phonemes matched.

Predictors of Phonological Awareness
84
Predictors of the LAC score for DPD and ARPP groups were also explored with
stepwise regression analysis. Age, BNT Z-score, Rapid Color Naming Z-score, Rapid
Object Naming Z-score, TONI-2 quotient, the three reading achievement standard scores
(Word Attack, Word Identification, Passage Comprehension), and AAT raw score were
entered as independent factors. Table 31 showed Pearson correlations between the LAC
score and each of these variables for DPD and ARPP groups. The CTRL group’s
correlations were also listed for comparison. When the phonologically impaired group
was divided into DPD and ARPP groups, power to detect correlations was decreased such
that the previously significant correlations between the LAC score and reading
achievement measures were no longer significant for either the DPD or ARPP group.
The negative correlation between the LAC and Rapid Color Naming was significant for
the ARPP group only. The better their phonological awareness, the more slowly ARPP
subjects were able to complete rapid naming, especially of colors. No variables were
selected as variables of predictive value by stepwise regression analysis for either DPD or
ARPP groups.
Correlation between the LAC raw score and response time on the Phoneme Match
Test was calculated for the two phonologically impaired groups. Neither the DPD nor
ARPP group's LAC score was correlated with their performance on the Phoneme Match
Test (DPD: RT, Pearson's r = -.24,/? = .49; accuracy, Pearson's r = .28,/? = .41; ARPP:
RT, Pearson's r = -.08,/? = .84; accuracy, Pearson's r = .08,/? = .84).

85
Table 31. Pearson correlations between the LAC score and variables entered into
stepwise regression analysis for DPP and ARPP groups.
DPD
ARPP
CTRL
Age
-.07
.44
.48*
BNT
.24
.50
.07
Rapid Color Naming
.18
-.61*
.16
Rapid Object Naming
-.08
-.50
.01
TONI-2
.14
.09
-.24
WRMT Word Attack
.42
.18
.55*
WRMT Word Identification
.26
.01
.44*
WRMT Passage Comprehension
.24
.24
.39*
AAT
.20
.12
.40*
Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with
Poor Phonology; CTRL = Controls; * p <= .05.
Poor vs. Adequate Articulatory Knowledge
The articulatory feedback hypothesis of naming hypothesized that articulatory
feedback facilitates name retrieval. This hypothesis makes the assumption of the
presence of articulatory knowledge. There was theoretical interest in examining the
name retrieval process of those subjects with adequate articulatory knowledge and those
with inadequate articulatory knowledge. Presumably, the hypothesis may hold true for
those with adequate articulatory knowledge but not for those with poor articulatory
knowledge. To group subjects into poor vs. adequate articulatory knowledge groups, the
mean AAT score for the entire population of subjects was calculated, and one standard
deviation below the mean, which corresponded to a Z-score of-1, was selected as the

86
cutoff score for grouping criterion. The mean AAT score of all 41 subjects was 6.44 with
a standard deviation of 2.03. Thus subjects with an AAT score of 4 or below were
grouped into the Poor Articulatory Knowledge group (PAK), and those with an AAT
score of 5 or above were grouped into the Adequate Articulatory Knowledge group
(AAK). Table 32 reported some descriptive statistics about each subject group.
Independent samples t-test indicated that the two groups did not differ in age (t = -0.16, p
= .88) or intellectual aptitude as estimated by the TONI-2 (t = -1.46,/? = .15). All of the
subjects in the PAK group were males, and the majority of this group was composed of
children with ADHD. Note there was unequal distribution of the number of subjects in
each group, with only seven subjects falling into the PAK group.
Table 32. Demographics of the Poor Articulatory Knowledge (PAK) and Adequate
Articulatory Knowledge (AAK) groups.
PAK AAK
(n - 7) (n = 34)
Age
9(2)
9(2)
TONI-2
111(7)
105(10)
M:F Ratio
7:0
20:14
ADHD
5
7
Descriptive Measures
The performance of PAK and AAK groups on descriptive measures was
examined for group differences. Table 33 summarized the groups’ performance on these
measures. There was no difference between groups on any of the naming measures when

87
group differences were tested using independent samples t-test (BNT, t = -0.18,/? = .86;
Rapid Color Naming, t = 0.84,/? = .41; Rapid Object Naming, t = 0.23,p = .82; Naming
Test, t = 1.15, p = .26). Independent samples t-test indicated that the PAK group scored
more poorly on the LAC than the AAK group (t = 2.43,/? = .02), not surprisingly as LAC
score was significantly correlated with AAT score (Table 20). Comparison of mean
standard scores between groups using independent samples t-test revealed that the PAK
group scored lower on Word Attack (t = 2.76, p = .01) and Word Identification (t = 2.27,
p = .03) compared to AAK, but not on Passage Comprehension (t = 1.77,/? = .08).
Table 33. Means and standard deviations on descriptive measures for the PAK and AAK
groups.
PAK
AAK
BNTa
-1.31 (1.83)
-1.44(1.83)
Rapid Color Naming3
-.65 (.72)
-.21 (1.33)
Rapid Object Naming3
-.63 (.87)
-.46(1.97)
Naming Testb
86(5)
89 (6)
LACc
47 (22)a
68 (21)b
WRMT Word Attackd
74 (12)a
92 (17)b
WRMT Word Identification11
77 (13)a
94 (19)b
WRMT Passage Comprehensiond
83(14)
95(17)
Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.
a Z-scores. b Percentage correct. 0 Raw score. d Age-corrected standard scores. Within
each row, numbers with different superscripts were significantly different from each
other.

Experimental Measures
88
Phoneme match. Performance on the Phoneme Match Test was examined to see
if PAK and AAK groups differed in their ability to match phonemes. Separate ANOVAs
were conducted for reaction time and response accuracy with Group (PAK vs. AAK) as
the between-subject variable. Table 34 showed each group's performance on the
Phoneme Match Test. The PAK group was both slower in reaction time (F = 5.45, p =
.02) and less accurate (F = 66.91, p = .00) than the AAK group. The overall AAT score
and Phoneme Match reaction time was not correlated (i.e., all subjects combined;
Pearson's r = -.26, /? = .10). However, the AAT score did correlate positively with
Phoneme Match accuracy (Pearson's r = .64, p = .00). Because the PAK group’s ability
to match phonemes was remarkably worse, their reaction time and accuracy on the
Phoneme Match Task were used as covariates in analyses involving NAPM because the
NAPM required phoneme match as an integral part of the task.
Table 34. Reaction time and accuracy on the Phoneme Match Test for the PAK and
PAK
AAK
Reaction time
3385 (1018)
2802 (490)
% Correct
76(11)
95 (4)
Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.
NAPM and visual match. Subjects’ reaction time and accuracy on the NAPM
were reported in Table 35, and their Visual Match performance were reported in Table
36. Separate analyses were conducted with NAPM and Visual Match because Phoneme
Match performance was used as a covariate in analyzing NAPM data while the use of this

89
covariate would be inappropriate for Visual Match because phoneme match was not
required as part of the Visual Match Test. Table 35 represented subjects' scores without
the covariate extracted.
Table 35. Reaction time and accuracy on the NAPM for PAK and AAK groups.
PAK
AAK
RT
% Correct
RT
% Correct
Mouth
Block 1
5795(2460)
70(17)
3836(1541)
86(12)
Block 2
5142 (2113)
79(18)
3659(1151)
87(14)
Foot
Block 1
5838(1671)
82(12)
3881 (1151)
87(10)
Block 2
4904(1687)
85(10)
3888(1275)
85(12)
Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.
On the NAPM (Table 35), separate MANCOVAs were conducted for reaction
time and response accuracy data. Group (PAK vs. AAK) was the between-subject factor,
Interference (Mouth and Foot) and Block (1 and 2) were within-subject factors, and
Phoneme Test reaction time and response accuracy were covariates in MANCOVAs
analyzing reaction time and response accuracy, respectively. The reaction time
MANCOVA revealed a significant Group X Interference X Block interaction (F = 4.33,/?
= .04), an Interference X Block X Covariate interaction (F = 6.65, p = .01), an
Interference X Block interaction (F = 6.51, p = .01), and a Group X Block interaction (F
= 4.43,/? = .04). The Group X Interference X Block interaction was explored by

90
separate MANOVAs for each group with Interference and Block as within-subject
variables. These analyses indicated that the PAK group's reaction time improved in
Block 2 (F = 17.02,/?= .01) while the AAK group’s did not (F = 0.48,/? = .49).
Dependent samples t-tests to compare the PAK group's Block 1 and Block 2 reaction
time for each interference condition revealed that the improvement in Block 2’s reaction
time was more salient during the Foot Interference condition (t = 3.85, p = .01) than
during the Mouth Interference condition (t = 2.14, p = .08).
The Interference X Block X Covariate interaction was explored by conducting a
MANOVA with Interference as the within-subject variable and Phoneme Match response
time as the covariate for each Block. These analyses revealed an Interference X
Covariate interaction for Block 1 (F = 6.07,/? = .02; Figure 2) that did not exist for Block
2 (F = 1.60,/? = .21; Figure 3). In Block 1 (Figure 2), the slower the subjects’ response
time on the Phoneme Match Test (i.e., poorer the phoneme match ability), the more
slowly they responded during the Mouth Interference condition compared to the Foot
Interference condition. This effect went away by Block 2 (Figure 3). The MANCOVA
on the NAPM accuracy data revealed no statistically significant interactions or effects,
which rendered the reaction time data free from speed-accuracy trade off.
Separate reaction time and response accuracy analyses were conducted for Visual
Match (Table 36). Again, Group (PAK vs. AAK) was the between-subject factor and
Interference (Mouth and Foot) and Block (1 and 2) were within-subject factors. The
MANOVA on reaction time data revealed only a Block effect (F = 5.81,/? = .02).
Subjects were faster during Block 1 than Block 2. The MANOVA on accuracy data
revealed a significant Interference X Block interaction (F = 7.38,/? = .01) and

91
f—
02
Q-
<
z
9500
8500
7500 -
6500
/♦
5500
4500
4-;
3500 -
♦r >
2500 -
1500
' * ♦
♦
T
1500 2500 3500 4500 5500
Phoneme Match RT
♦ Mouth Interference
Foot Interference
Figure 2. Block 1 NAPM reaction time, plotted against the ability to match end phonemes

92
9500 -
8500
♦
7500 -
“ 6500
2 5500
Z 4500 -i
♦
♦ Mouth Interference
Foot Interference
3500 -
2500
1500 - t t r-
1500 2500 3500 4500 5500
Phoneme Match RT
Figure 3. Block 2 NAPM reaction time, plotted against the ability to match end phonemes

93
Interference (F = 6.57,p = .01) and Block main effects (F = 5.95,p = .02). Follow up
pairwise comparisons using dependent samples t-test showed that subjects became less
accurate during Block 2 on Mouth Interference (t = 3.27, p = .00), but there was no
accuracy difference between Block 1 and Block 2 during Foot Interference (t = \A2,p =
.16).
Table 36. Reaction time and accuracy on the Visual Match Test for PAK and AAK
groups.
PAK
AAK
RT
% Correct
RT
% Correct
Mouth
Block 1
5065 (1887)
82 (7)
4809(1488)
85(15)
Block 2
5088(1927)
70(18)
5455 (2022)
78(18)
Foot
Block 1
4640(1637)
84 (14)
4766(1407)
86(12)
Block 2
5017(1735)
86(10)
5305 (1736)
83(12)
Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.
Interference movements. The frequency of interference movements produced
during Mouth and Foot Interference conditions were analyzed to see if PAK and AAK
groups produced these movements with equal facility. A MANOVA with Group (PAK
vs. AAK) as the between-subject factor and Task (NAPM and Visual Match),
Interference (Mouth and Foot), and Block (1 and 2) as within-subject factors was
conducted. This revealed the following significant interactions and main effects: Group
X Task (F = 7.88,/? = .01), Group X Interference (F = 5.82,p = .02), Task X Interference

94
(F = 5.57,/?= .02), Task main effect (F = 9.57, p = .00), Interference main effect (F =
10.91,/? = .00), and Block main effect (F = 5.16,/? = .03). Subjects produced interfering
movements at a greater frequency during Block 2 than Block 1. Because Block did not
interact with any other variables, mean movement frequencies reported in Table 37 were
collapsed across the two blocks. Dependent samples t-tests to examine the nature of the
Group X Task interaction indicated that the PAK group produced movements at a slower
rate during NAPM than during Visual Match (t = -4.50,/? = .00). The AAK group did
not demonstrate a difference in the frequency of movements produced across these two
tasks (t = -.33,/? = .74). Dependent samples t-tests to examine the Group X Interference
interaction revealed that the PAK group tended to be slower in producing interfering
movements during Mouth Interference than during Foot Interference (t = 2.06,/? = .09).
The AAK group produced interfering movements with equal facility across the two
interference conditions (t = 1.24,/? = .22). Dependent samples t-tests to examine the
Task X Interference interaction revealed a difference between Mouth and Foot
Interference on the NAPM (t = 2.39,/? = .02) but not on Visual Match (t = 1.21,/? = .23).
Interfering foot movements were produced with greater facility than interfering mouth
movements during the NAPM task. In combination, these three interactions reflected the
trend that the PAK subjects tended to produce interfering movements most slowly during
the Mouth Interference condition of the NAPM. This would be represented by a three-
way interaction between Group, Task, and Interference, which was marginally significant
in the overall MANOVA analyzing interfering movement frequency (F = 3.58,/? = .07).
The power to detect this three-way interaction was limited (power = .46). The effect size
of this Group X Task X Interference interaction was calculated using the formula in

95
Figure 4, which yielded, {[(.68 - 1.02) - (.95 - 1.00)] - [(1.06 - 1.18) - (.98 - 1.01 )]}/.26
= -.77.
Table 37, Interfering movement frequency index for the PAK and AAK groups.
PAK
AAK
NAPM
Mouth
.68 (.14)
.95 (.29)
Foot
1.02 (.34)
1.00 (.23)
Visual Match
Mouth
1.06 (.21)
.98 (.23)
Foot
1.18 (.38)
1.01 (.24)
Note: PAK = Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge.

ES = {[NAPM, PAK(Mouth - Foot) - NAPM, AAK(Mouth - Foot)] -
[Visual Match, PAK(Mouth - Foot) - Visual Match, AAK(Mouth - Foot)]
/Pooled standard deviation,
Figure 4. Formula for calculating the effect size reflecting the Group X Task X
Interference interaction.

DISCUSSION
Review of Hypothesis
To examine the relationship between articulatory knowledge and name retrieval,
phonologically impaired readers were chosen as experimental subjects because of the
literature documenting their naming deficits and impaired articulatory knowledge. The
literature on the naming ability of dyslexic individuals indicated that in addition to their
problems with reading comprehension, they also have problems with name retrieval
(Swan & Goswami, 1997; Fawcett & Nicolson, 1994). Their retrieval deficit was not due
to problems of intelligence or vocabulary. When given multiple choices, dyslexics as a
group was better at correctly recognizing a target item although they failed at naming that
item (Wolf & Obregon, 1992). This pattern of performance contrasted with that of
garden-variety poor readers, who usually have lower vocabulary scores and do not
perform as well as dyslexics in recognizing target items from a choice of options. Swan
and Goswami (1997) captured this pattern of naming performance well by describing
dyslexic children's poor naming as due to retrieval problems and garden-variety poor
readers' poor naming as due to limited vocabulary. Name retrieval difficulties have been
shown to be evident in dyslexics' narrative speech as well as on formal
neuropsychological measures (Murphy et al., 1988). Dyslexic children's naming
difficulties persisted into adulthood (Korhonen, 1995; Fawcell & Nicolson, 1994; Felton
97

98
et al., 1990). That is, their naming difficulties were deficits that did not go away with
maturation.
In an intriguing study, Montgomery (1981) found that dyslexic children tended to
have worse knowledge of their articulators' positions than normal readers. This was
important because it raised the question of how articulatory knowledge and language
functioning are related. Some treatment programs for reading disability included
protocols to train readers to be more aware and sensitive to the position of their
articulators while making speech sounds. The success of such programs suggested that
articulatory knowledge and reading are related (Alexander, Andersen, Heilman, Voeller,
& Torgesen, 1991; Oakland, Black, Stanford, Nussbaum, & Balise, 1998). Among
normal readers, there may be a relationship between articulatory awareness, or
spontaneous use of articulatory knowledge during linguistic activities, and reading. The
existence of such a relationship has support from neuroanatomical studies showing that
dyslexic individuals have a higher rate of abnormality in the regions surrounding or
involving the Heschl's gyrus and planum temporale (Hund et al., 1990; Larsen et al.,
1990; Leonard et al., 1993). These are posterior language regions important for the
perception of speech and phonological representations of words. Metabolic studies also
pointed to abnormal activation of anterior and posterior language regions among dyslexic
individuals while they performed language tasks (Paulesu et al., 1996). These evidences
led to questions about the nature of the relationship between articulatory knowledge and
name retrieval.
The articulatory feedback hypothesis of naming stated that having articulatory
feedback facilitates the name retrieval process. Without articulatory feedback, name

99
retrieval may be less efficiently achieved. To test this hypothesis, this study utilized an
interference paradigm during a name retrieval task. An interference task was introduced
(i.e., Mouth Interference) such that articulatory feedback appropriate to the name retrieval
task cannot be generated. Because subjects' mouths were engaged in an interference task,
naming had to be assessed through phoneme match (NAPM). Naming reaction time and
accuracy of the control subjects were contrasted with naming performance of those with
poor articulatory knowledge. If there is support for the articulatory feedback hypothesis
of naming, taking away appropriate articulatory feedback by introducing an interference
task should negatively affect the control group’s performance but not the performance of
groups with impaired articulatory knowledge. A condition (Foot Interference) was
introduced to control for general interference effects of engaging in multiple tasks at
once. A non-verbal task (Visual Match) was included to control for a potential difference
between the attentional demand of the two interfering conditions (Mouth and Foot
Interference).
In this discussion of the results, the general relationship between articulatory
knowledge and name retrieval was discussed first. Then that relationship was further
elucidated by examining group differences. Three different grouping methods were
employed to examine differences in the pattern of responses on experimental measures
(i.e., NAPM and Visual Match). The performance of the Phonologically Impaired (PI)
group was first contrasted against the performance of normal reader control subjects
(CTRL). Then the Phonologically Impaired group was divided into two subgroups,
Developmental Phonological Dyslexics (DPD) and Adequate Readers with Poor
Phonology (ARPP). Finally, level of articulatory knowledge was examined more directly

100
by dividing groups into Poor Articulatory Knowledge (PAK) and Adequate Articulatory
Knowledge (AAK) groups based on their performance on the A AT. As secondary
explorations, the influence of subjects with ADHD and the relationship between
articulatory knowledge and phonological awareness were also addressed. Finally, this
discussion terminated with a modification of the articulatory feedback hypothesis of
naming.
Correlation Between Articulatory Knowledge and Name Retrieval
The articulatory feedback hypothesis of naming predicted that Mouth Interference
would interfere with appropriate feedback for naming, and therefore the better knowledge
one had of one's articulator positions, the more slowly one would perform on our naming
task. This would manifest in a non-significant or a positive correlation between
articulatory knowledge and response latency. In contrast, a negative correlation was
found indicating faster naming latency with better articulatory knowledge despite
interference with appropriate feedback (Table 6). The hypothesis predicted that subjects
with better articulatory knowledge would use appropriate articulatory feedback during the
Foot Interference condition to facilitate name retrieval. This would manifest in a
negative correlation between articulatory knowledge and naming latency during Foot
Interference. Instead, a non-significant correlation was found indicating that subjects did
not spontaneously use articulatory feedback to assist retrieval.
Disregarding the direction of correlations for a moment to consider the correlation
that was statistically significant, findings indicated that a significant correlation between
articulatory knowledge and naming latency but not between articulatory knowledge and

101
visual match suggested a relationship between articulatory knowledge and name retrieval
(Table 6). Dividing subjects into phonologically impaired and control groups revealed
that this pattern was driven by the control group (Table 8). For normal readers, their
level of articulatory knowledge was correlated with their naming performance but not
with their performance during a nonlinguistic control task. This indicated the existence
of a relationship between articulatory knowledge and name retrieval among normal
readers.
The direction of the correlation between articulatory knowledge and each of the
two interference conditions was unanticipated. Normal readers did not appear to use
articulatory feedback in a spontaneous fashion to facilitate name retrieval, as their
naming latency was not correlated with articulatory knowledge when articulators were
free to provide feedback (i.e., Foot Interference). But when their attention was drawn to
their articulators, better articulatory knowledge was accompanied by faster naming
latency (i.e., Mouth Interference). Their articulators were engaged in interfering mouth
movements at this time; therefore articulatory feedback was not complimentary to name
retrieval. Despite irrelevant articulatory feedback, naming latency decreased as
knowledge level of articulatory' position increased. The decreasing naming latency could
not have been due to specific articulatory feedback. It must have been due to some other
factor related to articulatory movement. The act of moving articulators required the
activation of the primary motor and premotor cortices controlling them. These areas are
also activated by the language system. One possibility is that activation of these cortical
areas resulted in the "spreading" of activation to connected language systems, in much
the same way as Collins and Loftus' (1975) spreading activation model explained

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semantic activation of related concepts from activation of one concept. Normal readers
with better articulatory knowledge may have more efficient connectivity of these
systems. Thus the better their articulatory knowledge, the more efficiently activation
could spread from motor cortices to the name retrieval system and engage that system.
The phonologically impaired subjects did not demonstrate any significant
correlation between articulatory knowledge and name retrieval. Regardless of how good
their articulatory knowledge when directly inquired about it (i.e., AAT score), they did
not use this information spontaneously to help them with name retrieval, as their naming
latency was not correlated with articulatory knowledge when articulators were free to
provide feedback (i.e., Foot Interference). When their articulators were not able to
provide feedback due to engagement in interfering movements, the phonologically
impaired subjects' articulatory knowledge was also not correlated with naming latency.
Dividing the phonologically impaired subjects into impaired (i.e., DPD) and non-
impaired (i.e., ARPP) readers showed that the impaired readers' pattern appeared to be
more similar to that of the normal readers. While the impaired readers' articulatory
knowledge appeared to be correlated with naming latency, non-impaired readers did not
show such a relationship (see Table 23). However, the impaired readers' correlation
between articulatory knowledge and naming latency was not statistically significant, and
the correlations of the two phonologically impaired groups were not statistically different
from one another. The lack of a significant difference between impaired and non-
impaired readers' correlations may have been due to the small number of subjects in each
group (DPD, n = 11; ARPP, n = 10).

Group Differences in Articulatory’ Knowledge
103
Montgomery's (1981) finding that dyslexic children have worse articulatory
knowledge compared to normal readers was not wholly supported by the present study.
While the difference between the phonologically impaired and control groups on the
AAT was in a direction consistent with Montgomery's findings (see Table 7), both groups
demonstrated significant variability in their articulatory knowledge levels as assessed by
AAT. The control group's score on the AAT ranged from 2 to 9 (maximum score was
10). The phonologically impaired group's score ranged from 1 to 9. Some normal
readers have poor articulatory knowledge. Some phonologically impaired readers have
good articulatory knowledge. Dividing the phonologically group into impaired and non-
impaired reader groups did not change results. Again, the pattern of mean scores
obtained by the three groups was consistent with Montgomery's findings, with the
impaired readers obtaining the worst score and the normal readers obtaining the best
score (Table 22). However, the variability demonstrated by all three groups precluded
the finding of statistical difference among groups on articulatory knowledge.
The data suggested that articulatory knowledge was neither necessary nor
sufficient for achieving reading skills. It is intriguing, however, that the impaired readers
obtained the worst score and the normal readers the best score, with the non-reading
impaired but phonologically impaired subjects falling in between. The impaired readers
have the worst reading achievement, followed by the adequate readers with poor
phonology, followed by the control subjects, who have age-appropriate reading
achievement scores. The congruence of this pattern with Montgomery’s (1981) finding
raised questions about methodological differences between these two studies and possible

104
factors contributing to the present finding of null significance. Montgomery's study
included 34 subjects in each of the dyslexic and normal reader groups. These data were
collected over two different time periods, and she altered the instruction given to her
second set of subjects by explicitly going over positions of each articulatory organ drawn
in her sagittal cartoons. This change in instruction reduced the difference between group
scores by increasing the scores obtained by the dyslexic subjects. The instruction to the
Articulatory Awareness Test used in the present study resembled Montgomery's second
set of instructions. Familiarizing subjects to the task by explicitly pointing out the
position of articulator depicted in drawings may have "taught" dyslexic subjects how to
do the task. The AAT is an experimental measure that has not undergone rigorous testing
and revision with large subject samples to validate it as an instrument capable of
detecting differences in articulatory knowledge between groups. It is possible that the
AAT, in its present form, is not sensitive enough to detect differences between groups
even if differences exist.
Group Differences on Naming Measures
On visual confrontation naming (BNT), phonologically impaired subjects tended
to perform worse than normal readers (Table 9), although this group difference was not
significant unless the phonologically impaired group was further divided into impaired
reading and non-impaired reading groups. The impaired readers' confrontation naming
was worse than that of the controls', but the non-impaired readers' confrontation naming
did not differ statistically from either of the other groups (Table 24). Wolf and Obregon
(1992) and Swan and Goswami (1997) both found that dyslexic subjects have worse

105
confrontation naming than normal readers. Both groups recruited dyslexic subjects who
have documented reading impairments. Among the present subject groups, although the
Adequate Reader with Poor Phonology group have impaired phonological skills, their
single-word reading and reading comprehension were actually commensurate with their
expected achievement scores given their estimated intellectual aptitude. Only the
Developmental Phonological Dyslexia group in the present study had impaired single¬
word reading and reading comprehension in comparison to their expected achievement
levels. Therefore, it was appropriate to compare the Developmental Phonological
Dyslexia group but inappropriate to compare the Adequate Reader with Poor Phonology’
group with the findings reported by Wolf and Obregon (1992) and Swan and Goswami
(1997). The present finding of impaired BNT score in the impaired readers (DPD) was
consistent with these researchers' findings of impaired confrontation naming among
dyslexics (Wolf & Obregon, 1992; Swan & Goswami, 1997).
On Rapid Color and Object Naming, the present study did not find slowed rapid
naming among the phonologically impaired groups compared to the controls (Tables 9
and 24). As discussed previously, it was appropriate to compare just the impaired reader
group (DPD) to dyslexic groups reported in the literature. Table 24 showed that although
group means were in the direction consistent with that reported in the literature (Denckla
& Rudel, 1976), the impaired reader group's score was not statistically different from the
controls' score. It was possible that small sample sizes in the present study limited the
power available to detect a difference between groups. This study only had sample sizes
of 11 and 20 in the dyslexic and control groups respectively. This contrasted with sample
sizes of 72 (dyslexic group) and 120 (control group) in Denckla and Rudel’s (1976) study.

106
Effect sizes reflecting the difference between dyslexic (i.e., DPD) and normal readers in
the present study were calculated. This yielded effect sizes of-.82 for Rapid Color
Naming and -.67 for Rapid Object Naming, which are considered large. Unfortunately,
Denckla and Rudel (1976) did not report means and standard deviations in their study;
thus the present effect sizes cannot be compared to that of their study. Given the large
effect sizes obtained, it is likely that with larger sample sizes, the present dyslexic
subjects' rapid naming score would differ statistically from those of the controls'.
However, despite the limited sample size, groups in this study were found to differ
statistically on a confrontation naming task. This suggested that confrontation naming
was more vulnerable to impairment than rapid naming in the present subject sample.
Groups did not differ from each other on the experimental Naming Test, which
used stimuli from the NAPM (Tables 9 and 24). Groups were not expected to differ on
this test, as this test was designed to compose of objects highly familiar to the present
subjects' age range. The lack of a group difference indicated that groups were equally
familiar with and knew the names of the objects used during the NAPM.
Reaction Time and Response Accuracy
PI vs. CTRL and DPD vs. ARPP vs. CTRL. Reaction time data showed that
subjects responded to the naming task faster than to the visual match task, and that on the
naming task, controls responded faster than phonologically impaired subjects. When the
phonologically impaired group was divided into subgroups, the non-impaired reader
group responded to the naming task faster than to the visual match task, similar to the
controls. Perusal of Tables 11 and 26 did not suggest speed-accuracy trade-off. The

107
phonologically impaired group was less accurate during Mouth Interference than during
Foot Interference, and this was driven by their performance on the Visual Match Test.
Together, data indicated that Visual Match was a more demanding task than our naming
task, NAPM, and the Visual Match Test required longer response time. Mouth
Interference was more demanding for phonologically impaired subjects than Foot
Interference. Relevant group findings indicated that impaired readers differed from the
non-impaired readers. While naming was easier for non-impaired readers and controls, it
was more difficult for impaired readers. The naming task required as much processing
time from the impaired readers as the Visual Match Test.
Block effects were unexpectedly significant and were mainly driven by
performance during the Visual Match Test. Subjects became both slower and less
accurate on Visual Match as time went on. They became especially inaccurate during the
Mouth Interference condition of the Visual Match. An obvious explanation was fatigue.
However, the lack of a Block effect with the naming task argued against this explanation.
Another more likely explanation was that Block effects were due to a stimulus artifact.
The Visual Match stimuli were taken from the Test of Visual-Perceptual Skills (non¬
motor )~Revised (TVPS-R; Gardner, 1996), which had stimuli in increasing order of
difficulty. In making the stimuli for the Visual Match Test, the approximate order of the
stimuli appearing in the TVPS-R was kept. The naming task stimuli, in contrast, were
designed to have approximately equal difficulty level. Word frequency, syllable length,
and age of acquisition of names were variables that may affect difficulty level. Stimulus
Sets A and B were balanced on these variables, and within each set, there was no
ascending order of difficulty as represented via these variables. The Task X Block

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interactions likely reflected an artifact of the Visual Match stimuli and were due to
increasing order of difficulty of items. This artifact, however, could not explain why
subjects became less accurate during the Mouth Interference condition in comparison to
the Foot Interference condition of the Visual Match Test as time went on. The two
stimulus sets of the Visual Match Test had equivalent difficulty level because the two sets
were composed of the same trials with just different combination of items within each
trial. Engaging in Mouth Interference during Block 2 resulted in less accurate responses
on this task. The lack of a similar accuracy decrease with Foot Interference in Block 2
suggested that Mouth Interference was harder or required more processing resources than
Foot Interference.
PAK vs. AAK. Grouping subjects by their AAT performance was done as a post
hoc procedure and was not expected to yield significant findings because only seven
subjects fell into the PAK group. Findings were unexpectedly revealed. Subjects with
poor articulatory knowledge were slower and less accurate than subjects with adequate
articulatory knowledge in their ability to match phonemes. Response time on the
Phoneme Match Test was entered as a covariate in analyses examining naming latency,
which yielded an interesting interaction involving the covariate variable. During Block 1,
subjects with slower response time to the Phoneme Match Test were disproportionately
slower to name during Mouth Interference (see Figure 2) in comparison to their response
time during Foot Interference. Subjects with faster response time to the Phoneme Match
Test did not demonstrate a difference in naming response time between Mouth and Foot
Interference conditions. The slower subjects were to make judgments about phoneme
match, the more they were interfered by the Mouth Interference condition. The faster

109
subjects were to make judgments about phoneme match, the less effect of interference
condition (Mouth and Foot Interference) was seen. This interaction between Phoneme
Match and Interference condition went away by Block 2, such that the difference between
Mouth and Foot Interference response time remained similar regardless of subjects’
ability to match end phonemes (see Figure 3).
The response time of subjects with poor articulatory knowledge improved during
Block 2 while the response time of subjects with adequate articulatory knowledge did
not, and this was more salient during the Foot Interference than during the Mouth
Interference condition (Table 35). The improvement in the response time of subjects
with poor articulatory knowledge may be a practice effect. Subjects with adequate
articulatory knowledge did not show this practice effect because their reaction time was
already fairly comparable with that of the control group's (compare AAK group's RT on
Table 35 with CTRL group's RT on Table 26). Subjects with adequate articulatory
knowledge may be approaching floor effects on the naming task. That the response time
of subjects with poor articulatory knowledge improved in the Foot Interference condition
and not in the Mouth Interference condition suggested that Foot Interference was easier,
less interfering, or made less demands from processing resources of these subjects.
Together, these data showed that during Block 1, before practice effect
manifested, subjects who were slow to match end phonemes (who were likely subjects
with poor articulatory knowledge, as they were slower than subjects with adequate
articulatory knowledge on the Phoneme Match Test) were disproportionately slow to
retrieve names during Mouth Interference (Figure 2). This pattern went away by Block 2,
concurrent with the improvement in naming latency of subjects with poor articulator}'

110
knowledge. Their improvement in naming latency was more salient during Foot
Interference than during Mouth Interference (Table 35). For subjects who had poor
articulatory knowledge, it was harder for them to retrieve names while engaging in
interfering mouth movements. Foot movements had a less interfering effect. With
practice, subjects with poor articulatory knowledge were able to improve their overall
response time, thus diminishing their naming latency discrepancy between Mouth and
Foot Interference conditions.
The pattern described above was not present in the Visual Match data. Reaction
time on this task revealed only a Block effect. Block 1 was responded to more quickly
than Block 2, and accuracy data revealed that subjects were less accurate during Mouth
Interference than Foot Interference in Block 2. These were the same findings discussed
in the section comparing subjects grouped by reading achievement scores (PI vs. CTRL
and DPD vs. ARPP vs. CTRL). The Block effect likely reflected a stimulus artifact. The
Mouth Interference condition was more difficult than Foot Interference.
Interference Movement Frequency
PI vs. CTRL and DPD vs. ARPP vs. CTRL. In addition to reaction time and
response accuracy, the frequency of interference movements during each condition was
also examined as a dependent variable. Analyses comparing phonologically impaired
with control subjects revealed a significant Group X Task X Interference X Block
interaction. The phonologically impaired group produced interfering mouth movements
at a slower frequency during name retrieval, and this was especially salient during Block
1 (Table 18). The frequency by which these subjects produced interfering mouth

Ill
movements during the naming task was slower than during any other interference
condition. Compared to the control group, the phonologically impaired subjects'
interfering movement index was slower only during the Mouth Interference condition of
the naming task. When required to retrieve names, subjects with phonological
impairment were less able to engage their articulators in another task. This was not due
to the attentional demands of engaging in two tasks at once, because they were able to
produce interfering foot movements with equal facility as the control group. This also
was not due to problems with oral praxis, because they were able to produce interfering
mouth movements with equal facility as the controls during a nonverbal task.
When the phonologically impaired group was divided into two subgroups,
differences in the pattern of interfering movement frequency were shown. Both impaired
readers and non-impaired readers were slower in producing interfering mouth movements
during naming, as consistent with the interaction reported above. However, during other
conditions (i.e., naming Foot Interference, Visual Match interference conditions), there
was a trend of faster interfering movement frequency by the non-impaired readers, even
in comparison to controls, and a trend of slower interfering movement frequency by
impaired readers in comparison to controls (Table 28). Further indication of a difference
between the two phonologically impaired groups was found in the correlation between
interfering movement frequency index and the AAT score. There was no relationship
between the control group’s rate of interfering movement frequency and their AAT score.
The impaired readers showed a positive correlation between their rate of interfering
mouth movement and AAT score during the naming task only (Table 29). The better
their obtained AAT score, the faster they were able to produce interfering mouth

112
movements while engaging in a name retrieval task. This pattern was not shown by the
non-impaired readers. Instead, they have a negative correlation between their AAT score
and their rate of interfering foot movements during Visual Match. The better their
articulatory awareness, the slower they were in producing interfering foot movements on
a nonverbal, visual match test.
The interference movement frequency data revealed differences between groups
when response time and response accuracy data did not. This study had assumed that
naming latency would be affected by the presence of a second interfering task. However,
subjects focused their attention on the primary naming task and did not allow interfering
movements to impinge upon their performance on this primary task. The interfering
movements and name retrieval competed for neural resources. More resources turned out
to have been directed to the primary naming task, resulting in a slowing down of the
secondary, interfering movement task. Rather than having mouth and foot movements
interfere with the primary tasks as originally planned, the primary tasks turned out to
have interfered with mouth and foot movements. This pattern was true for the
phonologically impaired subjects only. They had to slow down the production of
interfering mouth movements because such movements interfered with their name
retrieval. They could not simultaneously engage in both name retrieval and a motor task
involving the articulators. This was important because it suggested that those who are
phonologically impaired may be more dependent on the motor system than normal
readers.
« '
PAK vs. AAK. Grouping subjects by their AAT performance yielded movement
frequency findings similar to that reported for Phonologically Impaired vs. Controls.

113
Subjects with poor articulatory knowledge produced interfering mouth movements more
slowly than interfering foot movements while engaging in the naming task, and this
pattern was not demonstrated by subjects with adequate articulatory knowledge. The
Group X Task X Interference interaction reflecting this pattern approached significance.
Although this was only a trend, that the three-way interaction approached significance
given the small sample size in the Poor Articulatory Knowledge group was remarkable.
The near presence of a high-level interaction with a group size of seven suggested
robustness of this interaction.
In addition to the primary aim of understanding the relationship between
articulatory awareness and name retrieval, two other questions were addressed. The first
of these secondary aims related to the influence of children with ADHD on the present
data set. The second explored the relationship between articulatory knowledge and
phonological awareness, and variables that predicted them.
Attention-Deficit/Hyperactivitv Disorder
The high co-morbidity rate between reading disability and ADHD rendered
attempts to limit ADHD in the present study impractical; what was learned about reading
disabled children's neurological system would have limited generalizability to the overall
reading disabled population if ADHD were considered an exclusionary criterion. Thus
rather than excluding subjects with ADHD from this study, these subjects were included
and ADHD status was noted to track their influence on the overall data set.
Subjects with ADHD tended to be slower and less accurate than their non-ADHD
counterparts. Excluding subjects with ADHD did not change the overall pattern of the

114
data, suggesting that although ADHD subjects tended to be slower and less accurate, they
responded to Interference conditions of NAPM and Visual Match Tests in a similar
pattern as non-ADHD subjects.
Relationship Between Articulatory Knowledge and Phonological Awareness
Predictors of articulatory knowledge were examined to explore if phonological
awareness was related to it. Variables that correlated with AAT score included the LAC
raw score (Tables 20) and the Phoneme Match accuracy. Examining groups individually
revealed that the correlation between AAT and LAC was from the control group. The
phonologically impaired readers' articulatory knowledge was not correlated with their
LAC score. The validity of the LAC as a measure of phonological awareness was
checked by its statistically significant correlation with performance on the Phoneme
Match Test, a task that also required phonological processing. Although articulatory
knowledge and phonological awareness was correlated for normal readers, that some
subjects performed poorly on the LAC (i.e., phonologically impaired subjects) but
adequately on the AAT (i.e., big variance on AAT score by phonologically impaired
subject) indicated that articulatory knowledge and phonological awareness were
dissociable phenomena. However, it should be noted that neither the AAT nor the LAC
was psychometrically strong measures of articulatory knowledge and phonological
awareness, respectively. Variance on both of these measures was large for all subject
groups. These instruments represented available although not ideal measures of
articulatory knowledge and phonological awareness.
*>

115
The only variable that predicted articulatory knowledge level was the non-reading
impaired group's Passage Comprehension performance. Phonological awareness was
more correlated with other measures. With all of the subjects together, phonological
awareness was positively correlated with age, BNT, and the three reading achievement
subtests in addition to articulatory knowledge. The pattern of relationship between
phonological awareness and each of these variables differed substantially by group. For
the control group, Word Attack was most highly correlated with phonological awareness,
not surprisingly as both Word Attack and LAC tested for phonological skills. Age was
also correlated with phonological awareness, indicating that for normal readers,
phonological awareness developed with age. For subjects who were phonologically
impaired, not surprisingly, there was no correlation between age and phonological
awareness because by definition, these subjects were those for whom phonological
awareness had failed to develop with age. Among the phonologically impaired, those
who had better phonological skills also had higher confrontation naming and reading
achievement scores. This probably reflected that there were high functioning or well
compensated readers who were phonologically impaired. Dividing the phonologically
impaired group into impaired and non-impaired reader subgroups decreased the number
of subjects available for these correlations to be significant. However, one negative
relationship was maintained despite small group size. For non-impaired readers, better
phonological awareness was correlated with slower rapid naming of color. The
relationship between rapid object naming and phonological awareness was in the same
direction as well. This negative relationship between rapid naming and phonological

116
skill may indicate that non-impaired readers slow down in speed in order to compensate
for their phonological impairment.
Unlike normal readers, those with phonological impairment did not develop
phonological skills concurrently with articulatory knowledge. Among the non-impaired
readers, phonological skills correlated negatively with rapid naming. The better the non-
impaired readers' phonological skills, the more slowly they were able to rapidly name.
Non-impaired readers as a group had worse phonological skills than the normal reader
group. Although this was a phonologically impaired group, defined by poor Word Attack
performance, data suggest that among these individuals, those with better phonological
skills took longer to finish rapid naming tasks. The Rapid Color and Object Naming
Tests used in this study scored the total time taken to complete the task and did not score
separately for errors. Most subjects self-correct their errors, thus faulty performance was
usually reflected in the longer time required to complete the task with self-correction.
Perhaps non-impaired readers with better phonological skills were those who caught their
mistakes and self-corrected, whereas those with very poor phonological skills were more
likely to complete the task sloppily with little attention to the precision of their responses.
Non-impaired readers' articulatory knowledge correlated positively with reading
achievement. The better their articulatory knowledge, the better they did on reading
achievement measures. It may be that these subjects use articulatory feedback as a
compensatory mechanism to supplement their impaired phonological skills. Although
these subjects have poor Word Attack score, their single-word reading and
comprehension were commensurate with their expected achievement levels. They may
be using the lexical route for reading with assistance from articulatory feedback. If so,

117
one would expect that these subjects would also use articulatory feedback in other
situations where such feedback could facilitate language functioning, such as in naming.
However, this was not supported by the lack of a correlation between AAT and BNT,
AAT and rapid naming (Table 30), and AAT and NAPM reaction time and interference
movement frequency (Tables 23 and 29).
The impaired readers did not evidence the same pattern as the non-impaired
readers. Their poor phonological skills were not correlated with any variable, including
articulatory knowledge. Unlike the non-impaired readers, the level of their phonological
skill did not affect their speed of rapid naming, and their articulatory knowledge was not
correlated with their reading achievement. This group, by definition, had poor single¬
word reading and comprehension in addition to poor phonological skills. In contrast with
the non-impaired readers, who may be using the lexical route for reading, impaired
readers appeared not to use the lexical or phonological routes for reading. Their LAC
score, Word Attack, Word Identification, and Passage Comprehension scores were all
worse than the non-impaired readers. Their AAT score was not statistically different
from that of the non-impaired readers although the direction of score differences between
groups suggested that impaired readers may have the worst level of articulatory
knowledge. The AAT’s inability to differentiate groups on level of articulatory
knowledge was a significant limitation of this study.
A possible explanation for the differences just described was that the impaired
readers represented the negative extreme on a continuum of phonological and reading
skills. Their articulatory knowledge was not correlated with better reading achievement
because basic skills were so poorly developed that compensatory strategies were not

118
sufficient to facilitate language functioning. Argument for a severity-of-impairment
difference between impaired and non-impaired readers came from the data on the name
retrieval task. Although impaired readers with better articulatory knowledge produced
interfering mouth movements more quickly (positive correlation between AAT and
interfering mouth movement frequency for the DPD group; Table 29), these same
subjects were slower in their response time on the name retrieval task (negative
correlation between AAT and NAPM Mouth Interference response time; Table 23). Thus
the positive correlation between AAT and interfering mouth movement frequency did not
necessarily mean that articulatory feedback did not influence naming; naming latency
was negatively impacted and was slower. In fact, similar to the non-impaired readers,
impaired readers' overall rate of interfering mouth movements had to slow down in order
to perform the name retrieval task (Table 28). This was not due to dispersing attention
between dual tasks because the same subjects were able to produce interfering foot
movements at the same rate as control subjects while engaging in name retrieval. This
also was not due to interfering mouth movements being harder to produce than
interfering foot movements because all subjects produced both with equal facility on the
nonverbal control task. The specificity of slowed interfering mouth movement during the
name retrieval task suggested that articulatory feedback interacted with name retrieval for
these subjects.
Articulatory Feedback Hypothesis of Naming
The most direct test of the articulatory feedback hypothesis of naming was
through examining performance patterns of those with poor articulatory knowledge

119
(PAK) and those with adequate articulatory knowledge (AAK). Grouping subjects by
reading achievement turned out to be an indirect way to test this hypothesis, as
phonologically impaired readers turned out not to have worse articulatory’ knowledge
compared to normal readers.
The articulatory feedback hypothesis of naming posited that for individuals with
adequate articulatory knowledge, articulatory feedback facilitates name retrieval.
Interfering with this feedback should slow down name retrieval. Individuals with poor
articulatory knowledge should respond differently. Although naming latency data did not
reveal theoretically important findings, interfering movement frequency data showed that
subjects with poor articulatory knowledge tended to produce interfering mouth
movements most slowly while engaging in name retrieval. Subjects with adequate
articulatory knowledge produced such movements at the same rate as during other control
conditions. In other words, for subjects with adequate articulatory knowledge,
articulatory feedback (i.e., Foot Interference) and no articulatory feedback (i.e., Mouth
Interference) conditions did not make a difference in their response latency or frequency
of interfering movements produced. They did not spontaneously use articulatory
feedback to assist with name retrieval when feedback was available (i.e., Foot
Interference latency). Providing inappropriate articulatory feedback did not slow down
their name retrieval (i.e., Mouth Interference latency). Engaging in a naming task did not
differentially influence the facility of their ability to produce interfering mouth or foot
movements.
Individuals with poor articulatory knowledge responded differently. Engaging in
name retrieval interfered with their mouth movements and slowed down the frequency of

120
mouth movements. Naming and interfering mouth movements competed for the same
neural resource. When naming was attended to as a primary task, interfering mouth
movements must slow down. However naming did not compete with foot movements.
Subjects with poor articulatory knowledge were able to name and produce interfering
foot movements with the same facility as the control subjects. Something about mouth
movements interfered with name retrieval, suggesting that these two processes share
some neural connectivity. The decreased rate of mouth movement production must be
related to this shared connectivity and can not due to this being a “harder” interference
task because all subjects were able to produce mouth and foot movements with equal
facility during a nonverbal control task.
In the present study, subjects attended to the naming task as the primary task and
sacrificed performance on the secondary interference task. If they had been instructed to
maintain a constant mouth movement, making this the primary task, the shared
connectivity' between mouth movement and name retrieval would likely cause these
subjects’ response time on naming tasks to slow down in comparison to their response
latency while maintaining a constant foot movement. This indirectly supported that
inappropriate articulatory feedback inhibited name retrieval. However, this inhibition
effect was seen in those with poor articulatory knowledge rather than in those with
adequate articulatory- knowledge. For individuals who have difficulty knowing the
position of their articulators while making speech sounds, articulatory feedback affected
their naming process. Individuals who have a good sense of articulatory position were
not affected by articulatory feedback during name retrieval. This difference between
groups may be due to efficiency of language processing. For those who have well-

121
developed language systems, name retrieval was executed automatically and did not
require much neural resources. Inappropriate articulatory feedback did not slow down
naming because sufficient resources were available to process both the feedback and
name retrieval at the same time. Compromised articulatory awareness may reflect
abnormal development of a language-related neural system. Under compromised
conditions, name retrieval, a language task, could not be executed as automatically as
under uncompromised conditions. More neural resources were necessary to complete the
same task. Inappropriate articulatory feedback competed for the same neural resource as
that needed for naming, resulting in inefficient processing of one of these tasks.
Limitations
The interference paradigm used in this study allowed for the examination of
naming latency in the presence of excess, irrelevant information making demands for
neural resources. This allowed for the examination of inhibition effects, not facilitation
effects. Therefore the modification of the hypothesis can address factors that impede
naming, but whether appropriate articulatory feedback facilitates naming was not directly
addressed by the design of this study and remains an empirical question.
The assessment of articulatory knowledge in the present study was based on the
Articulatory Awareness Test. This was an experimental instrument that has not
undergone rigorous validity and reliability testing to prove its efficacy in accurately
identifying those with good articulatory knowledge from those without articulatory
knowledge. This instrument was used because it was the only one known to assess the
level of articulatory knowledge. The present data showed that both normal and

122
phonologically impaired readers vary greatly on their performance on this task. That is,
reading achievement and articulatory knowledge dissociated and were not dependent on
each other. While this may be a “true” finding, replication is necessary to rule out this as
an artifact of the assessing instrument.
There were several limitations related to methodology. First, interference
movements were not successful in interfering with the naming task. Subject attended to
the naming task as the primary task instead, and this primary task affected the rate of
production of the mouth and foot movements. The lack of an interference effect on
naming latency precluded direct conclusions about whether inappropriate feedback
inhibited naming latency. This must be inferred indirectly. A better methodology would
have been to control for the rate of interfering movements to allow for examination of
inhibition effects on naming latency.
Second, unexpected Block effects were found. This was likely due to a stimulus
artifact in the Visual Match stimulus sets. These stimuli were unintentionally arranged in
order of increasing difficulty, with more difficult items appearing the in the second half
of the trials. Because the block effects found mainly involved the Visual Match Test,
other factors, such as fatigue or practice effects, were not likely to have been operative.
However, these confounding factors cannot be completely ruled out.
Finally, although the two NAPM stimulus sets were balanced on word frequency,
number of syllables, and grade level by which the word is taught, they still turned out to
differ in difficulty level. Subjects responded to Set A faster than to Set B. Stimulus sets
were counterbalanced across interfering conditions and across subjects. This randomly
dispersed the variance introduced by different difficulty levels of the two stimulus sets in

123
the study. It is not known whether more balanced stimulus sets would have reduced
random variance sufficiently for naming latency difference between conditions/groups to
reach significance.
Summary of Findings
With these limitations in mind, findings of this study could be summarized as
follows:
Correlation Between Articulatory Knowledge and Naming
For normal readers, articulatory knowledge and name retrieval were related.
Although normal readers did not spontaneously use articulatory feedback to assist
naming, when their attention was drawn to their articulators, better articulatory
knowledge was associated with faster naming latency. This association was not due to
articulatory feedback. It may be related to “spreading” activation to the name retrieval
systems from pre-motor and motor areas controlling the articulators.
Dvslexics Do Not Have Worse Articulatory Knowledge
Montgomery’s (1981) finding that dyslexics have impaired articulatory
knowledge was not wholly supported. Both normal and phonologically impaired readers
demonstrated a wide range of articulatory knowledge. The present version of the
Articulatory Awareness Test may not have the psychometric properties required to
differentiate groups by their level of articulatory knowledge even if a "true" difference
existed. Articulatory knowledge and reading achievement were correlated only for

124
phonologically impaired readers who have adequate single-word reading and
comprehension skills. The possibility that these individuals use articulatory knowledge
to help them compensate and thus achieve expected reading levels was posited.
Relationship Between Articulatory Knowledge and Phonological Awareness
Articulatory knowledge and phonological awareness were correlated among
normal readers but not for phonologically impaired readers. Because intact articulatory
knowledge can manifest in a person with impaired phonological awareness, these are
dissociable phenomena.
Modification of the Articulatory Feedback Hypothesis of Naming
Inappropriate articulatory feedback impeded naming in those who have poor
articulatory knowledge. Those with compromised articulatory knowledge were more
vulnerable to be disrupted in the name retrieval process by irrelevant linguistic
information. Individuals with adequate articulatory knowledge were free from such
vulnerability, probably because their efficiency of processing freed neural resources to
handle extraneous information. Among normal readers, heightened articulatory
knowledge was associated with faster naming latency. However, articulatory feedback
per se was not the major contributor to this relationship, because this positive correlation
between articulatory knowledge and faster naming latency occurred during the presence
of articulatory feedback that was inappropriate to the naming task at hand. This study
found no data to support that articulatory feedback was used spontaneously to facilitate
naming.

APPENDIX 1
ARTICULATORY AWARENESS TEST
Name
Date
Date of Birth
Age
Grade
Sex
Race
Handedness
Pantomime
The Examiner should put out picture #8 (/a/) and go over the areas of the mouth
making sure that the patient is aware of the tongue, lips, and teeth. After the Examiner
has reviewed these, the patient should be asked to imitate the position that is seen with
cards 8, 7, and 6.
Picture # Pantomime Score Efficiency Rating
(+/-) (1 -groping, 2-slow, 3-easy)
/8/ 1.
m 2.
¡61 3.
Practice Items
(The cards are numbered on the back. The Examiner holds them in a fan (as with
a card game) with the numbers facing the Examiner to allow easy access to test items).
125

126
Examiner: "Now I am going to say a sound, and I want you to repeat the sound
after me several times. As you repeat the sound I want you to think about how you are
making the sound."
Produce /a/ "as in apple" and have the patient say it after you several times. Do
not let the patient see your mouth as you say it [hold cards in front of Examiner's mouth],
"Now I want you to feel the sound as you say it and then point to the picture
which matches how your mouth feels when you make the sound."
Place cards, 8, 3, 6 in front of the patient to be chosen from. Praise correct
response and ask why he/she chose that picture. If incorrect, review the three pictures as
before (i.e., placement of tongue, lips, and teeth). Then have the patient produce the
sound again and select the corresponding picture.
Follow the same procedure for /p/ (cards 1, 7, 2) and /th/ (5,2, 1).
Practice Item Score Efficiency Rating
(+/-) (1,2,3)
1. a apple (8 3 6)
2. p pie (1 7 2)
3. th that (5 2 1)
-s

127
Test Items
Directions
"Let's try a few more. Listen to the sound I say and repeat it after me several
times. Remember, as you say the sound, think about how the sound feels as you are
making the sound. Then choose the picture which shows you how you are making the
sound. I'll show you one card at a time. When you see the one that matches, tell me, and
we will go on to the next sound. Only one card will match, but sometimes I'll be tricky
and no card will match. Ready?"
[If the patient produces a sound in an unusual way (e.g., /I/ with tongue between
teeth), none of the pictures may be representative, so a "none" response is
Test Items
1. s (1 7 5)
saw
2. v (3 6 4)
vase
3. t (8 3 2)
table
4. th (2 7 5)
thermometer
b (7 1 8)
bicycle
Score Quality
(+/-) (1,2,3)
•s
5.

128
6. 1 (6 4 2)
ladder
7. d (5 8 1)
dog
8. f (6 1 4)
fish
9. e (8 2 7)
exit
10. k (3 5 1)
kite
Total
Did the subject use hands/fingers on mouth? Yes No
Did the subject try a mirror? Yes No
Please note if subject produces sound differently, e.g., tongue out or flat for /!/.

Experimental Test Items
Test Items
11. m (5 6 7)
mom
12. ee (7 2 8)
each
Í3. z (3 7 5)
zap
14. a (6 1 3)
gas
15. a (5 4 8)
ant
16 n (7 6 3)
nice
17. th (1 2 8)
thick
18. 1 (1 6 5)
leg
19 p (1 7 4)
Score
(+/-)
Quality
(1,2,3)
puppy

dance
Experimental Total
Total from #1-10
Combined Total

Card 1: /k/, /g/
131
Card 2: /th/

Card 3: /t/, /d/, Ini
132
Card 4: /!/

Card 5: /s/, Izl
133

Card 8: /a/, Id
r

APPENDIX 2
ATTENTION-DEFICIT/HYPERACTIVITY DISORDER INTERVIEW
Subject:
Date:
ADHD Questionnaire
Inattention
a. Does your child often fail to give close attention to details or make careless
mistakes in schoolwork or other activities?
b. Does s/he often have difficulty sustaining attention in tasks or play?
c. Does s/he often not seem to listen when spoken to directly?
d. Does s/he often not follow through on instructions and fail to finish schoolwork or
chores (not due to oppositional behavior or failure to understand instruction)?
e. Does s/he often have difficulty organizing tasks and activities?
135

136
f. Does s/he often avoid, dislike, or is reluctant to engage in tasks that require
sustained mental effort (such as school work or homework)?
g. Does s/he often lose things necessary for tasks or activities (e g., toys, school
assignment, pencils, or books)?
h. Is s/he often easily distracted by extraneous stimuli?
i. Is s/he often forgetful in daily activities?
Hyperactivity
a. Does s/he often fidget with hands or feet or squirm in his/her seat?
b. Does s/he often leave his/her seat in classroom or in other situation in which
remaining seated is expected?
c. Does s/he often run about or climbs excessively in situations in which it is
inappropriate9
d. Does s/he often have difficulty playing or engaging in leisure activities quietly?
e. Is s/he often "on the go"' or often act as if “driven by a motor?”

137
f.Does s/he often talk excessively?
Impulsivity
g.Does s/he often blurt out answers before questions have been completed?
h.Does s/he have difficulty awaiting turn?
i.Does s/he often interrupt or intrude on others (e.g., butts into conversations or
games)?
When did you first notice these behaviors?
How long did these behaviors last?
Do you think your child’s behaviors are worse than can be expected given his/her age?
Where do you notice these behaviors? Are they present in more than one situation?
How have these behaviors affected your child’s social or academic functioning?

138
314.00 ADHD, Predominantly Inattentive
Inattention (6)
314.01 ADHD, Combined (6-6) or Predominantly Hyperactive-Impulsive (6)
Hyperactivity Impulsivity
6 mo
Inconsistent w/ developmental level
before 7 yrs
> 1 setting
social or academic impairment

APPENDIX 3
NAPM STIMULI
Stimulus Set
A
Stimulus Set
B
A1
Gun
Sun
Y
B1
Finger
Tiger
Y
A2
Parrot
Carrot
Y
B2
Knife
Nose
N
A3
Violin
Gorilla
N
B3
Star
Car
Y
A4
Bird
Flag
N
B4
Balloon
Scissors
N
A5
Shovel
Basket
N
B5
Ball
Frog
N
A6
Hair
Pear
Y
B6
Magnet
Pirate
Y
A7
Dress
Lamp
N
B7
Vest
Nest
Y
A8
Wagon
Dragon
Y
B8
Barrel
Sandwich
N
A9
Volcano
Piano
Y
B9
Cake
Snake
Y
A10
Broom
Eye
N
BIO
Mitten
Kitten
Y
All
Pumpkin
Napkin
Y
B11
Squirrel
Clothespin
N
A12
Elephant
Strawberry
N
B12
Medal
Needle
Y
A13
Seal
Wheel
Y
B13
Bed
Bread
Y
A14
Swan
Comb
N
B14
Kite
Book
N
A15
Mushroom
Wallet
N
B15
Bam
Pipe
N
A16
Tree
Key
Y
B16
Canoe
Bamboo
Y
A17
Camel
Banana
N
B17
Guitar
Monkey
N
139

140
A18
Mountain
Fountain
Y
B18
Axe
Bowl
N
A19
Clown
Crown
Y
B19
Bee
Knee
Y
A20
Owl
Bus
N
B20
Ladder
Camera
N
A21
Dolphin
Coffin
Y
B21
Ear
Deer
Y
A22
Umbrella
Porcupine
N
B22
Saddle
Feather
N
A23
Leaf
Drum
N
B23
Muscle
Pencil
Y
A24
Cat
Hat
Y
B24
Sheep
Purse
N
A25
Flower
Shower
Y
B25
Bear
Chair
Y
A26
Bow
Fish
N
B26
Fox
Box
Y
A27
Necktie
Butterfly
Y
B27
Pocket
Rocket
Y
A28
Chimney
Zebra
N
B28
Pie
Whale
N
A29
Lock
Clock
Y
B29
Bucket
Turkey
N
A30
Dog
Cup
N
B30
Folder
Shoulder
Y
A31
Giraffe
Hotdog
N
B31
Sweater
Rabbit
N
A32
Moon
Spoon
Y
B32
Desk
Goat
N
Note: Y/N indicates if end phonemes match.

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BIOGRAPHICAL SKETCH
Lisa Hsiao-Jung Lu was bom in Taipei, Taiwan, in 1971. She immigrated to the
United States with her parents in 1980 and completed her secondary education in Tucson,
Arizona. She obtained her Bachelor of Arts degree summa cum laude from the
Washington University in St. Louis, where she majored in psychology and minored in
English literature. During her study there, she developed an interest in neuropsychology
and decided to pursue this formally at the University of Florida, Department of Clinical
and Health Psychology . Over the course of her graduate study, she developed an interest
in the neuropsychological layout of the language system within the central nervous
system. Her initial work in this area involved understanding the semantic organization of
the language system through studying category-specific naming deficits. This research
earned her recognition from the American Psychological Foundation, the Manfred Meier
scholarship. Lisa H. Lu also has an interest in the developmental aspects of language.
The present dissertation represents her initial query into the development of
neuropsychological subcomponents that work together to yield a functional language
system. She hopes to further this line of inquiry by studying dysfunction within a
pictographic reading and writing system, the Chinese language. By studying two
manifestations of language, English and Chinese, she hopes to contribute to
neuropsychology's understanding of nervous system components that work together to
allow the complex behavior we call language.
146

I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
CjLloy^ ^CtwjJUL
Eileen B. Fennell, Chair
Professor of Clinical and Health Psychology
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
/Bruce Crosson
Professor of Clinical and Health Psychology
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
l)»~- t\dL
Duane E. Dede
Clinical Associate Professor of
Clinical and Health Psychology
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy. y
Kenneth M. Heilman
Distinguished Professor of Clinical and
Health Psychology
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Jaifees J. Algina
Professor of Edu
ecology

This dissertation was submitted to the Graduate Faculty of the College of Health
Professions and to the Graduate School and was accepted as partial fulfillment of the
requirements for the degree of Doctor of Philosophy.
August, 2000
Dean, College of Health Profession
Dean, Graduate School

Lisa Hsiao-Jung Lu
5020 S. Lake Shore Dr, #1211
Chicago, IL 60615
773-643-8875
lhlu@vahoo.com
August 1,2000
College of Health Professions
University of Florida
Box 100185
Gainesville, FL 31610-0185
RE: Dissertation
Dear College Office:
Enclosed is the final version of my dissertation for your records. If you have
questions, please contact me via phone or email. Thank you very much!
Sincerely,
Lisa Hsiao-Jung Lu

„UNIVERS|TY OF FLORIDA
3 1262 08555 2726