phonological processes, confrontational naming, and immediate memory in dyslexia

http://ldx.sagepub.com/Journal of Learning Disabilities

http://ldx.sagepub.com/content/26/9/597The online version of this article can be found at:

DOI: 10.1177/002221949302600910

1993 26: 597J Learn DisabilPeggy T. Ackerman and Roscoe A. Dykman

Phonological Processes, Confrontational Naming, and Immediate Memory in Dyslexia

Published by:

Hammill Institute on Disabilities

and

http://www.sagepublications.com

can be found at:Journal of Learning DisabilitiesAdditional services and information for

http://ldx.sagepub.com/cgi/alertsEmail Alerts:

http://ldx.sagepub.com/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

http://ldx.sagepub.com/content/26/9/597.refs.htmlCitations:

What is This?

- Nov 1, 1993Version of Record >>

at RMIT UNIVERSITY on August 23, 2014ldx.sagepub.comDownloaded from at RMIT UNIVERSITY on August 23, 2014ldx.sagepub.comDownloaded from

http://ldx.sagepub.com/

http://ldx.sagepub.com/content/26/9/597

http://www.hammill-institute.org/

http://www.sagepublications.com

http://ldx.sagepub.com/cgi/alerts

http://ldx.sagepub.com/subscriptions

http://www.sagepub.com/journalsReprints.nav

http://www.sagepub.com/journalsPermissions.nav

http://ldx.sagepub.com/content/26/9/597.refs.html

http://ldx.sagepub.com/content/26/9/597.full.pdf

http://online.sagepub.com/site/sphelp/vorhelp.xhtml



RESEARCH

Phonological Processes, Confrontational Naming, and Immediate Memory in Dyslexia

Peggy T. Ackerman and Roscoe A. Dykman

A group of poor readers classified as dyslexic by age/IQ discrepancy criteria (n=42) were contrasted with two clinic control groups: 56 adequate-for-age readers with attention deficit disorder (ADD) and 21 poor-for-age readers not meeting the IQ discrepancy criterion (slow/borderline group). The children (33 girls, 86 boys) ranged in age from 7.5 years to 12 years. Variables chosen for study included simple and complex phonological processing, speech rate, continuous naming speed, running memory span, serial memory span, and mental addition. Evidence is presented that the two poor reader groups are distinguishable. Unlike the dyslexic group, the slow/borderline group did not differ from the ADD group on three key measures: simple auditory phonological sensitivity, continuous naming speed, and running memory span. Stepwise regression to predict word list reading level showed that once age and verbal IQ were removed (51% of variance), these three key measures accounted for an additional 22% of the variance (R = 0.86, R2 = 0.73). The single best predictor of word list reading level was nonsense word list reading level, which was explained by the same set of five variables that explained real word reading (R = 0.77, R2=.60). Severity of attentional problems was not linearly related to reading skill in this clinic sample.

A mong the deficits most consis-tently found in samples of chil-dren with specific/unexpected

reading disability (or developmental dyslexia) are phonological insensitivi-ty (see reviews by Goswami & Bryant, 1990; Pennington, 1991; Wagner & Torgesen, 1987); slow continuous nam-ing of randomized numbers or let-ters (Wolf, 1991); and poor short-term memory, especially for serial alpha-numeric stimuli (Torgesen, Rashotte, Greenstein, Houck, & Portes, 1987).

Baddeley (1986), theorizing from his impressive body of research on work-ing memory, suggested that all of these deficits may be rooted in one overarch-ing deficit, namely, a slow articulation rate. He convincingly demonstrated that memory span for verbal stimuli is robustly correlated with the rate at

which to-be-learned sound units can be articulated. For example, the aver-age memory span for one-syllable words is appreciably longer than for five-syllable words . This finding is ex-plained by the fact that one-syllable words can be enunciated at the rate of about 2.3 words per second, whereas five-syllable words can be said at only 1.3 words per second. Thus, encod-ing and rehearsal time is usurped by longer words . It follows that children who articulate rather slowly would then be at a disadvantage on serial memory tasks, such as the Digit Span subtest of the WISC-R. It also follows that because young children speak more slowly than older children, they should have shorter memory spans. Indeed, speech rate and memory span form a linear function for the child-

hood years (Case, Kurland, & Gold-berg, 1982; Hulme, Thomson, Muir, & Laurence, 1984).

Of additional interest, Spring and Perry (1986) reported that rate of count-ing from memory in same-age chil-dren correlates with naming rate, mem-ory span, and reading skill. Torgesen et al. (1987) likewise found a signifi-cant correlation between digit nam-ing speed and digit span in a group of children with learning disabilities. These investigators suggested that chil-dren with reading disabilities cannot maintain phonetically coded material in working memory long enough to achieve blending. But, it is puzzling that this limitation would impede the learning of the short, mostly one-syllable words that predominate in beginning reading textbooks.

Indeed, a large body of evidence shows that most children with reading disabilities exhibit impaired phonolog-ical sensitivity even in tasks that do not overly tax working memory (Olson, Wise, Conners , Rack, & Fulker, 1989; Stanovich, 1986; Vellutino, 1987; Wag-ner & Torgesen, 1987). In short, these children seem less able to appreciate that articulated sounds fall into pat-terns. They falter in recognizing and generating rhymes and alliterations (Ackerman, Anhalt, & Dykman, 1986; Ackerman, Anhalt, Dykman, & Hol-comb, 1986; Ackerman, Dykman, & Gardner , 1990a, 1990b; Bradley & Bryant, 1983). Also, younger children

JOURNAL OF LEARNING DISABILITIES VOLUME 26, NUMBER 9, NOVEMBER 1993

PAGES 597-609

at RMIT UNIVERSITY on August 23, 2014ldx.sagepub.comDownloaded from


598

who are poor readers are impaired when asked to segment words into sounds—even into syllables, but espe-cially into phonemes (Liberman & Shankweiler, 1985).

Another bit of evidence linking im-paired phonological sensitivity to poor reading comes from so-called phono-logical interference tasks. Here the focus is not on absolute memory span but on the difference between the spans for rhyming and nonrhyming strings of letters, words, or syllables. Rhyme creates interference. Thus, whereas young children who are nor-mal readers have a longer memory span for nonrhyming than rhyming letters or words, those who are poor readers tend to perform at the same level on both types of stimuli (Liber-man & Shankweiler, 1985; Mann, 1986). But the task that stymies chil-dren and adults with reading disorders the most is the decoding of pronounce-able, short nonsense words (Acker-man, Dykman, Holloway, Paal, & Gocio, 1991; Olson et al., 1989; Richardson, 1985). This task would ap-pear to require phonological knowl-edge foremost, but also adequate working memory.

We designed an experiment to in-clude measures of articulation rate, continuous naming, immediate mem-ory, and simple and complex phono-logical processing. Additionally, we included a rapid addition-verification task, which served to evaluate both working memory and automatization of learning in a nonphonetic area. We wished to test Baddeley's (1986) hy-pothesis linking slow articulation to poor reading, while at the same time looking at other possible factors. We theorized from pilot work (Ackerman et al., 1990b) that articulation rate, con-tinuous naming rate, simple phonolog-ical sensitivity, and memory span would each contribute independent variance in the prediction of degree of reading impairment relative to age/IQ expectancy. But, we wished to con-sider less-well-researched factors that could explain additional variance. For example, Baddeley's research shows

JOURNAL OF LEARNING DISABILITIES

that working memory for verbal stim-uli is disrupted by extraneous noise. Because a classroom is rarely free of extraneous noise, and because children with ADD and reading disabilities are frequently considered by their teach-ers to be easily distracted (Dykman, Ackerman, & Holcomb, 1985), we hy-pothesized that both of these groups would be very sensitive to extraneous noise (foreign speech) when engaged in tasks that tax working memory. Fur-thermore, we considered evidence that ADD is associated with inconsistent performance and that some children with reading disabilities are passive vis-a-vis learning (Ackerman, Elardo, & Dykman, 1979), and we concluded that contingent monetary reward might enhance both groups' perfor-mance on memory tasks. Accordingly, distraction, reward, and reward plus distraction conditions were included, in part to extend Baddeley's findings to children, but also to search for other factors that contribute to impaired reading. Further extending Baddeley's working memory model to children with reading disabilities, we explored the possibility that these children might have relatively more difficulty with word strings than with letter and digit strings, particularly with disyl-labic and trisyllabic words. We also explored the possibility that these chil-dren might have relatively more im-paired visual than auditory digit spans.

Most studies of serial short-term memory in children with reading dis-orders have pointed to a rehearsal fail-ure (e.g., Cohen & Netley, 1981; Tor-gesen et al., 1987), which according to theorists (see Ellis, 1970) is manifested in impaired recall of primacy as op-posed to recency items. That is, items early in the list are forgotten, or inter-fered with, as new items are presented, but the most recently presented items remain available from the working memory store. In an earlier study, we (Ackerman et al., 1986b) found evi-dence of both a primacy and a recency deficit in children with reading dis-abilities who were presented supra-span lists. Cohen and Netley (1981) de-

signed a serial running memory task that specifically tests the recency effect; that is, rehearsal is precluded by pre-senting long and variable length lists at rapid rates and requiring the subject to recall the last three items. They found two reading disabled groups to be inferior to age-matched nondisabled reading groups on this task. They con-cluded that impaired readers are less able to encode serial items in the form of phonological patterns into an al-ready loaded short-term memory store. We elected to use their task and study the relation of this type of "auto-matic" memory measure to speech rates, naming, and other effortful memory measures.

The model we had in mind sees spe-cific reading disabilities (or dyslexia) as being caused by (or associated with) several factors, some more powerful than others. We also regard degree of retardation to be quite variable. Thus, a logical way of taking into account the effect of several dimensional variables is via multiple regression analysis. Then it becomes clear, to give a sim-ple example, that three children can be equally retarded in reading but for different reasons: One might be se-verely phonologically insensitive, say, but within normal range on other fac-tors, another might be laboriously slow at naming but have an adequate mem-ory and an understanding of phonetic patterns, and the third might be mod-erately impaired in all areas.

With the multiple causation model in mind, we recruited two clinic control groups to contrast with a group of chil-dren classified as specifically reading disabled, or dyslexic. The larger group were children with attention deficit disorder who, nonetheless, were ade-quate-for-age readers. We know from previous studies that ADD and dys-lexia are frequently overlapping dis-orders but that half or more children with ADD, at least in clinic settings, have age-appropriate reading and spelling scores (Dykman & Ackerman, 1991a; Dykman et al., 1985). We also know that the majority of children with dyslexia seen in clinical samples exhibit



VOLUME 26, NUMBER 9, NOVEMBER 1993 599

some ADD symptomatology (Dykman et al., 1985), but severity of symptoms is not linearly related to degree of read-ing impairment. Still, some deficits re-ported for dyslexic groups could be attributable to attentional problems, and hence the logic of having a nor-mally reading ADD clinic control group.

The other group of control children were poor readers for age but not for IQ level. It is still being debated whether these "garden-variety" poor readers, as Stanovich (1988) labeled them, differ in critical ways from chil-dren classified as dyslexic. The classic Isle of Wight study (Yule & Rutter, 1976) did reveal different profiles and different outcomes for what the inves-tigators termed specifically retarded and backward readers. The latter (low-IQ poor readers) actually had better read-ing scores at adolescent follow-up than the former, whose childhood reading scores were significantly lower than predicted from their IQ scores.

From both a practical and a theoreti-cal research standpoint, it is desirable to know whether all poor readers have the same profile of underlying deficits. Unfortunately, we were unable to re-cruit as many garden-variety as dys-lexic poor readers, but the sample size is adequate to suggest areas of dif-ference.

The dyslexia literature, Baddeley's (1986) corpus of studies on working memory, and our own research sug-gested several major hypotheses to be tested:

1. The dyslexic group relative to the ADD control group will have slower speech and continuous naming rates, which will limit their memory spans and account, in part, for the discrepancy between actual and ex-pected (for age and IQ) reading level.

2. The dyslexic group will also exhibit deficits in simple as well as more complex phonological processing.

3. The poor reader group not classified as dyslexic will not differ from the ADD groups on continuous naming

or simple phonological processes. Their arithmetic scores will be in line with their reading and spelling scores, whereas the dyslexic group will have relatively higher arith-metic scores.

4. The ADD group will be more af-fected by condition manipulations (reward, distraction) than the dys-lexic group, indicating that they have performance deficits rather than processing deficits.

5. Scores on attentional and behav-ioral indices (obtained for all sub-jects) will not contribute unique variance in the prediction of read-ing skill.

Method

Subjects

Subjects were 119 children, aged 7.5 years to 12 years, who were referred to our Child Study Center or Develop-mental Center for psychoeducational evaluation. Those admitted to the study met clinical criteria for DSM-III or III-R (American Psychiatric Associ-ation, 1980, 1987, respectively) diag-noses of developmental reading dis-order, attention deficit-hyperactivity disorder, or both. The sample included 33 girls and 86 boys; 11 were black, 108 were white. Inclusion criteria included normal intelligence (IQ>80), normal hearing and vision, normal physical health, a history of regular attendance at accredited schools, and English as the only language. Children with known neurological conditions or who were in need of psychiatric interven-tion were excluded. Children taking psychostimulants were admitted pro-vided they could go on drug holiday for the study.

Subjects were assigned to three groups: dyslexic, slow learner /border-line, or attention deficit disorder (ADD) only. These groups were formed using cut-scores on the revised Wechsler In-telligence Scale for Children (WISC-R) (Wechsler, 1974) and the revised Wide

Range Achievement Test (WRAT-R) (Jastak & Jastak, 1984). The children with ADD only (n = 56) were average or better readers and spellers (mean of standard scores on the WRAT-R Read-ing and Spelling subtests >90). The other two groups were below-average readers (mean reading/spelling stan-dard scores <90). Poor readers desig-nated as having developmental dys-lexia (n=42) had Full Scale IQs at least 17 points higher than their reading/ spelling average. The term dyslexia is used synonymously with the term spe-cific reading disabilities to designate an unexpected failure in literacy acquisi-tion (single-word decoding and spell-ing); it does not imply a specific etiol-ogy. Poor readers classified as slow learner/borderline (n = 21) had less than a 17-point difference on these measures. These children are intended to represent the garden-variety poor reader. The mean discrepancy between the reading/spelling index and IQ was 28.4 points for the dyslexic group, 8.1 points for the slow group, and 5.0 points for the ADD-only group, F(2,116) = 77.87, p<.001. Two of the slow learner group and 14 of the ADD group had reading/spelling means greater than their IQs. Eight subjects with ADD had IQs > 17 points above their reading/spelling scores, but they were not disabled-for-age readers.

The assignment to groups is obvi-ously somewhat arbitrary but is defen-sible from the standpoint of educa-tional policy as well as the regression of achievement scores on IQ (see Dyk-man & Ackerman, 1992, for an elabo-ration of the latter point). A 17-point spread corresponds to a 2-standard error (SE) difference between predicted (from Full Scale IQ) and observed read-ing standard scores for an unselected large sample (see Shaywitz, Shaywitz, Fletcher, & Escobar, 1990). Thus, the mean spread for the slow learner group is about 1 SE while that for the dyslexic group is over 3.5 SEs. Read-ing and spelling scores from the WRAT-R are so robustly correlated (0.94 for 10-year-olds) that it is sensible to combine them for a more reliable



600

measure of literacy acquisition. More-over, studies of so-called "compen-sated" adults with dyslexia indicate that impaired spelling remains as a hall-mark (Felton, Naylor, & Wood, 1990).

The research plan called for equal numbers of subjects in the two poor reader groups, but this goal could not be reached from our referral base. A school referral base would probably yield as many slow as dyslexic readers, especially if the IQ cut were lowered to 70 or 75.

Note that children in the two poor reader groups could also have a clini-cal diagnosis of ADD or ADHD, in-cluding ADD without hyperactivity (ADD/WO). Indeed, the three groups had highly similar mean scores on in-dices of attention and hyperactivity (see below). This finding was expected and replicates previous studies (Dyk-man & Ackerman, 1991a; Dykman et al., 1985). That is, ADD and specific or developmental learning disorders are separate but often overlapping diagnoses.

Preliminary Work-up

Subjects came to our Child Study Center to be administered a battery of reading tests. If their WISC-R and/or WRAT-R scores were not current (done within the year), these tests were also given. The reading battery included the Word Identification subtest of the revised Woodcock Reading Mastery Test (Woodcock, 1992), a word list similar to that from the WRAT-R; the revised Gray Oral Reading Tests (GORT-R) (Wiederholt & Bryant, 1986), which entails reading graded paragraphs and answering compre-hension questions; and Part II of the Decoding Skills Test (DST) (Richard-son & DiBenedetto, 1985), which re-quires reading lists of real and non-sense words, each nonsense word being a pronounceable rhyme match for a paired real word. The real words (30 monosyllabic and 30 polysyllabic) are presented first, followed by the nonsense list. The Woodcock word list and the GORT-R yield standard scores,


whereas the DST is considered to be a diagnostic tool.

Parents or guardians (usually the mothers) completed the Child Behav-ior Checklist (Achenbach & Edelbrock, 1983), which yields two broad band factors (Internalizing and Externaliz-ing) as well as several narrow band scores. They were also asked the atten-tion deficit disorder questions from the Diagnostic Interview for Children-Parent Version (Herjanic & Reich, 1982) to validate the clinical diagnosis of ADD.

Teachers were asked to complete our expanded Conners (1973) question-naire, which includes 10 attention items listed in DSM-III as well as the 10-item abbreviated hyperkinesis in-dex. The questionnaire also includes the items constituting the Iowa over-active/inattention and aggression fac-tors (five items each); see Loney and Milich (1982). Each item is rated on a scale of 0 to 3 {absent to very much a problem).

Laboratory Tests

Children arrived at our laboratory at 8:45 a.m. A research assistant toured the area with them and then began ad-ministration of a battery of brief tests hypothesized to tap dysfunctional pro-cesses underlying or associated with poor reading. These tests were admin-istered to all subjects in the same or-der; however, conditions within some tests were randomized, as will be noted. It is our policy to administer first those tests that are apt to be less frustrating. The order of testing was as follows:

1. Articulation Rates. The purpose of these tasks was to determine how rapidly the children could articulate automatized information to be used in subsequent memory span tasks (digits, letters, words). Fifteen-second, digi-tally timed speech samples were tape-recorded in each condition. The experi-menter demonstrated and asked the children to practice first, to get them over possible anxiety or silliness. The

children were cautioned to articulate at a normal rate and not slur over sounds. Trials done improperly were repeated. Pilot work showed that children do not increase their speed with practice.

First, the children were asked to count from 1 to 10 as many times as possible until stopped. Next they were asked to say ABCD over and over. Then they were asked to say the word cat over and over, then teacher, and finally Arkansas (i.e., one-, two-, and three-syllable, highly familiar words). The tape-recorded samples were later replayed and scored.

2. Confrontational Naming. These rapid automatized naming (RAN) tests were modeled on those developed and studied by Denckla and Rudel (1976) and Wolf (1991). The children were asked to name 50 stimuli per card as rapidly as possible. There were 5 col-umns and 10 rows of stimuli on each 5-inch x 7-inch card. The stimuli on one card were the digits 1 through 9, those on a second card were capital-ized consonant letters, and those on a third card had alternating numbers and letters. Every other subject was given the numbers card first, while the remainder had letters first. All had the alternating stimuli last. A digital stop-watch was used to time each trial.

The Boston Naming Test (BNT) (Kap-lan, Goodglass, Weintraub, & Segal, 1983) was given to measure the chil-dren's ability to recognize pictures of common objects and reprieve the cor-rect label from memory. The test book-let contains 60 line drawings, which are presented in order for naming (15-sec. limit per card). Testing con-tinued until the child missed six of eight consecutive items. Following Wolf and Goodglass (1986), we next administered a multiple choice test over the items that were missed. Along with the correct choice, there was a semantic, a phonological, and a visual foil (e.g., the foils for comb were brush, code, and rake, respectively).

3. Auditory Phonological Sensitiv-ity. Bradley's (1984) oddity test is a




measure of the child's ability to detect the one word among four that does not sound like the others. There are two rhyme conditions and one alliteration condition, each having two practice trials followed by eight test trials. All stimuli are one-syllable, highly familiar words. The examiner can repeat the four-word lists if the child requests. In Condition 1, the odd word has a differ-ent last sound (e.g., hat, mat, fan, cat). In Condition 2, the odd word has a different middle sound (mop, hop, tap, lop). In Condition 3, the odd word has a different onset sound (rot, rod, rock, box). This task discriminated ADD-only and dyslexic groups in a previous study (Ackerman et al., 1990a) and ac-counted for independent variance in the prediction of severity of word-list reading deficits in a dyslexic group (Ackerman et al., 1990b).

4. Serial Memory Tests. These tests assessed the children's ability to recite back series of digits, letters, and words in exact order. All tests were given under standard and reward con-ditions. Additionally, the auditory and visual digit span tests were given under two auditory distraction con-ditions (one without and one with reward).

Odd-numbered subjects received the auditory digit span before the visual digit span tests, while even-numbered subjects had the opposite order. The letter span test was given next and the word span test last (auditory condition only for these tests).

The auditory digit span task was modeled on the WISC-R subtest. Digits were presented at a rate of one per sec-ond. In the two nonreward conditions (standard and distraction), the subjects had one additional trial at each series length if they erred on the first trial. In the reward conditions (standard and distraction), the children had up to four trials at each series length. The distraction was tape-recorded contin-uous foreign speech (Russian) played at 75 dB during the presentation and recall phases. In the reward conditions, the children were offered 25 cents for

each unit increase in their memory spans. They warmed up with series one less than their standard span and then proceeded as high as they could. The conditions were given in the fol-lowing order: standard, standard with distraction, reward, and reward with distraction.

The visual digit span task was an analog of the auditory digit span task. Digit strings were presented at 2-sec-ond intervals on a computer screen in the same four conditions.

Next, the children were aurally pre-sented strings of similar- and dissim-ilar-sounding consonants. The odd-numbered subjects received similar letters first, and even-numbered sub-jects received dissimilar ones first. The similar letters rhymed (B,C,D,G,P,T, V,Z) and the dissimilar ones did not (F,H,J,L,M,N,Q,R,S,X). For each letter type, there was a standard and reward condition (no distraction). Rate of presentation was one per second. This task was included primarily to test for the phonological interference effect (see introduction).

Finally, the children were presented strings of words under standard and reward conditions. In the first phase, the words were one syllable in length; in the next phase, they were disyllab-ic; in the third phase, the words were trisyllabic. Examples of the stimuli are pig, truck, door, rug; window, cookie, air-plane, garage; telephone, Kentucky, spa-ghetti, gardener. The examiner enunci-ated the words distinctly, leaving as much pause time as when presenting digits or letters. Of necessity, the pre-sentation rate was somewhat more than one per second for longer words.

5. Serial Running Memory (Re-cency). As discussed in the intro-duction, this task was modeled on one developed by Cohen and Netley (1981). Six test lists of auditory digits were tape-recorded and played at three presentation rates: 2.3, 4.8, and 7.3 digits per second. Two lists were pre-pared for each speed, a shorter one having 16 to 18 digits and a longer one with 22 to 24 digits. The children were

asked to repeat the last three digits heard in each list. The variable length was to prevent the children from antic-ipating when the lists would end. They were told they would receive 5 cents for each correct digit recalled. Three warm-up trials at the slow speed pre-ceded the test trials.

After completion of the memory tasks, the children were given a rest break and then escorted across the hall to our electrophysiology laboratory for the remaining procedures. Because EEG recordings were to be obtained, the children were seated in a comfort-able chair and had scalp electrodes attached prior to the tasks. The EEG procedures and findings will be re-ported in a separate article.

6. Computerized Arithmetic Task. This verification task was modeled on a paradigm used by Ashcraft and Fier-man (1982) to study automaticity of mental arithmetic in elementary school children. To tap working memory as well as automatization, we presented three digits serially, followed by an answer that was either correct or off by one digit. On the video screen, the children saw the following sequence: green square (warning) for 500 msec; Digit 1 (e.g., 6) for 1,000 msec; blank screen for 500 msec; Digit 2 (e.g., +3) for 1,000 msec; blank screen for 500 msec; Digit 3 (e.g., +4) for 1,000 msec; blank screen for 500 msec; answer ac-companied by question mark (12?). The answer and question mark re-mained on the screen for 5 seconds, followed by a blank screen for 5 sec-onds. Then the sequence started over. The children used their dominant hand to press either a "right" or "wrong" button mounted on a board strapped to the appropriate (dominant-hand) chair arm. The buttons were la-beled "R" and "W" and colored green and red, respectively, with the R (right) button to the right and the W (wrong) button to the left.

The children were told they would win 50 cents for playing, and this re-ward was given after 20 trials. Then they were told they would play again,



602 JOURNAL OF LEARNING DISABILITIES

with another 50 cents promised plus the chance to win 5 cents for each cor-rect answer. In this second block of trials, there was feedback on the screen after each response (either "You won 5 cents" or "Wrong" or "Late"), each message displayed for 1 sec. Late re-sponses were those that occurred after the answer faded or when no response occurred. Both accuracy and latency were recorded for each trial.

7. Computerized Rhyme Decision Task. The rhyme decision task was composed of 200 trials of paired stim-uli, presented on a video screen in a predetermined random order. The children first saw a one-syllable regular word (e.g., cat) and then an ortho-graphically similar word or nonsense word (e.g., hat, dat, car, cam). Their task was to press the "right" reaction time button (labeled R) if the second stimulus rhymed with the first or the "wrong" button (labeled W) if the sec-ond stimulus did not rhyme with the first. These buttons were the same as used on the arithmetic task. Practice trials preceded the test proper.

A 900-msec warning signal (a green square) followed by a 150-sec blank screen preceded the comparison word (SI). This word remained on the screen for 1,000 msec. The second stimulus (S2), also displayed for 1,000 msec, fol-lowed 1,000 msec after the onset of SI. The child had up to 3 seconds to re-spond. A 3.5-sec blank period followed before the next warning signal.

Short rest breaks occurred every 50 trials and the children were told they had won another 25 cents at each break. Both accuracy and latency were recorded, but there was no feedback.

Following this procedure, the elec-trodes were removed, and the children were given a voucher for the total rewards earned and then returned to their caregivers.

Data Analyses

Standard mixed-design ANOVAs and ANCOVAs from the BMDP pack-age were employed in comparisons of

the three groups. The alpha level was set at .05. For tests and procedures with repeated measures, omnibus anal-yses were done first, followed by uni-variate analyses where main effects reached significance. In pairwise con-trasts, the Bonferroni correction was used to assess significance.

Stepwise regression was used to assess the variance in reading scores accounted for by the dependent vari-ables.

Results

Analyses of Variance and Covariance

Table 1 shows mean scores for the three contrast groups on selection and descriptive measures. The table also gives sex ratios and mean occupational ratings of parents/guardians. Occupa-tions of both parents were coded on a scale of 1 to 7, following the guidelines of Hollingsworth (1957). If both par-

ents were employed, the score of the more prestigious job was entered. A rank of 1 went to major professionals and a rank of 7 to unskilled laborers. The majority of parents were from Cat-egories 3 and 4, encompassing small business owners, minor professionals, managers, clerical and sales workers, and technicians. The groups were not significantly different on this estimate of socioeconomic status (grand mean = 3.65 ± 1.38). Also, the groups did not differ in gender count.

As would be expected, the slow learner/borderline (SLO) group was lowest on cognitive measures and in-termediate on achievement measures (see Table 1). The significant age differ-ence was not anticipated, but both age and Full Scale IQ were used as covari-ates in subsequent group contrasts. Note that Woodcock word list stan-dard scores tended to be higher than WRAT-R scores for the two poor read-er groups, while GORT-R scores were more in line with the WRAT-R. Note also that the dyslexic (DYS) group had

TABLE 1 Descriptive and Selection Measures:

Group Means and Standard Deviations (in parentheses)

Measure

Age (mos.) Sex ratio (M:F) WISC-R

Verbal IQ Performance IQ Full Scale IQ

WRAT-R Reading SS Spelling SS Arithmetic

GORT-R Text SS Comprehension SS

Woodcock Reading SS

ADD-only (n =

120.6

56)

(14.2) 40:16

106.1 106.1 106.6

103.3 100.0 99.2

10.0 10.1

100.6

(10.7) (12.6) (10.8)

(9.7) (9.0)

(11.2)

(2.3) (2.6)

(8.1)

Slow (i? =

118.2

21)

(14.5) 12:9

90.3 94.2 91.1

83.3 82.8 85.9

8.0 8.7

88.2

(9.0) (10.9) (6.7)

(6.7) (7.0) (7.8)

(2.7) (2.2)

(11.5)

Dyslexia (n =

111.0

= 42)

(11.7) 34:8

98.5 109.0 103.5

74.3 75.9 91.8

5.8 7.7

82.1

(12.8) (11.6) (10.5)

(11.0) (8.4)

(14.0)

(3-2) (2.8)

(12.1)

F(2,116)

6.24*

16.16* 11.04* 18.35*

111.45* 102.77*

11.05*

28.61* 10.14*

35.75*

Pairs8

1>3

1 > 3 > 2 1 = 3 > 2 1 = 3 > 2

1 > 2 > 3 1 > 2 > 3 1>2 = 3

1 > 2 > 3 1>3

1 > 2 > 3

Note. ADD = attention deficit disorder; WISC-R = Wechsler Intelligence Scale for Children-Revised; WRAT-R = Wide Range Achievement Test-Revised; GORT-R = Gray Oral Reading Tests-Revised; SS = standard score. Significant pairwise contrasts of groups; 1 = ADD-only, 2 = Slow, 3 = Dyslexia. *p<.01.




TABLE 2 Behavioral Ratings: Group Means

and Standard Deviations (in parentheses)

Measure

CBCL IN T Score

EX T Score

Teacher Abr. Hyper Index

ADD Index

Iowa I/O

Iowa Agg.

ADD-only

63.7 (8.5) 67.0 (8.2)

16.2 (7.0)

20.0 (6.5) 9.4

(3.7) 4.0

(4.6)

Slow

61.5 (7.8) 65.7 (8.8)

15.6 (7.4) 16.8 (5.9) 8.0

(4.5) 2.8

(3.9)

Dyslexia

61.1 (12.0) 63.0

(12.2)

16.5 (7.6) 18.0 (7.2) 8.6

(4.5) 3.2

(4.5)

F value

0.95

1.88

0.13

2.11

1.00

0.72

Note. ADD = attention deficit disorder; CBCL = externalizing; I/O = inattentive/overactive; Agg.

Child Behavior Checklist; IN = internalizing; EX = Aggression.

TABLE 3 Speech Production Per 15 Seconds:

Group Means, Actual and Adjusted (in parentheses)

Measure

Serial 1 to 10 (no. digits)

ABCD (no.

Omnia

Discreet CAT

Teacher

Arkansas

Omnib

letters)

ADD-only

70.0 (68.6) 60.8

(59.7) 65.4

49.4 (48.5) 28.1

(26.3) 21.5

(21.1) 33.0

Groups

Slow

67.1 (68.5) 60.4

(61.6) 63.8

41.3 (42.3) 25.6

(26.0) 19.9

(19.9) 28.9

Dyslexia

64.2 (65.4) 54.6

(55.4) 59.4

43.5 (44.1) 25.8

(26.3) 19.0

(19.4) 29.4

COVF

Age IQ

11.57**

8.99**

17.58**

1.92

7.28**

15.11**

5.81*

1.79

2.85

3.71

0.55

0.87

0.15

0.77

Group

F

1.14

5.69**

4.33*

1.65

1.15

3.69*

2.42

Sample

SD

11.0

8.1

13.7

4.8

3.2

Note. ADD = attention deficit disorder; COV = covariates. aNumbers>letters. bCAT>teacher>Arkansas. *p<.05. **p<.01.

considerably higher arithmetic than reading/spelling scores, while the other groups did not. On another cog-nitive measure, the Boston Naming Test, the ADD group had significantly higher scores (mean = 41.1 ± 6.2) than both of the poor reader groups (DYS mean = 37.0 ± 5.6; SLO mean = 36.9 ± 6.1). However, this test did not differentiate the groups when age and IQ entered as covariates. Also, the multiple choice cues abetted the groups equally—an average gain of 10.1.

Table 2 presents behavioral ratings for the three groups. No significant group effects were found.

Table 3 presents speech production data. The omnibus analysis yielded a group difference in serial naming from memory, but the univariate tests were significant only for the ABCD task. Pairwise contrasts, again with age and IQ as covariates, showed both the SLO and ADD groups to be significantly faster than the DYS group in repeat-ing the letters over and over. The om-nibus test for repeated articulation of words did not show a significant group effect; however, a planned comparison showed the ADD group to be signifi-cantly faster at repeating the trisyllabic words than the DYS group.

Table 4 gives the findings from the rapid automatized naming test and phonological processing tasks. As ex-pected from the literature review, these tasks yielded the largest be-tween-group effects. In pairwise con-trasts, with age and IQ as covariates, the ADD group was significantly faster at continuous naming (all three condi-tions separately) than the DYS group. But the SLO group did not differ from either of the other groups.

On the Bradley (1984) oddity task, both the SLO and the ADD groups were more accurate than the DYS group at identifying the odd word (true in all three conditions). As noted above, the child can ask to have the word strings repeated on the Brad-ley task, and 74% requested one or more repeats. The groups did not differ significantly on repeats, how-

ever (SLO mean = 2.4 ± 2.0; DYS mean = 1.9 ± 2.5, and ADD mean = 1.6 ± 1.3).

On the Decoding Skills Test, the ac-curacy in reading real words (mono-and polysyllabic) was as expected: ADD > SLO > DYS. The same sig-nificant ordering held for the nonsense words.

Table 5 presents data from the serial memory span tasks. Only the auditory letters condition yielded a group differ-ence. Follow-up pairwise contrasts showed that for similar sounding (rhyming) letters, both the SLO and ADD groups had longer spans than the DYS group. The omnibus test did not yield a group by stimulus type in-




TABLE 4 Continuous Naming and Phonological Processing:


Measure

Naming (log sees) Numbers

Letters

Alternating

Omnia

Oddity (errors) Rhyme 1

Rhyme 2

Alliteration

Omnia (sum)

Decoding (correct) Mono real

Poly real

Mono nonsense

Poly nonsense

Omnib,c (sum)

ADD-only

1.37 (1.39) 1.46

(1.47) 1.52

(1.55) 1.45

(1.47)

.44 (0.62) 1.15

(1.37) 1.27

(1.58) 2.86

(3.57)

27.5 (25.3) 27.2 (25.8) 23.2

(22.0) 21.0

(19.7) 98.9

(93.8)

Group

Slow

1.41 (1.40) 1.49

(1.48) 1.54

(1.51) 1.48

(1.47)

1.38 (0.98) 2.29

(1.78) 2.05

(1.53) 5.72

(4.29)

20.6 (21.7) 19.2

(20.1) 12.9

(14.2) 7.0

(7.9) 59.7

(63.9)

Dyslexia

1.51 (1.49) 1.56

(1.55) 1.66

(1.64) 1.58

(1.56)

1.79 (1.74) 3.10

(3.05) 2.64 (2.50) 7.53

(7.29)

13.2 (14.2) 11.5

(12.9) 7.6

(8.5) 4.0

(5.2) 36.3

(40.8)

COVF

Age

22.44**

17.30**

18.36**

24.18**

2.20

1.50

7.33**

6.64*

18.61**

37.63**

16.27**

31.70**

34.66**

IQ

0.93

2.16

5.57*

3.82*

8.99**

8.99**

8.94**

17.49**

3.79*

3.53

4.99*

3.16

3.36

Group

F

10.83**

8.06**

8.86**

12.74**

8.43**

12.66**

3.93*

15.01**

41.27**

50.57**

48.24**

67.95**

64.09**

Sample

SD

0.11

0.10

0.13

1.44

1.84

1.85

9.6

10.1

9.7

10.1

Note. ADD = attention deficit disorder; COV = covariates. Condition effect. bWord type effect. cWord length effect. *p<05. * *p<01.

teraction. That is, all three groups showed the phonological interference effect. Both the incentive and distrac-tion conditions produced significant effects, but there were no significant interactions with group.

The running memory task, which taps the recency effect, yielded an overall group difference, F(2,112) = 3.89, p<.05, and two within-group effects: The children were more accu-rate at the two slower speeds than the fastest speed, F(2,228)=5.72, p<M, and they were more accurate with shorter than longer lists, F(l,114) = 28.90, p< .001. There was also a signifi-cant speed x list length interaction, F(2,228) = 9.19, p<.01, reflecting the

fact that the fast speed had a more detrimental effect on long than short list performance. However, there were no group by condition effects. Sum-ming over all conditions, both the ADD and SLO groups were signifi-cantly more accurate than the DYS group.

Table 6 presents data from the two longer computer-generated labora-tory tasks. Looking first at the arith-metic verification task, there was no group effect once age and IQ were co-varied out. The condition effect was robust both for accuracy, F(l,114) = 65.92, p<.00\, and logged latencies, F(l, 114) = 84.53, p< .001. The group x condition interaction approached sig-

nificance for accuracy, F(2,114) = 2.58, p< .10, reflecting variation in degree of improvement with reward (DYS and SLO > ADD).

The rhyme detection task, as expect-ed, yielded accuracy differences be-tween groups, F(2,104) = 8.73, p<.001. Overall, and contrary to expectation, nonsense-word rhymes were discrim-inated as accurately as real-word rhymes. But, on the real-word trials, valid rhymes were detected more ac-curately than invalid rhymes, which did not hold for the nonsense-word trials (for the stimulus type x validity interaction, F[l,106] = 19.59, p<.001). Both age and IQ were significant covar-iates in the analysis.

Pairwise contrasts, controlling for age and IQ, showed the ADD group to be more accurate than the DYS group for both stimulus types (real and nonsense, p< .001), but ADD subjects were no more accurate than SLO sub-jects. The SLO group was marginally more accurate than the DYS group on real-word rhymes, F(l,53)=3.60, p<A0.

Latencies in the rhyme detection task did not discriminate groups.

Stepwise Regression Analyses

Stepwise regression was used to pre-dict the actual number of words cor-rectly read on the Woodcock test. Vari-ables were forced in the following order, provided F was > 4.0: age, verbal IQ, RAN speed for numbers (logged), running memory total score, Bradley oddity test errors, and total nonsense words read. Table 7 summa-rizes the analysis.

A second stepwise regression pre-dicted the number of nonsense words correctly read. Variables were forced in the following order (again with F to enter set at 4.0): age, verbal IQ, RAN speed for numbers, running mem-ory score, and Bradley oddity score. Table 7 also shows this analysis.

Other variables that were included in the analyses but not accepted were mean auditory and visual digit spans (i.e., averaged across the four condi-




TABLE 5 Serial Memory Spans:


Group COV F Group Sample

Type/condition

Visual digits ST Noise IN IN + N Omnia-b

Auditory digits ST Noise(N) IN IN + N Omnia'b

Auditory letters Rhyme-ST Rhyme-IN Don't-ST Don't-IN Omnic

Auditory words MONO-ST MONO-1N DI-ST DI-IN TRI-ST TRI-IN Omnia'd

ADD-only

5.1(4.9) 4.7(4.6) 5.8(5.5) 5.4(5.2) 5.3(5.1)

5.5(5.4) 5.2(5.1) 5.6(5.6) 5.5(5.3) 5.4(5.4)

3.3(3.2) 3.8(3.7) 4.9(4.8) 4.9(4.8) 4.2(4.1)

4.3(4.2) 4.5(4.4) 4.1(4.0) 4.2(4.1) 3.6(3.5) 3.8(3.7) 4.1(4.0)

Slow

4.6(4.8) 4.1(4.3) 5.3(5.6) 5.3(5.3) 4.8(5.0)

5.0(5.2) 4.9(5.0) 5.3(5.3) 5.3(5.5) 5.2(5.2)

3.5(3.7) 3.5(3.7) 4.6(4.7) 4.6(4.8) 4.1(4.2)

4.0(4.2) 4.1(4.3) 4.1(4.3) 4.1(4.3) 3.5(3.6) 3.5(3.7) 3.9(4.1)

Dyslexia

4.6(4.7) 4.0(4.1) 5.1(5.3) 4.7(4.9) 4.6(4.7)

5.2(5.2) 4.6(4.6) 5.2(5.3) 5.0(5.1) 5.0(5.1)

2.9(3.0) 2.9(3.0) 4.4(4.5) 4.5(4.6) 3.7(3.8)

3.9(4.0) 4.1(4.1) 3.7(3.8) 4.0(4.1) 3.3(3.3) 3.3(3.4) 3.7(3.8)

Age IQ F SD

21.52** 4.15* 1.27

7.49** 1.28 1.06

7.51** 6.09* 4.44*

15.49** 10.39** 2.48

1.1 1.3 1.4 1.3

1.0 1.0 1.1 1.0

0.9 1.1 1.1 1.2

0.8 0.7 0.6 0.8 0.7 0.9

Note. ADD = attention deficit disorder; COV = covariates; ST = standard; IN = incentive; N = noise (distraction). incentive > Nonincentive; bNo noise > Noise; cDon't > Rhyme; dMonosyllabic and disyllabic > trisyllabic. *p<.05. **p<.01.

tions), speed of articulation for the word Arkansas, and the ADD index. The zero-order correlation of ADD scores with Woodcock word list scores was 0.16 and with nonsense word scores, 0.20 (both ps < .05); that is, the higher the ADD score, the better the reading score in this clinic sample! The partial correlations of the speech rate variable with real and nonsense words became nonsignificant once age and verbal IQ were removed. The partial correlations of the auditory and visual digit span means became nonsignifi-cant once age, verbal IQ, and RAN scores were removed.

Obviously, the ability to read non-sense words is the strongest correlate of the ability to read real words; and, the skill level for reading both real words and nonwords depends on the same set of variables. Of those five variables, the three with the highest standardized regression coefficients at Step 5 for both real and nonsense words were age, RAN, and Bradley oddity scores.

Discussion

Baddeley's (1986) corpus on working memory suggested a parsimonious model for pulling together the deficits seen in children with dyslexia. How-ever, we did not find support for his theory that a slow articulation rate is the overarching deficit in these chil-dren. Articulation rate is significantly correlated with continuous naming rate and with auditory and visual memory span, as his theory suggests. Furthermore, all of these variables are strong correlates of reading skill. But when the effects of age and verbal IQ are removed, the partial correlations decline, whereas continuous naming and simple phonological sensitivity re-main robust correlates of reading skill.

Our results do extend Baddeley's im-portant observations on the nature of working memory to children. The chil-dren, like the adults of his studies, showed a decrement in memory span when exposed to extraneous noise.

This finding clearly has implications for teachers and parents. Baddeley em-phasized that it is extraneous speech in particular that disrupts working memory (e.g., radios or TVs in the background, children working in small groups in the classroom, someone talk-ing on the telephone).

Torgesen et al. (1987) were unable to induce improved memory spans by re-ward in their children with reading and memory impairments. Here, we did find an incentive effect. As Table 5 shows, this effect was strongest for vi-sually presented digits. There were no significant group x condition inter-actions for the serial memory tasks. Thus, all three groups were sensitive to incentive and interference.

The effect of word length on serial memory span was confirmed in all three groups. Two-syllable words were recalled as accurately as one-syllable words, but trisyllabic words elicited the theorized decline. We suspect that four- and five-syllable words would have resulted in a group difference, be-cause children with dyslexia have been shown to have difficulty even repeat-ing single polysyllabic words accu-rately (Catts, 1986).

The auditory letter span task was in-cluded primarily to assess the so-called phonological interference effect. Earlier studies suggested that because chil-dren with dyslexia are less sensitive to phonological similarity, they are as ef-ficient at repeating strings of rhyming




TABLE 6 Performance Scores

Task

Rhyme detection accuracy Words rhyme Words don't Nonwords rhyme Nonwords don't

Arithmetic accuracy Nonreward Reward

ADD-only

38.9 36.6 37.4 38.3

15.4 17.2

on Laboratory

Slow

34.1 29.8 32.3 31.9

13.0 16.5

Tasks3

Dyslexia

27.1 22.9 26.5 25.2

12.6 15.5

Sample SD

11.5 12.8 12.2 13.0

4.4 3.2

aSee text for statistical analyses.

TABLE 7 Stepwise Regression to Predict

Accuracy of Reading Real and Nonsense Words3

Zero r /?2 df

Dependent = Woodcock Word List 1. 2. 3. 4. 5. 6.

Age Verbal IQ RAN Running Memory Bradley Oddity Nonsense Words

Dependent = Decodin 1. Age 2. Verbal IQ 3. 4. 5.

RAN Running Memory Bradley

Decoding Skills Nonsense Word List

.61

.33

.70

.47

.65

.85

.47

.36

.60

.48

.64

.61

.72

.81

.83

.86

.91

.47

.62

.70

.74

.77

.37

.51

.65

.70

.73

.83

.22

.38

.49

.55

.60

61.3 55.0 65.2 58.8 56.2 83.9

30.4 32.1 33.3 31.3 36.2

1,106 2,105 3,104 4,103 5,102 6,101

1,106 2,105 3,104 4,103 5,102

aAII coefficients significant at p<.01.

as nonrhyming letters or words. But subsequent work showed this type of interference only with younger chil-dren with dyslexia (see Liberman & Shankweiler, 1985; Mann, 1986). In the present study, all three groups showed the effect equally. Thus, our children with dyslexia did exhibit this more ele-mental evidence of phonological sen-sitivity, even though they faltered on rhyme appreciation. The older ages of our children may account for this finding.

The impaired performance of the dyslexic group on the running memory task deserves special comment for two reasons. First, the other serial memory tasks did not reveal the DYS group to be significantly worse, overall, than the

ADD-only and SLO groups. Second, the running memory task contributed additional unique variance in the step-wise multiple regression equations. The paradigm provides an excellent measure of the recency effect in that the stimuli are presented too rapidly to allow rehearsal. Thus, the subject must report what is automatically encoded. That the recency effect is automatic (see Hasher & Zacks, 1979) is suggest-ed by the fact that performance was not significantly related to age like the other memory variables were. All three groups showed deterioration with in-creasing speed of presentation, but neither list length nor speed interacted with group. It could be that this task taps interference or mental fatigability,

or even perseveration (which would hinder updating). However, Cohen and Netley (1981) believed that the skill in question was that of maintaining rapidly presented, serial phonological patterns in an already loaded working memory.

Although we were interested in rep-licating Baddeley's (1986) findings in children with ADD and/or reading im-pairment, our major aim was to ex-plore differences among the three groups. We have explained our rea-sons for choosing the ADD-only and slow learner groups for contrast with the dyslexic group; this approach proved worthwhile.

Research evidence is rather clear that an attention deficit disorder does not cause developmental dyslexia (Shay-witz et al., 1991; Wood, Felton, Flow-ers, & Naylor, 1991). The data pre-sented above, as well as previous findings on a large ADD sample (Dyk-man & Ackerman, 1991a), further dem-onstrate that severity of ADD symp-tomatology is not linearly related to severity of reading retardation. Yet, the two disorders are frequently overlap-ping, perhaps more in clinic samples than in the total school population (Shaywitz et al., 1991).

The data presented above suggest that poor readers who do not meet dis-crepancy criteria for a diagnosis of developmental dyslexia have a differ-ent cognitive profile. Unlike the dys-lexic group, the slow learner/border-line group was not significantly slower than the ADD group on continuous naming speed (the RAN tests). The SLO group was not different from the ADD group on the Bradley oddity test, which taps elementary phonological sensitivity; indeed, the former was superior to the DYS group on this task as well as on the running memory task.

It can be said that the children clas-sified as SLO were better on all these tasks because, despite lower IQs, they were more advanced readers than those classified as dyslexic. But, as we have reported elsewhere (Dykman & Ackerman, 1991b), even children with




moderate dyslexia (whose reading scores matched the SLO group) per-formed significantly more poorly than the ADD group on continuous naming and oddity tasks. Because both con-tinuous naming and simple phonolog-ical sensitivity (recognition of rhyme and alliteration) have been shown in follow-up studies of preschool children to be robust predictors of reading ac-quisition rate, it appears that deficits in these skills are the major proximate contributors to specific reading retar-dation or dyslexia. Also, these defi-cits continue in adults with dyslexia (Bruck, 1990; Wood et al., 1991). More-over, the genetic studies of the Colo-rado group (see Olson et al., 1989) suggest that phonological weakness is highly heritable. Further suppor t comes from a group of English inves-tigators (MacLean, Bryant, & Bradley, 1987), who showed no effect of social class in preschool performance on phonological sensitivity tasks.

Wolf (1991) provided the most thor-ough report assessing the role of slow naming in developmental dyslexia. Re-searchers initially believed that only rapid continuous naming, not discrete trial naming, could elucidate this defi-cit, but now there is convincing evi-dence that individuals with dyslexia are slower at name retrieval for discrete stimuli as well (Bowers & Swanson, 1991). Whether this deficit is also heri-table has not yet been established. Wolf believed that slow naming is evi-dence of a more general deficit in rapid temporal processing. It is important to emphasize that on the Boston Naming Test, the DYS and SLO groups per-formed equally, but rapid naming was not required. We had thought the DYS group might benefit more from the clues on the BNT, but such was not the case—all groups improved equally. Thus, the speed of retrieval appears to be the critical variable.

The uppermost question now is which brain areas are dysfunctional in children with dyslexia. And this ques-tion is being addressed by neuroanat-omists (e.g., Galaburda, 1991), neu-rologists and neuropsychologists (e.g.,

Rourke, Fisk, & Strang, 1986), brain imagers (e.g., Filipek & Kennedy, 1991; Hynd, Semrud-Clikeman, Lorys, Novey, & Eliopulos, 1990; Lubs et al., 1991; Wood et al., 1991), and elec-trophysiologists studying brain waves during active mental processing (e.g., Holcomb, Ackerman, & Dykman, 1985; Licht, Bakker, Kok, & Bouma, 1988; Taylor & Keenan, 1990). Recent major findings in these areas have been reported in Duane and Gray (1991). While no consensus has emerged as to the biological substrates of dyslexia, an underfunctioning left temporal site (Wernicke's area) is often implicated. Most recently, the magnocellular path-way of the visual system has been implicated (Livingstone, Rosen, Dris-lane, & Galaburda, 1991). This system comes into play w h e n rapid, transient visual tracking is required.

In a related paper on the subjects of this s tudy (Ackerman, Dykman, & Oglesby, 1992b), we report EEG event-related potential differences between the three groups as they participated in the computerized rhyme detection task. These differences occurred most prominently in the electrodes located over the right and left parietal areas. The groups also differed on low beta production (parietal and midline areas) as they viewed strings of short, easy words and were asked to report the last word seen (Ackerman, Dykman, & Oglesby, 1992a). These and the other findings reviewed above suggest that the brains of children with dyslexia may have dysfunctional circuits rather than well-localized areas of dysfunc-tion. However, researchers are still a long way from knowing the core bio-logical causes of dyslexia, and, for now, interventions must continue to address deficits.

Because phonological insensitivity and continuous naming can be easily and reliably assessed in preschool chil-dren, and because both factors predict later reading difficulties and are linear-ly related to degree of retardation in literacy acquisition, it behooves educa-tors to insist on more practice on these skills in the nursery schools and kin-

dergartens in this country. We have evidence from the work of Bradley and associates (see Bradley, 1989, for a re-view) that early stimulation of phono-logical sensitivity can make a lasting difference. Practice at rapid naming of letters and words may bear similar fruit. The work of Moyer (1982) with repeated reading should not be over-looked.

ABOUT THE AUTHORS

Peggy T. Ackerman, MA, is a research asso-ciate in the Departments of Psychiatry and Pediatrics, University of Arkansas for Medical Sciences (UAMS). RoscoeA. Dykman, PhD, is emeritus professor of psychiatry, UAMS, and director of the Behavioral Laboratory, Depart-ment of Pediatrics, Arkansas Children's Hospi-tal. Address: Peggy T. Ackerman, Department of Pediatrics, Center for Ambulatory Research and Education, Arkansas Children's Hospital, 800 Marshall St., Little Rock, AR 72202.

AUTHORS' NOTE

This research was supported by a grant (HD24634) from the National Institute of Child Health and Human Development and by the Marie Wilson Howells Fund. The authors are indebted to co-workers Michelle Gocio, D. Michael Oglesby, Dwayne Tucker, and Nancy Stewart for their help in data collection, statisti-cal analyses, and manuscript preparation. They are also indebted to colleagues at the Child Study Center and the Center for Ambulatory Research and Education who assisted in recruitment of subjects.

REFERENCES

Achenbach, T.M., & Edelbrock, CS . (1983). Manual for the Child Behavior Checklist and revised Child Behavior Profile. Burlington: University of Vermont, Department of Psychiatry.

Ackerman, P.T., Anhalt, J.M., & Dykman, R.A. (1986). Inferential word decoding weakness in RD children. Learning Dis-ability Quarterly, 9, 315-324.

Ackerman, P.T., Anhalt, J.M., Dykman, R.A., & Holcomb, P.J. (1986). Effortful processing deficits in children with read-ing and/or attention disorders. Brain and Cognition, 5, 22-40.



608

Ackerman, P.T., Dykman, R.A., & Gard-ner, M.Y. (1990a). ADD students with and without dyslexia differ in sensitivity to rhyme and alliteration. Journal of Learn-ing Disabilities, 23, 279-283.

Ackerman, P.T., Dykman, R.A., & Gard-ner, M.Y. (1990b). Counting rate, nam-ing rate, phonological sensitivity, and memory span: Major factors in severe dyslexia. Journal of Learning Disabilities, 23, 325-327.

Ackerman, P.T., Dykman, R.A., Hollo way, C , Paal, N.P., & Gocio, M.Y. (1991). A trial of Piracetam in two subgroups of stu-dents with dyslexia enrolled in summer tutoring. Journal of Learning Disabilities, 24, 542-549.

Ackerman, P.T., Dykman, R.A., & Ogles-by, D.M. (1992a). EEG power spectra of dys-lexic, slow learner, and ADD children during word list viewing. Unpublished manu-script.

Ackerman, P.T., Dykman, R.A., & Ogles-by, D.M. (1992b). Visual event related poten-tials of dyslexic children to rhyming and non rhyming stimuli. Manuscript submitted for publication.

Ackerman, P.T., Elardo, P.T., & Dykman, R.A. (1979). A psychosocial study of hy-peractive and learning-disabled boys. Journal of Abnormal Child Psychology, 7, 91-99.

American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author.

American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author.

Ashcraft, M.H., & Fierman, B.A. (1982). Mental addition in third, fourth, and sixth graders. Journal of Experimental Child Psychology, 33, 216-234.

Baddeley, A. (1986). Working memory. Ox-ford, England: Clarendon Press.

Bowers, P.G., & Swanson, L.B. (1991). Naming speed deficits in reading disabil-ity: Multiple measures of a singular process. Journal of Experimental Child Psy-chology, 51, 195-219.

Bradley, L. (1984). Assessing reading difficul-ties: A diagnostic and remedial approach. London: Macmillan Education Ltd.

Bradley, L. (1989). Predicting learning dis-abilities. In J.J. Dumont & H. Nakken (Eds.), Learning disabilities (Vol. 2): Cogni-tive, social, and remedial aspects (pp. 1-17). Amsterdam: Si vets & Zeitlinger.


Bradley, L., & Bryant, P. (1983). Categoriz-ing sounds and learning to read: A causal connection. Nature (London), 271, 746-747.

Bruck, M. (1990). Word recognition skills of adults with childhood diagnoses of dyslexia. Developmental Psychology, 26, 439-454.

Case, R., Kurland, D.M., & Goldberg, J. (1982). Operational efficiency and growth of short-term memory span. Journal of Ex-perimental Child Psychology, 33, 386-404.

Catts, H.C. (1986). Speech production/ phonological deficits in reading-disor-dered children. Journal of Learning Dis-abilities, 19, 504-508.

Cohen, R.L., & Netley, C. (1981). Short-term memory deficits in reading disabled children, in the absence of opportunity for rehearsal strategies. Intelligence, 5, 69-76.

Conners, C.K. (1973). Rating scales for use in drug studies with children: Pharmaco-therapy of children. Psychopharmacology Bulletin, 9, 24-84.

Denckla, M.B., & Rudel, R. (1976). Rapid ''automatized'' naming (R.A.N.): Dys-lexia differentiated from other learning disabilities. Neuropsychologia, 14, 471-479.

Duane, D.D., & Gray, D.B. (Eds.). (1991). The reading brain: The biological basis of dys-lexia. Parkton, MD: York Press.

Dykman, R.A., & Ackerman, P.T. (1991a). Attention deficit disorder and specific reading disability: Separate but often overlapping disorders. Journal of Learning Disabilities, 24, 96-103.

Dykman, R.A., & Ackerman, P.T. (1991b). Working memory in dyslexic children: Final report. Bethesda, MD: NICHD.

Dykman, R.A., & Ackerman, P.T. (1992). Diagnosing dyslexia: IQ regression plus cut points. Journal of Learning Disabilities, 25, 574-576.

Dykman, R.A., Ackerman, P.T., & Hol-comb, P.J. (1985). Reading disabled and ADD children: Similarities and differ-ences. In D. Gray (Ed.), Biobehavioral measures of dyslexia (pp. 47-62). Parkton, MD: York Press.

Ellis, N.R. (1970). Memory processes in retardates and normals: Theoretical and empirical considerations. In N.R. Ellis (Ed.), International review of research in men-tal retardation (Vol. 4). New York: Aca-demic Press.

Felton, R., Naylor, C.E., & Wood, F. (1990). Neuropsychological profiles of adult dys-lexics. Brain and Language, 39, 485-497.

Filipek, P., & Kennedy, D. (1991). Magnetic resonance imaging: Its role in the devel-opmental disorders. In D.D. Duane & D.B. Gray (Eds.), The reading brain: The biological basis of dyslexia (pp. 133-160). Parkton, MD: York Press.

Galaburda, A. (1991). Anatomy of dyslexia: Argument against phrenology. In D.D. Duane & D.B. Gray (Eds.), The reading brain: The biological basis of dyslexia (pp. 119-132). Parkton, MD: York Press.

Goswami, U., & Bryant, P. (1990). Phono-logical skills and learning to read. East Sussex, UK: Erlbaum.

Hasher, L., & Zacks, R.T. (1979). Automatic and effortful processes in memory. Journal of Experimental Psychology, 108, 356-388.

Herjanic, B., & Reich, W. (1982). Develop-ment of a structured psychiatric interview for children: Agreement between child and parent on individual symptoms. Jour-nal of Abnormal Child Psychology, 10, 307-324.

Holcomb, P.J., Ackerman, P.T., & Dyk-man, R.A. (1985). Cognitive event-related brain potentials in children with attention and reading deficits. Psychophysiology, 22, 656-666.

Hollingsworth, A.B. (1957). Two factor index of social position. New Haven, CT: Author.

Hulme, C , Thomson, N., Muir, C , & Laurence, A. (1984). Speech rate and the development of short-term memory span. Journal of Experimental Child Psychol-ogy, 38, 241-253.

Hynd, G.W., Semrud-Clikeman, M., Lorys, A.R., Novey, E.S., & Eliopulos, D. (1990). Brain morphology in developmental dyslexia and attention deficit disorder/ hyperactivity. Archives of Neurology, 47, 919-926.

Jastak, J.F., & Jastak, S. (1984). The wide range achievement test-Revised. Wilming-ton, DE: Jastak Associates.

Kaplan, E., Goodglass, H., Weintraub, S., & Segal, D. (1983). The Boston naming test. Philadelphia: Lea & Febiger.

Liberman, I.Y., & Shankweiler, D. (1985). Phonology and the problems of learning to read and write. Remedial and Special Education, 6(6), 8-17.

Licht, R., Bakker, D.J., Kok, A., & Bouma, A. (1988). The development of lateral event-related potentials (ERPs) related to word naming: A four year longitudinal study. Neuropsychologia, 26, 327-340.

Livingstone, M., Rosen, G., Drislane, F., & Galaburda, A. (1991). Physiological and anatomical evidence for a magno-




cellular defect in developmental dyslexia. Proceedings of the National Academy of Science, 88, 7943-7947.

Loney, J., & Milich, R. (1982). Hyperac-tivity, inattention and aggression in clin-ical practice. In M. Wolraich & D.K. Routh (Eds.), Advances in behavioral pedi-atrics (pp. 113-145). Greenwich, CT: JAI Press.

Lubs, H., Duara, R., Levin, B., Jallad, B., Lubs, M.L., Rabin, M., Kushch, A., & Gross-Glenn, K. (1991). Dyslexia sub-types: Genetics, behavior, and brain im-aging. In D.D. Duane & D.B. Gray (Eds.), The reading brain: The biological basis of dys-lexia (pp. 89-118). Parkton, MD: York Press.

MacLean, M., Bryant, P.E., & Bradley, L. (1987). Rhymes, nursery rhymes and reading in early childhood. Merrill-Palmer Quarterly, 33, 255-282.

Mann, V.A. (1986). Why some children en-counter reading problems. In J.K. Tor-gesen & B.L. Wong (Eds.), Psychological and educational perspectives on learning dis-abilities (pp. 139-159). Orlando, FL: Aca-demic Press.

Moyer, S.B. (1982). Repeated reading. Jour-nal of Learning Disabilities, 15, 619-623.

Olson, R., Wise, B., Conners, F., Rack, J., & Fulker, D. (1989). Specific deficits in component reading and language skills: Genetic and environmental influences. Journal of Learning Disabilities, 11, 339-348.

Pennington, B. (1991). Diagnosing learning disorders: A neuropsychological framework. New York: Guilford Press.

Richardson, E. (1985). The reliability, valid-ity, and flexibility of the Decoding Skills Test. In D.B. Gray & J.F. Kavanagh (Eds.), Biobehavioral measures of dyslexia (pp. 279-296). Parkton, MD: York Press.

Richardson, E., & DiBenedetto, B. (1985). The decoding skills test. Parkton, MD: York Press.

Rourke, B.P., Fisk, J.L., & Strang, J.D. (1986). Neuropsychological assessment of chil-dren: A treatment-oriented approach. New York: Guilford Press.

Shaywitz, B., Shaywitz, S., Liberman, I., Fletcher, J., Shankweiler, D., Duncan, J., Katz, L., Liberman, A., Francis, D., Drey-er, L., Crain, S., Brady, S., Fowler, A., Kier, L., Rosenfield, N., Gore, J., & Makuch, R. (1991). Neurolinguistic and biologic mechanisms in dyslexia. In D.D. Duane & D.B. Gray (Eds.), The reading brain: The biological basis of dyslexia (pp. 27-52). Parkton, MD: York Press.

Shaywitz, S.E., Shaywitz, B.A., Fletcher, J.M., & Escobar, M.D. (1990). Prevalence of reading disability in boys and girls: Results of the Connecticut Longitudinal Study. Journal of the American Medical Association, 164, 998-1002.

Spring, C., & Perry, L. (1986). Naming speed, articulation speed, and serial recall in poor and adequate readers. Unpublished manuscript.

Stanovich, K.E. (1986). Cognitive processes and the reading problems of learning dis-abled children: Evaluating the assump-tion of specificity. In J. Torgesen & B. Wong (Eds.), Psychological and educational perspectives on learning disabilities (pp. 87-131). Orlando, FL: Academic Press.

Stanovich, K.E. (1988). Explaining the dif-ferences between the dyslexic and the garden-variety poor reader: The phono-logical-core variable-difference model. Journal of Learning Disabilities, 11, 590-604.

Taylor, M.J., & Keenan, N.K. (1990). Event-related potentials to visual and language stimuli in normal and dyslexic children. Psychophysiology, 17, 318-327.

Torgesen, J.K., Rashotte, C.A., Greenstein, J., Houck, G., & Portes, P. (1987). Aca-demic difficulties of learning disabled children who perform poorly on memory span tasks. In H.L. Swanson (Ed.), Mem-ory and learning disabilities: Advances in learning and behavioral disabilities (pp. 305-333). Greenwich, CT: JAI Press.

Vellutino, F.R. (1987). Dyslexia. Scientific American, 156, 34-41.

Wagner, R.K., & Torgesen, J.K. (1987). The nature of phonological processing and its causal role in the acquisition of reading skills. Psychological Bulletin, 101,192-212.

Wechsler, D. (1974). The Wechsler intelligence test for children-Revised. San Antonio, TX: Psychological Corp.

Wiederholt, J.L., & Bryant, B.R. (1986). Gray oral reading tests-Revised. Austin, TX: PRO-ED.

Wolf, M. (1991). Letter naming, naming speed, reading, and the contribution of the cognitive neurosciences. Reading Re-search Quarterly, 16, 123-141.

Wolf, M., & Goodglass, H. (1986). Dyslexia, dysnomia, and lexical retrieval: A longi-tudinal investigation. Brain and Language, 18, 154-168.

Wood, F., Felton, R., Flowers, L., & Nay-lor, C. (1991). Neurobehavioral definition of dyslexia. In D.D. Duane & D.B. Gray (Eds.), The reading brain: The biological basis of dyslexia (pp. 1-26). Parkton, MD: York Press.

Woodcock, R.W. (1992). Woodcock reading mastery tests-Revised. Circle Pines, MN: American Guidance Service.

Yule, W., & Rutter, M. (1976). The epidemi-ology and social implications of specific reading retardation. In R. Knights & D.J. Bakker (Eds.), The neuropsychology of learn-ing disorders (pp. 25-40). Baltimore: Uni-versity Park Press.



phonological processes, confrontational naming, and immediate memory in dyslexia

Documents