Memory Functioning in Dyslexia 2

Running Head: Memory Functioning in Dyslexia

Memory functioning in developmental dyslexia: An analysis using two clinical memory

measures

Michelle Y. Kibby

Southern Illinois University - Carbondale

Correspondence: Michelle Kibby, PhD, Southern Illinois University-Carbondale, Department of

Psychology, LSII, Room 281, Carbondale, IL 62901. mkibby@siu.edu

Acknowledgement: This project was funded in part by a grant awarded to the author from the

National Institutes of Health, National Institute of Child Health and Human Development (R03

HD048752).

Kibby, M. Y. (2009). Memory Functioning in Developmental Dyslexia: An Analysis Using Two

Clinical Memory Measures. Archives of Clinical Neuropsychology, 24(3), 245–254.
http://doi.org/10.1093/arclin/acp028

The published article is available at: https://acn.oxfordjournals.org/content/24/3/245

 Memory Functioning in Dyslexia 3

Abstract

The goals of this project were threefold: to determine the nature of the memory deficit in

children/adolescents with dyslexia, to utilize clinical memory measures in this endeavor, and to

determine the extent to which semantic short-term memory (STM) is related to basic reading

performance. Two studies were conducted using different samples, one incorporating the

WRAML and the other incorporating the CVLT-C. Results suggest that phonological STM is

deficient in children with dyslexia, but semantic STM and visual-spatial STM are intact. Long-

term memory (LTM) for both visual and verbal material also is intact. Regarding reading

performance, semantic STM had small correlations with word identification and pseudoword

decoding across studies despite phonological STM being moderately to strongly related to both

basic reading skills. Overall, results are consistent with the phonological core deficit model of

dyslexia as only phonological STM was affected in dyslexia and related to basic reading skill.

Keywords: dyslexia; reading disabilities; child; adolescent; short term memory; long term

memory
 Memory Functioning in Dyslexia 4

Over the past few decades a great deal of research has been conducted on short-term

memory (STM) functioning in children with dyslexia. While many suggest verbal STM is

impaired in this population (for reviews see Baddeley & Hitch, 1994; Jorm, 1983; McDougall &

Hulme, 1994), the findings on visual STM have been disparate. Several researchers have found

visual STM is intact in dyslexia (Jeffries & Everatt, 2004; Kibby & Cohen, 2008; Kibby, Marks,

Morgan, & Long, 2004; McDougall, Hulme, Ellis, & Monk, 1994; for a review see Jorm, 1983;

McDougall & Hulme, 1994), while others have found visual STM is impaired, even when using

stimuli that cannot be verbally coded (Henry, 2001; Howes, Bigler, Burlingame, & Lawson,

2003; Howes, Bigler, Lawson, & Burlingame, 1999; Kaplan, Dewey, Crawford, & Fisher, 1998).

Furthermore, there is debate regarding the nature of the verbal STM deficit in dyslexia.

Many suggest the deficit is a result of difficulty encoding material by its sound (called “phonetic

coding” and “phonological STM” for the purposes of this study; Kibby, in press; Kibby &

Cohen, 2008; Wagner, Torgesen, & Rashotte, 1994), whereas encoding material by its meaning

is intact (called “semantic coding” and “semantic STM” for the purposes of this study; Jorm,

1983; Lee & Obrzut, 1994). Consistent with this belief, poor phonological processing is

considered to be the ‘core’ deficit in dyslexia (Liberman & Shankweiler, 1991; Rack, Snowling,

& Olson, 1992; Stanovich, 1988; Wagner et al., 1994), and phonological STM is one component

of phonological processing. Nonetheless, some researchers have found semantic STM deficits in

dyslexia (Delis, Kramer, Kaplan, & Ober, 1994; Kaplan et al., 1998; Kramer, Knee, & Delis,

1999). Thus, the verbal STM impairment may be more general in nature or specific to

phonological coding.

An issue receiving limited research over the past few decades is whether long-term

memory (LTM) is intact in dyslexia when deficits at encoding are controlled. Moreover, the few
 Memory Functioning in Dyslexia 5

studies conducted in this area have yielded inconsistent results, with one researcher finding

impaired verbal LTM (Kaplan et al., 1998) and others reporting intact verbal and visual LTM

(Jorm, 1983; Kibby & Cohen, 2008; Kramer et al., 1999). Another topic requiring further

investigation is semantic STM’s relation to word identification and decoding skill. Although

several researchers have found phonological STM to be predictive of basic reading skill

(Cormier & Dea, 1997; Hansen & Bowey, 1994; Kibby, in press; for a review see Bishop &

Snowling, 2004), limited research has been conducted on semantic STM’s relation to basic

reading ability.

Hence, the purposes of this project were threefold: to determine the nature of the memory

deficit in children with dyslexia, to utilize clinical measures of memory in this endeavor, and to

determine the extent to which semantic STM is related to basic reading performance. In terms of

the second purpose, much of the prior research on dyslexia has utilized experimental measures of

memory functioning, limiting its generalizability for clinical neuropsychologists at large who use

clinical measures. Therefore, the Wide Range Assessment of Memory and Learning (WRAML;

Sheslow & Adams, 1990) and the California Verbal Learning Test-Children’s Version (CVLT-

C; Delis et al., 1994) were used as the WRAML assesses phonological, semantic and visual

STM, along with LTM, and the CVLT-C assesses semantic coding, STM and LTM. Two studies

were conducted using different samples, one including the WRAML and the other including the

CVLT-C. Thus, much of the remainder of the article will discuss the two studies separately.

Study 1

Limited research has been conducted on the WRAML in dyslexia despite its frequent use

in clinical practice. Of the research conducted, Kaplan and colleagues (1998) found children with

dyslexia performed worse than controls on STM subtests that foster phonetic coding (Sentence
 Memory Functioning in Dyslexia 6

Memory, Sound Symbol and Number/Letter). They also performed worse than controls on

Verbal Learning, a STM measure that allows semantic coding (task entails 4 learning trials using

familiar words that can be recalled in any order). Nonetheless, they still performed within the

Average range on this subtest. Children with dyslexia performed comparably to controls on Story

Memory, a STM subtest which fosters semantic coding. They also performed comparably to

controls on most measures of visual STM except Finger Windows, a measure of serial-order

visual-spatial STM. Children with dyslexia performed worse than controls on one measure of

LTM (Story Memory savings score). Taken together, the findings of Kaplan et al. suggest that

the memory deficits in dyslexia may extend beyond phonological STM.

Despite the findings of Kaplan et al. (1998), it was hypothesized that children with

dyslexia would perform worse than controls on the phonological STM subtests only;

performance on the rest of the measures would be intact given the literature reviewed in the

general introduction. It also was hypothesized that phonological STM (Number/Letter) would be

related to Word Attack but semantic STM (Story Memory) would not due to the phonological

nature of decoding. Examination of semantic STM’s relation to word identification was

exploratory given the limited research examining the relation between the two skills.

Method

Participants. Twenty children with dyslexia and 20 controls, ages 9-13 years, were tested

with the WRAML. These data were collected during an earlier study (Kibby, Marks et al., 2004).

Sixty percent of the dyslexia group were male, and 45% of the control group were male. Groups

were equated on prior diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD), age, grade

level, gender, and socio-economic status (SES). Children with a prior diagnosis of ADHD were

included in the study due to the high comorbidity between dyslexia and ADHD (Holborow &
 Memory Functioning in Dyslexia 7

Berry, 1986; Shaywitz, Fletcher, & Shaywitz, 1994). However, parents of children with ADHD

reported that their child had sufficiently mild ADHD so as to not warrant medication. Presence

of ADHD was equated across the groups: 3 children in the dyslexia group and 2 children in the

control group.

Dyslexia was diagnosed according to State of Tennessee criteria for a specific learning

disability in reading, as children with dyslexia were recruited through the local school system. To

be diagnosed with a learning disability by the State at the time of data collection, children had to

be of normal intelligence, reading below grade level, and have at least a one standard deviation

discrepancy between their reading ability and measured intelligence. In addition, their reading

problems could not be better accounted for by a medical/neurological condition, sensory or

motor impairment, emotional disturbance, or quality of education. Children were selected for this

study through review of school records once school and parental permission were obtained.

To be included in the dyslexia group, there had to be at least a standard deviation

discrepancy between the child’s measured intellect and his/her word identification standard score

given State learning disability criteria. Poor word identification was chosen as the defining

feature as opposed to poor reading comprehension because poor decoding skills are the central

deficit in most definitions of dyslexia/reading disability (Lyon, Fletcher, & Barnes, 2003).

Intelligence and academic achievement scores were obtained from school records. All the State’s

school psychologists used the Wechsler Intelligence Scale for Children, Third Edition (WISC-

III) to measure intelligence. They used one of the following measures to assess academic

achievement: Wide Range Achievement Test–Revised, Wechsler Individual Achievement Test

or the Woodcock-Johnson–Revised Tests of Achievement (WJ-R). As the discrepancy level

 Memory Functioning in Dyslexia 8

required by the State was small, some children had a mild form of dyslexia. Nonetheless, the

mean discrepancy in the dyslexia group was 25.70 standard score points (range: 15 - 55).

Controls were recruited through the university’s subject pool (undergraduates brought

them in for testing with parental consent), as well as through flyers and advertisements in the

local community. A screening version of the WISC-III was administered to the control group to

reduce testing time. The screening version included Information, Vocabulary, Picture

Completion, and Block Design. This short-form is highly correlated (r = 0.94) with the full

battery (Sattler, 1992). IQ scores were prorated from this battery, with Verbal IQ (VIQ) being

prorated from Vocabulary and Information and Performance IQ (PIQ) being prorated from

Picture Completion and Block Design. Academic achievement was screened with the Wide

Range Achievement Test–Third Edition (WRAT-3) to verify controls did not have an IQ/word

identification discrepancy. Controls also were screened for prior special education evaluation and

assistance and for repetition of grade levels to ensure they did not have a history of learning

problems. All children were administered the WJ-R Word Attack subtest to assess their

phonological decoding skill. The mean prorated Full-Scale IQ/WRAT-3 Reading discrepancy for

the control group was -7.10 (range: -39 – +10). There was an overlap in reading ability between

the two groups for two controls whose WRAT-3 Reading scores were 85 and 92, as the highest

word identification standard scores for the dyslexia group were in the 90s. The rest of the

controls had WRAT-3 Reading standard scores that were greater than 100.

All children were screened for uncorrected sensory impairment, medical conditions,

neurological conditions (including seizures and traumatic brain injury), and psychiatric disorders

through a questionnaire completed by the parent. Children were excluded from the study if they

had any of these conditions, except for allergies or mild ADHD. No child was prescribed mood
 Memory Functioning in Dyslexia 9

altering or stimulant medication. All children were fluent in English. All children also had a

measured intellect of 80 or above.

The two groups were compared to assess how well they were equated on age, grade level,

gender, SES, and IQ. Groups were comparable in age, grade, FSIQ, PIQ, and VIQ using

ANOVA (ps > .10). They also were comparable in gender, SES, and presence of ADHD using

chi square (ps > .10). Groups differed in word identification [F(1,37)=65.83, p < .001], WJ-R

Word Attack [F(1,37)=44.17, p < .001], and spelling ability [F(1,35)=23.04, p < .001] but were

comparable in math calculation skills, F(1,36)=3.18, p > .05. See Table 1 for descriptive data.

<Table 1 here>

Measures. The WRAML purports to be the first comprehensive measure of memory

functioning for children/adolescents (Bigler & Adams, 2001). The verbal STM subtests include

immediate memory for stories (Story Memory), sentences (Sentence Memory), word lists

(Verbal Learning) and number/letter strings (Number/Letter Memory), along with Sound

Symbol. Although Sound Symbol requires paired associate learning of nonsense sounds and

symbols, researchers have suggested it loads more heavily on verbal factors than visual ones

(Burton, Mittenberg, Gold, & Drabman, 1999; Dewey, Kaplan, & Crawford, 1997). The visual

STM measures include immediate memory for meaningful scenes (Picture Memory), serial recall

for strings of spatial positions (Finger Windows), and short-term memory for geometric figures

(Design Memory) and spatial positions (Visual Learning). STM subtests have a mean of 10 and a

standard deviation of 3. LTM is measured through savings scores, subtracting number of items

recalled at long-delay from those recalled at short-delay. Hence, deficits at encoding are

controlled. Measures with savings scores include Story Memory, Verbal Learning, Sound

Symbol, and Visual Learning. Story Memory also has a long-delay recognition subtest.
 Memory Functioning in Dyslexia 10

The WRAML has been described in detail elsewhere including its reliability and validity

(Bigler & Adams, 2001; Sheslow & Adams, 1990); therefore, its psychometric properties will

not be discussed here. Prior research suggests verbal STM tests that require serial order recall,

verbatim recall, or nonword recall necessitate greater phonetic coding than those which permit

recall of words/sentences in any order (Henry, 2001; Howes et al., 2003; Swank, 1994). Thus,

based on their content, the verbal STM subtests likely vary in the extent to which they support

semantic coding, with Story Memory fostering the greatest semantic coding, then Verbal

Learning as the words are familiar and can be recalled in any order, then Sentence Memory as

verbatim repetition is required, and lastly Number/Letter. Number/Letter may require the

greatest reliance on phonetic coding as the stimuli are presented orally, encouraging focus on

their phonological rather than their orthographic characteristics; the stimuli are not words; and

the stimuli must be recalled in serial order. This proposed ordering of subtests, from those

fostering the greatest semantic coding to those fostering the greatest phonetic coding, is

consistent with subtest inter-correlations. See Table 2 for inter-correlations.

<Table 2 here>

Articulation rate was assessed as the verbal STM buffer is reported to hold as much

information as an individual can say in 2 seconds (Baddeley, 1986). Articulation rate was

measured using a modified approach from Roodenrys, Hulme, and Brown (1993). Specifically,

children were presented with 40 words, in 20 pairs. Each pair of words was repeated as often as

necessary for the child to say the pair correctly. Once the child could repeat the word pair

accurately, he/she had to say the pair 10 times as quickly as possible, and the time required to do

this was recorded. The mean of these times was transformed to yield a measure of items spoken

per second. Groups had comparable articulation rates [F(1,37)=1.02, p > .10].
 Memory Functioning in Dyslexia 11

Procedure. The WRAML, Word Attack, and articulation rate measures were administered

on the first day of testing after parental informed consent/child informed assent were obtained.

The WISC-III screener and WRAT-3 were administered to controls on a second testing day.

Results

WRAML. Three sets of MANOVAs were run to test for group differences in memory

performance: one containing the verbal STM subtests, one containing the visual STM subtests,

and one containing the LTM savings scores. For the verbal STM measures, the omnibus tests

were significant [F(5,33)=3.25, p < .05]. At the univariate level groups differed on Sentence

Memory [F(1,37)=4.96, p < .05] and Number/Letter Memory [F(1,37)=17.05, p < .001]. Group

differences approached significance on Sound Symbol, F(1,37)=3.63, p=.06. In contrast, the

groups performed comparably on Story Memory [F(1, 37) < 1.0, p > .10] and Verbal Learning

[F(1, 37) < 1.0, p > .10]. The omnibus tests were not significant for the visual STM measures

[F(4,34) < 1.0, p > .10] nor the LTM savings scores [F(5,31) < 1.0, p > .10]; furthermore, none

of the univariate tests were significant. See Table 3 for WRAML descriptive data.

<Table 3 here>

Verbal STM. As children with dyslexia were hypothesized to have greater difficulty with

phonological STM than semantic STM, a paired t-test was run to compare performance on

Number/Letter and Story Memory. The paired t-test was significant for the dyslexia group,

t(18)=3.76, p=.001, but it was not significant for the control group, t(19)=0.78, p > .10.

Basic reading performance. Using the total sample, partial correlations were conducted

between Story Memory, Number/Letter Memory, word identification and Word Attack,

controlling Full-Scale IQ and articulation rate. In order to determine whether verbal STM may be

directly related to basic reading ability, articulation rate was controlled given work by
 Memory Functioning in Dyslexia 12

McDougall and colleagues (1994) which suggests slow articulation rate may mediate the relation

between verbal STM and basic reading skill. FSIQ was controlled to see if verbal STM is related

to reading performance beyond general intellectual ability. See Table 4. Results did not change

when not controlling FSIQ: Story Memory had small correlations with word identification

(r=.12, p > .10) and Word Attack (r=.24, p > .10), but Number/Letter had moderate to large

correlations with word identification (r=.55, p < .001) and Word Attack (r=.70, p < 001).

<Table 4 here>

Discussion

Memory performance. Consistent with hypotheses, children with dyslexia performed

worse than controls on the phonological STM subtests. Some caution in interpretation is

warranted, however, as mild ADHD was allowed in the sample and Number/Letter Memory has

been shown to load on an attention factor (Burton et al., 1999). Nonetheless, presence of ADHD

was comparable between both groups, and Number/Letter was highly correlated with

phonological skill (WJ-Word Attack) in this sample. In contrast to phonological STM, the two

groups were quite comparable in semantic STM, visual-spatial STM, and LTM for both verbal

and visual material, with the dyslexia group scoring within the Average range on these measures.

Moreover, the dyslexia group performed worse on phonological STM (Number/Letter) than

semantic STM (Story Memory), whereas controls performed comparably on these two subtests.

Hence, the memory deficit in dyslexia appears to be specific to phonological STM, with the rest

of memory functioning being intact. This finding is consistent with the phonological core deficit

model of dyslexia (Liberman & Shankweiler, 1991; Rack et al., 1992; Swank, 1994; Wagner et

al., 1994).
 Memory Functioning in Dyslexia 13

Basic reading performance. Phonological STM was moderately to highly correlated with

word identification and decoding skill in the total sample, even when controlling Full-Scale IQ

and articulation rate. This finding is consistent with hypotheses and prior research (Hansen &

Bowey, 1994; Snowling, 1991; Wagner et al., 1994). In contrast, correlations between semantic

STM and basic reading measures were small. Therefore, semantic STM may play a limited role

in basic reading performance when older children are studied. However, future research is

indicated to determine whether semantic STM contributes substantially to reading

comprehension, as both tasks require semantic processing. Future research also is warranted on

semantic STM’s relation to basic reading skill in individuals with language impairment.

Study 2

Similar to the WRAML, limited research has been conducted on individuals with

dyslexia using the California Verbal Learning Test (CVLT). Only one published study was found

which used the CVLT-C to study children with dyslexia (Kramer et al., 1999), and no published

studies were found using the CVLT or CVLT-II in adults with dyslexia. This could be a serious

shortcoming in the literature given that the CVLT variants measure semantic coding, storage and

retrieval, along with intrusions and interference. Therefore, the CVLT-C has the potential to be

an excellent tool to decipher the nature of the semantic STM/LTM deficit in dyslexia if there is

one.

The findings on the CVLT-C by Kramer and colleagues (1999) suggest that children with

dyslexia have poor encoding of word lists but intact retention and retrieval over time, along with

intact interference and intrusion scores. Children with dyslexia learned fewer items in general,

and they learned the items more slowly. They also had different serial position effects than

controls, recalling fewer items from the middle of the list. According to the authors, these
 Memory Functioning in Dyslexia 14

findings are consistent with poor encoding due to faulty strategy use during rehearsal.

Nonetheless, given the literature reviewed in the general introduction, group differences were not

expected on the CVLT-C as this measure fosters semantic coding.

Methods

Participants. Eighteen children with dyslexia and 18 controls were tested with the

CVLT-C. These data were collected during an earlier study (Kibby, in press). In the dyslexia

group 61% of participants were male; in the control group 56% of participants were male.

Groups were equated on age, grade level, gender, SES, and WISC-III FSIQ, similar to Study 1.

Children with dyslexia and controls were recruited and defined according to the same

criteria and procedures used in Study 1, although children with a history of ADHD or suspected

ADHD were excluded from the study. Presence of ADHD was determined on the basis of

parental report and review of school records. The mean IQ/word identification discrepancy for

the dyslexia group was 21.94 (range: 15 - 44); the mean prorated Full-Scale IQ/WRAT-3

Reading discrepancy for the control group was -7.17 (range: -29 - +5).

The two groups were comparable in age, grade level, gender, SES, FSIQ, and PIQ. They

differed in VIQ [F(1,34)=9.77, p < .01], word identification [F(1,34)=55.61, p < .001], WJ-R

Word Attack [F(1,33)=41.34, p < .001], spelling [F(1,34)=21.08 p < .001], and arithmetic

[F(1,34)= 9.15, p < .01], with controls scoring higher. See Table 5 for descriptive data.

<Table 5 here>

Measures. The CVLT-C is a word list test that fosters semantic coding of material.

Similar to other memory tasks, it includes measures of STM and LTM. However, the CVLT-C

only measures verbal learning and memory. As the CVLT-C has been described in detail

elsewhere (Bigler & Adams, 2001; Delis et al., 1994), only a brief description of the test is
 Memory Functioning in Dyslexia 15

provided here. The child is presented with a list of 15 familiar words that can be grouped into 3

categories (List A). The test measures immediate recall of List A over 5 learning trials, free and

cued short-term recall of List A after a distracter list (List B), and free and cued long-term recall

of List A. After long-term cued recall of the list, recognition testing of List A is performed

(Discriminability Index). The recognition task includes the words from Lists A and B, as well as

other words that are semantically related to List A, phonetically similar to it, and unrelated to it.

The CVLT-C yields several scores. These include two measures of strategy use: semantic

clustering (spontaneously grouping the words by category) and serial clustering (recalling the

words in serial order). Semantic clustering is purported to be the more active learning strategy

and may be associated with better learning and retention (Delis et al., 1994). The CVLT-C

computerized scoring program was used to generate the scores. Articulation rate was assessed

with a task similar to that used in Study 1, modified from Hulme, Maughan, and Brown (1991).

When controlling VIQ, children with dyslexia and controls were comparable in articulation rate.

Results

Immediate memory, STM and LTM. WISC-III VIQ was used as a covariate due to group

differences on this measure. Given low power, analyses also were re-run without a covariate.

For immediate memory, total number of words recalled and rate of acquisition over the 5

learning trials were analyzed through repeated measures ANCOVA. Diagnosis was the between-

subject variable (dyslexia versus control), and Trial was the within-subject variable (raw scores

on Trials 1–5). Diagnosis [F(1,33)=1.89, p > .10] and the Diagnosis X Trial interaction [F(4,132)

< 1.0, p > .10] were not significant. When VIQ was not used as a covariate, Diagnosis was

significant [F(1,34)=6.16, p < .05], but the interaction was not. A repeated measures ANCOVA

was used to assess retention over time. The within-subject variable was Recall (Trial 5, Short-
 Memory Functioning in Dyslexia 16

delay, and Long-delay Free Recall raw scores). Diagnosis and the Diagnosis X Recall interaction

were not significant (Fs < 1.0, ps > .10). Moreover, groups did not differ in delayed recognition

when using ANCOVA, F(1,33) < 1.0, p > .10. Results for short- and long-delayed recall and

recognition did not change when using ANOVA. See Table 6 for CVLT-C descriptive data.

<Table 6 here>

Serial position effects were assessed using repeated measures ANCOVA. Position

(number of words immediately recalled from the primacy, middle, and recency portions of the

list) was the within-subject variable. Diagnosis was not significant [F(1,33)=1.72, p > .10], nor

was the Diagnosis X Position interaction [F(2,66) < 1.0, p > .10]. Results did not change when

using repeated measures ANOVA. Strategy use was assessed using the clustering scores from

List A Trials 1-5. Using ANCOVA, groups were highly comparable in semantic clustering

[F(1,33) < 1.0, p > .10] and serial clustering [F(1,33) < 1.0, p > .10]. Results did not differ when

ANOVA was used.

Basic reading performance. Using the total sample, partial correlations were conducted

between List A Trials 1-5, word identification, and Word Attack, controlling FSIQ and

articulation rate, following the procedure used in Study 1. Consistent with Study 1, partial

correlations between List A Trials 1-5 and the basic reading measures were small (r=.24 with

word identification and r=.25 with Word Attack, ps > .10). Pearson correlations between List A

Trials 1-5 and the basic reading measures were not significant (r=.30 with word identification

and r=.30 with Word Attack, ps > .05) when not controlling FSIQ.

Discussion

Memory performance. When VIQ was statistically controlled, performance on the

CVLT-C was highly comparable between the two groups as hypothesized. This was true for
 Memory Functioning in Dyslexia 17

immediate, short-term, and long-term memory, as well as semantic clustering. When VIQ was

not controlled, groups only differed in immediate recall. However, this difference in performance

likely was due to the high proportion of controls with above average VIQ (72% of controls

versus 11% of the dyslexia group, X2(1)=13.83, p < .001), as those with high VIQ performed

better than the rest of the sample on List A Trials 1-5, F(1, 34)=4.45, p < .05. Furthermore, the

mean T-score from List A Trials 1-5 was Average for the dyslexia group despite it being better

for the control group. In general, children with dyslexia scored in the Average range on all

CVLT-C measures when using z-scores, suggesting their semantic STM and LTM are intact.

Basic reading ability. Similar to Study 1, there were small correlations between

immediate semantic memory and measures of basic reading performance when FSIQ and

articulation rate were controlled, and there were non-significant correlations between immediate

semantic memory and basic reading when not controlling FSIQ. Hence, semantic STM may not

contribute substantially to word identification and decoding skill in older children, unlike

phonological STM. However, further research is needed on the relation between semantic STM

and reading comprehension and on individuals with language impairment, as noted in Study 1.

General Discussion

Taken together, results suggest the primary memory deficit in children with dyslexia is

poor phonological STM, with the rest of memory functioning being spared. More specifically, in

Study 1 all aspects of visual STM were intact in dyslexia at the group level despite some prior

research finding visual STM impairment in this population (Henry, 2001; Howes et al., 1999,

2003; Kaplan et al., 1998). Study 1’s findings of Average visual STM in dyslexia is consistent

with other work in this area, however (Kibby & Cohen, 2008; Kibby, Marks et al., 2004;

McDougall et al., 1994). LTM also appears to be spared in dyslexia, as LTM was comparable to
 Memory Functioning in Dyslexia 18

controls across the two studies regardless of whether material was verbal or visual in nature.

Similar results were found by Kibby and Cohen (2008). Given the limited research conducted on

LTM in dyslexia to date, this is an important contribution to the literature.

In terms of verbal STM, semantic STM was intact in both studies despite their using

different samples and measures. Intact semantic STM in dyslexia also has been found by Kibby

and Cohen (2008) and Lee and Obrzut (1994). In contrast, phonological STM was impaired in

dyslexia in Study 1, consistent with prior research (Kibby, in press; Kibby & Cohen, 2008; Rack

et al., 1992; Wagner et al., 1994). As it has been suggested that there are at least two verbal

short-term stores, one for phonetically coded material and another for semantically coded

material (Martin, Shelton, & Yaffee, 1994), the store which holds material coded phonetically

likely is affected in dyslexia whereas the store(s) which holds material coded semantically may

be intact. The phonological store may be located within/around the supramarginal gyrus (Kibby,

Kroese, et al., 2004; Jonides et al., 1998), and the posterior peryisylvian region is frequently

implicated in dyslexia (for a review see Kibby & Hynd, 2001). In contrast, semantic processing

is wide-spread throughout the brain, including both hemispheres (Kolb & Wishaw, 2003). Such

wide-spread networks may provide sparing of semantic coding/STM in dyslexia as other brain

regions may be able to help compensate for left posterior perisylvian dysfunction.

Limitations to this research with corresponding future directions are as follows. First,

sample sizes were small, and overall severity of dyslexia was mild for both studies. Hence, this

study should be replicated with a larger sample of children with more severe dyslexia to

determine whether deficits are still limited to phonological STM. Second, mild ADHD was

allowed in the first study, but the small number of participants with ADHD made further analysis

of its effects problematic. Furthermore, presence of ADHD was assessed through review of
 Memory Functioning in Dyslexia 19

school records and parent report for both studies. Consequently, future research on memory

functioning in dyslexia should formally assess for the presence and severity of ADHD.

Nonetheless, the memory deficits found in Studies 1 and 2 are consistent with dyslexia, as

ADHD tends to be associated with poor visual-spatial STM functioning (Kibby & Cohen, 2008)

and visual-spatial STM was intact in Study 1. Third, an abbreviated WISC-III and the WRAT-3

were used with controls to minimize testing time, but the full WISC-III and various achievement

batteries were used to assess children with dyslexia. Given the two groups had different IQ

measures, this may have affected analyses where IQ was controlled in some fashion (partial

correlations, ANCOVA). Thus, future studies should use the same IQ and achievement battery

for all participants. Nonetheless, the IQ screening version used with controls has a high

correlation with the full WISC, and results did not differ substantially when IQ was not

controlled. Fifth, this project utilized a large age range. Therefore, future research should utilize

a tighter age range. Sixth, working memory/central executive (CE) functioning was not assessed

in this study, as neither the WRAML nor the CVLT-C have measures of CE functioning. Lastly,

neither study had a measure of reading comprehension. As a result, future research on STM’s

relation to reading comprehension is necessary.

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 Memory Functioning in Dyslexia 25

Table 1

Participant Descriptive Data for the WRAML Study

Characteristic Dyslexia Controls

Mean (SD) Mean (SD)

Age 11.10 (1.38) 11.36 (1.48)

Grade 4.68 (1.25) 5.25 (1.52)

SES 2.47 (.84) 2.40 (.68)

FSIQ 102.70 (12.85) 104.50 (9.86)

VIQ 101.75 (12.80) 105.95 (12.94)

PIQ 104.30 (14.35) 103.30 (13.18)

Word identification*** 80.95 (11.67) 111.60 (11.91)

Spelling*** 86.82 (13.23) 105.35 (10.24)

Arithmetic 94.72 (13.17) 102.65 (14.13)

Articulation rate 1.86 (.42) 1.98 (.37)

WJ-R Word Attack*** 12.84 (4.99) 22.45 (4.01)

Note. Socioeconomic status (SES) was measured on a 5 point scale according to the

Hollingshead (1975) Four Factor Index of Social Status. Articulation rate was recorded in

number of words spoken per second. Word Attack was measured in raw scores.

***p < .001.

 Memory Functioning in Dyslexia 26

Table 2

Partial Correlations amongst the WRAML Verbal STM Measures Controlling Articulation Rate

Variables Verbal Learning Sentence Memory Sound Symbol Number/Letter Memory

Story Memory .51*** .26 .35* .19

Verbal Learning .29 .46** .18

Sentence Memory .38* .59***

Sound Symbol .32*

*p < .05, **p < .01, ***p < .001

 Memory Functioning in Dyslexia 27

Table 3

WRAML Performance by Group

Subtests by area Dyslexia Controls

Mean (SD) Mean (SD)

Verbal STM Scaled Scores

Story Memory 9.42 (3.15) 10.40 (3.49)

Verbal Learning 9.74 (2.62) 10.50 (3.09)

Sound Symbol 9.11 (2.75) 10.90 (3.11)

Sentence Memory* 8.42 (2.27) 10.80 (2.26)

Number/Letter Memory*** 6.84 (2.34) 9.70 (1.98)

Visual STM Scaled Scores

Picture Memory 10.37 (2.11) 10.95 (2.44)

Design Memory 10.21 (2.07) 9.90 (2.22)

Visual Learning 10.68 (2.77) 11.15 (3.20)

Finger Windows 9.16 (3.05) 9.05 (2.28)

LTM Savings Scoresa

Story Memory 3.94 (0.66) 3.95 (0.69)

Verbal Learning 3.53 (1.01) 3.85 (1.27)

Sound Symbol 3.76 (1.35) 3.85 (1.42)

Visual Learning 3.71 (0.92) 4.25 (0.97)

Story Recognition 3.94 (0.83) 4.10 (0.97)

aFor the LTM Savings measures, scores are normed as follows: 1 is Atypical; 2 is Borderline; 3

is Low Average; 4 is Average, and 5 is Bright Average.

*p < .05. ***p < .001.

 Memory Functioning in Dyslexia 28

Table 4

Partial Correlations between Phonological/Semantic STM and Basic Reading Performance

Controlling Full-Scale IQ and Articulation Rate

Variables Number/Letter Memory Word Recognition Word Attack

Story Memory .11 .09 .23

Number/Letter Memory .53*** .70***

Word Recognition .76***

***p < .001

 Memory Functioning in Dyslexia 29

Table 5

Participant Descriptive Data for the CVLT-C Study

Characteristic Dyslexia Controls

Mean (SD) Mean (SD)

Age 12.27 (1.11) 11.97 (1.87)

Grade 5.61 (1.14) 5.72 (1.99)

SES 2.33 (1.08) 2.11 (.68)

FSIQ 101.11 (8.55) 104.72 (9.95)

VIQ** 98.50 (9.94) 108.50 (9.24)

PIQ 104.67 (10.87) 101.61 (15.02)

Word identification*** 84.00 (8.75) 111.89 (13.23)

Spelling*** 84.61 (11.90) 105.44 (15.13)

Arithmetic** 91.50 (12.56) 105.67 (15.40)

WJ-R Word Attack*** 13.41 (5.27) 23.06 (3.47)

Articulation rate 2.27 (.51) 2.68 (.46)

Note. SES was measured on a 5 point scale using the Hollingshead (1975) Four Factor Index of

Social Status. Word Attack was measured in raw scores. Articulation rate was recorded in

number of words spoken per second.

**p < .01. ***p < .001.

 Memory Functioning in Dyslexia 30

Table 6

CVLT-C Performance by Group

Subtests by area Dyslexia Controls

Mean (SD) Mean (SD)

Immediate Memory

List A Total Trials 1 – 5 47.61 (13.08) 57.28 (9.48)

List A Trial 1 6.00 (1.53) 6.94 (1.43)

List A Trial 5 11.33 (2.89) 12.72 (1.84)

List A Semantic Clustering 1.55 (0.62) 1.57 (0.36)

List A Serial Clustering 2.35 (2.34) 2.26 (1.79)

List A Primacy Region 29.52 (6.73) 28.63 (3.89)

List A Middle Region 42.75 (6.20) 42.51 (3.13)

List A Recency Region 27.73 (6.66) 27.75 (8.05)

List B 5.89 (1.78) 6.67 (1.85)

STM

List A Short-Delay Free Recall 10.22 (3.49) 11.78 (2.53)

LTM

List A Long-Delay Free Recall 10.67 (3.18) 11.83 (1.86)

Discriminability Index 94.94 (6.89) 96.17 (4.69)

False Positives 0.89 (1.37) 0.94 (1.76)

Note. The Discriminability Index is in standard scores, and List A Total Trials 1-5 is a T-score.

The Clustering scores are measured in observed/expected, and the Region scores are in

percentages. The rest are raw scores. There are no group differences when controlling Verbal IQ.