www.elsevier.com/locate/b&c

Brain and Cognition 57 (2005) 189–194

Influence of auditory–verbal, visual–verbal, visual,and visual–visual processing speed on reading and spelling

at the end of Grade 1

Monique Plazaa,*, Henri Cohenb

a Laboratoire Cognition et Developpement, CNRS, Paris, Franceb Centre de Neuroscience de la Cognition, Universite du Quebec a Montreal, Canada

Accepted 12 August 2004

Abstract

This study examined cognitive processing speed through four modalities (auditory–verbal, visual–verbal, visual, and visual–vi-sual) at the end of Grade 1 and how it influences reading and spelling. The subjects were 124 French-speaking children, selectedfor their contrasting performance on reading and spelling tasks. The children in the first group (N = 69) were average readers;the second group of children (N = 55) performed worse or much worse on all reading and spelling tasks. The experimental designconsisted of a set of 10 tasks administered in two sessions. The major findings reveal that: (1) the children with reading difficultiesdisplayed low and slow performance on most cognitive tasks, whatever the modality; (2) auditory–verbal and visual–verbal process-ing speed significantly predicted written language, which was not the case with the visual modalities; and (3) that visual problems didnot appear to be a potential cause of reading problems in most delayed readers. The findings also confirm the independence of pho-nological and naming-speed skills in reading development and reading impairment.� 2004 Elsevier Inc. All rights reserved.

1. Introduction

It is widely accepted that phonological awareness isone of the best predictors of reading ability. Currentdyslexia research has provided evidence to the effect thatphonological processing deficits play a critical role inreading impairments (Bryant, Nunes, & Bindman,1998; Catts, 1993; Liberman & Shankweiler, 1991; Wag-ner & Torgesen, 1993; Wagner, Torgesen, & Rashotte,1994).

However, the evident importance of phonologicalskills for the acquisition of the alphabetic principle hasdistracted attention from the fact that reading acquisi-tion also involves non-linguistic skills. An alternativeneuropsychological research strategy has emerged that

0278-2626/$ - see front matter � 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.bandc.2004.08.043

* Corresponding author.E-mail address: plaza@psycho.univ-paris5.fr (M. Plaza).

recognizes the importance of phonological processing,but focuses on cognitive processes that may accountfor non-linguistic correlates of reading impairment.

The ‘‘temporal’’ hypothesis suggests that temporalresolution deficits manifested clinically in the languageimpairments of dyslexic subjects will also be expressedin their time-dependent non-linguistic behavior relatedto manual actions (Denckla, 1979; Wolff, Michel,Drake, & Ovrut, 1990), auditory and/or visual stimuliprocessing (Bakker, 1972; Tallal, 1980; Tallal, Miller,& Fitch, 1993; Watson & Miller, 1993). According toKlein (2002) the temporal processing deficit could be re-lated to (a) rate of information processing, (b) increasedsmudging of signals over time, or (c) increased jitters inthe assignment of features to moments in time.

The ‘‘visual’’ hypothesis suggests that visual process-ing deficits occur in developmental dyslexia, involving adisturbance of the magnocellular pathway (Lovegrove &

190 M. Plaza, H. Cohen / Brain and Cognition 57 (2005) 189–194

Slaghuis, 1989; Slaghuis, Lovegrove, & Davidson,1993), decreased sensitivity to low contrast (Stein, Tal-cott, & Walsh, 2000), abnormal processing of visual mo-tion (Demb, Bynton, & Heeger, 1998), and/or visualattentional disorders. In fact the visual hypothesis was,and remains, controversial (Hulme, 1988; Stein et al.,2000), and might apply to a restricted group of dyslexicchildren with surface dyslexia (Valdois et al., 2003).

The ‘‘cross-modal’’ hypothesis suggests that dyslexicchildren have difficulties associating verbal labels withvisual stimuli (Mayringer & Wimmer, 2000; Windfuhr& Snowling, 2001). This paired associate learning defi-cit could produce effects on learning to read that areindependent of those that follow from phonologicalawareness problems. Mayringer and Wimmer (2000)suggest that visual–verbal paired associate learningability taps into the ability to establish word-specificorthographies for words. In turn, this learning difficultymight prevent the development of automaticity in read-ing. By contrast, data concerning visual–visual (e.g.,non-verbal) paired associate learning show that dyslexicchildren perform similarly to normal readers (Liber-man, Mann, Shankweiler, & Werfman, 1982; Nelson& Warrington, 1980; Rapala & Brady, 1990; Vellutino,Steger, & Pruzek, 1973) and that they do not haveany problems with non-verbal learning (Vellutino,Steger, Harding, & Phillips, 1975; Vellutino, Scanlon,& Spearing, 1995).

Other researchers link the temporal, visual–verbal,and cross-modal hypotheses. Breznitz (2002) claims thatthe verbal and visual systems process information differ-ently. Discrimination and identification are achievedfaster through the visual than the auditory route. Suc-cessful word recognition may require the auditory andvisual aspects of printed words to be combined in thebrain within a certain time frame. In that sense, readingdisability is hypothesized to be based on speed of pro-cessing deficits, which affect the connections betweenthe visual/orthographic and auditory/phonologicalroutes (Breznitz, 2002). Wolf, Bowers, and Biddle(2000), Wolf et al. (2002) propose the ‘‘double-deficit’’hypothesis, which incorporates both phonology andnaming-speed processes as sources of reading break-down. The cognitive structure of naming speed is con-ceptualized as a temporally ordered ensemble ofperceptual processes (visual detection, discriminationand recognition), lexical processes (word retrieval) andmotor processes (articulation) (Wolf et al., 2000, 2002).

Our previous studies showed that in French, phono-logical awareness and naming speed account for a signif-icant proportion of independent variance in reading, atthe end of Grade 1 (Plaza & Cohen, 2003) and fromGrade 1 to Grade 2 (Plaza & Cohen, 2004). The presentstudy was conducted to explore the influence of audi-tory–verbal, visual–verbal, visual, and visual–visual pro-cessing speed on reading acquisition. The assessment

was conducted at the end of Grade 1, after one yearof reading instruction, in a group of 124 children (55poor readers and 69 average readers). The auditory–ver-bal modality was assessed through a phoneme elisiontask, the visual–verbal modality through three naming-speed tasks, the visual modality through a visual atten-tion task, and the visual–visual modality through a cod-ing visual-matching task. Each task was timed, andscoring took into account both speed and accuracy.To explore the influence of each modality on readingperformance, comparative analyses between poor andaverage readers were done, and hierarchical regressionanalyses were conducted for the group of children as awhole.

2. Method

2.1. Participants

Two groups of French-speaking children were re-cruited from the first-grade classrooms of eight schools.The criteria for selecting the sample were based on fourreading and spelling tasks involving syllable, word, andsentence reading, and word and pseudoword spelling.The mean age of the children was 82 months. The 69children of the first group were average readers andspellers; the second group of 55 children exhibited lowor very low performance on all tasks.

2.2. Instruments and procedure

The children were tested in two sessions, at the end ofGrade 1, at school. The first assessment was done in agroup setting, while the second session was individuallyadministered.

2.2.1. Group sessionThe group session included four reading and spelling

tasks; two of them derived from a reading battery (Ini-zan, 2000) and two others were experimental. The taskswere administered at school by the children�s teachers.

1. Word discrimination. Children were asked to select 12words uttered by the teacher, each word beingincluded in a set of four similar words (e.g., the childhad to select pocher from among the set cacher/

cocher/pocher/rocher).2. Syllable discrimination. Children were asked to select

nine syllables uttered by the teacher, from among alist of 20 items which included similar items (e.g.,foul/flou or cra/car).

3. Word spelling. Children were asked to spell 20 com-mon words dictated by the teacher.

4. Pseudoword spelling. Children were asked to spell 12pseudowords dictated by the teacher.

M. Plaza, H. Cohen / Brain and Cognition 57 (2005) 189–194 191

2.2.2. Individual session

Eight tasks were administered, including assessmentof reading, auditory–verbal, visual–verbal, visual–visu-al, and visual modalities. All tasks were timed in orderto assess both accuracy and processing speed.

2.2.2.1. Reading tasks.

(a) Accuracy of single-word identification was assessedusing a ‘‘One-Minute Test’’ (Khomsi, 1999), whichrequires the child to read as many words aloud aspossible in one minute.

(b) Ability to use phonic skills to decode words wasassessed through an experimental pseudoword read-ing test, used in previous studies, which requires thechild to read 12 pseudowords aloud. Reading timewas measured.

2.2.2.2. Cognitive tasks.

(a) Auditory–verbal processing: phonological awareness

task. The childwas asked to delete the initial phonemeof orally presented pseudowords and to pronouncethe resulting pseudowords (e.g., /pouk/without/p/be-comes/ouk/). The list included six monosyllabic andsix disyllabic pseudowords, with nine vowels andthree consonants in the initial position. The taskwas timed.

(b) Visual–verbal processing: naming speed. The threetasks, involving digits, letters, and colors, derivedfrom the RAN Test (Denckla & Rudel, 1974). Thechild was asked to quickly name a visual array of 48stimuli, consisting of four items that are presented12 times each in random order. The naming taskswere timed.

(c) Visual–visual processing: matching coding task. Thechild was asked to visually match digits and visualgraphic (non-alphabetic) stimuli, using a code.The 32 stimuli consisted of five stimuli presented sev-eral times in randomorder. The coding taskwas timed.

(d) Visual processing: visual attention task. The childwas asked to search for a target (a letter-like sym-bol) in a visual array of 100 stimuli which included20 targets and 80 distractors (which were visuallysimilar letter-like symbols). The task was timed.

3. Results

3.1. Analyses of variance

3.1.1. Comparative performance of average and poor

readers on group reading and spelling tasks

In comparison with average readers, poor readersperformed below �2 standard deviations on word

discrimination [F (1,123) = 64.3, p < .0001], syllable dis-crimination [F (1,123) = 118.12, p < .0001], word spell-ing [F (1,123) = 323.35, p < .0001], and pseudowordspelling [F (1,123) = 323.2, p < .0001]. The most difficulttasks were those involving spelling. With regard to read-ing, syllable discrimination was more difficult than worddiscrimination.

3.1.2. Comparative performance of average and poor

readers on individual reading tasks

In comparison with average readers, poor readersexhibited significant difficulties with pseudoword read-ing [F (1,123) = 350.10, p < .0001] and word reading[F (1,123) = 160.5, p < .0001].

3.1.3. Cognitive processing speed

3.1.3.1. Comparative performance of average and poor

readers in the auditory–verbal modality. In comparisonwith average readers, poor readers had significantdifficulties with the phoneme elision task. They per-formed worse [F (1,123) = 274.9, p < .0001] andmore slowly [F (1,123) = 31.1, p < .0001] than theirage-peers.

3.1.3.2. Comparative performance of average and poor

readers in the visual–verbal modality. In comparison withaverage readers, poor readers exhibited impaired perfor-mance on digit, letter, and color naming tasks. Althoughtheir accuracy scores were significantly different fromthose of average readers, they committed few qualitativeerrors. By contrast, their processing speed was slowerfor digit naming [F (1,123) = 30.6], p = .0009], letternaming [F (1,123) = 41.8, p < .0001], and color naming[F (1,123) = 25.2, p < .0001].

3.1.3.3. Comparative performance of average and poor

readers in the visual–visual modality. In comparison withaverage readers, poor readers performed slower on thevisual-matching task [F (1,123) = 12.5, p = .0006] andcommitted more qualitative errors [F (1,123) = 9.3,p = .002].

3.1.3.4. Comparative performance of average and poor

readers in the visual modality. Average and poor readersperformed similarly on the visual attention task, with re-gard to both accuracy [F (1,123) = 1.9, NS] and process-ing speed [F (1,123) = 0.8, NS].

3.2. Intercorrelations among reading accuracy and the

independent variables

Scores on the six reading and spelling tasks wereaveraged to yield a composite measure of written lan-guage skills. On the other hand, in order to take into ac-count both accuracy and processing speed for theindependent variables, the scores which were used in

192 M. Plaza, H. Cohen / Brain and Cognition 57 (2005) 189–194

the following statistical analyses were calculated on thebasis of an accuracy/time ratio.

The correlations among the variables are displayed inTable 1.

Most of the correlations were sizable. Nevertheless,written language was highly correlated with phonemeelision (.80, p < .001), letter naming (.64, p < .001), digitnaming (.57, p < .001), and color naming (.49, p < .001).

In order to determine whether the auditory–verbal,visual–verbal, visual–visual, or visual modalities couldaccount for reading ability in Grade 1, several hierarchi-cal multiple regression analyses were performed and areshown in Table 2. To determine whether one variable

Table 1Correlations among written language and processing speed measures

Written language Digit naming Letter naming

Written language 1Digit naming .57 1Letter naming .64 .83 1Color naming .49 .68 .17Visual attention .32 .29 .33Phoneme elision .80 .54 .63Coding .35 .42 .40

Note. Correlations greater than .19 are significant at the .05 level, correlationsthan .32 are significant at the .001 level.

Table 2Hierarchical multiple regression analysis for experimental variables predictin

Step Variable entered R

1 Auditory–verbal .792 Visual–verbal .813 Visual–visual .814 Visual .822 Visual–visual .83 Visual .814 Visual–verbal .822 Visual .83 Visual–visual .814 Visual–verbal .82

1 Letter naming .662 Phoneme elision .813 Visual attention .824 Visual–visual .822 Visual–visual .663 Visual attention .674 Visual–verbal .822 Visual .673 Visual–verbal .674 Verbal–visual .82

1 Visual–visual .362 Visual .453 Auditory–verbal .814 Visual–verbal .82

1 Visual .312 Visual–visual .453 Auditory–visual .814 Visual–verbal .82

significantly predicts reading after the other variableshave been statistically controlled, each variable was,respectively, entered at Step 1, Step 2, Step 3, and Step4. Letter naming speed was chosen to represent the vi-sual–verbal modality since it was the most significantvariable.

Table 2 indicates that: (a) auditory–verbal and vi-sual–verbal processing speed accounted for a significantproportion of the variance in written language whenentered at any step, (b) visual attention processingaccounted for a significant proportion of the variancein written language only when entered at Step 1 or 2before auditory–verbal and visual–verbal processing,

Color naming Visual attention Phoneme elision Coding

1.17 1.49 .31 1.46 .25 .28 1

greater than .25 are significant at the .01 level, and correlations greater

g written language

R2 change p Value

.63 <.0001

.3 .0010 NS.2 NS.1 .04.2 NS.4 .008.1 .01.2 .01.4 .008

.43 <.0001

.23 <.0001

.1 NS

.1 NS

.1 NS

.1 NS

.23 <.0001

.2 NS

.1 NS

.23 <.0001

.13 <.0001

.7 <.0001

.44 <.0001

.4 .008

.09 .0004

.2 <.0001

.37 <.0001

.4 .008

M. Plaza, H. Cohen / Brain and Cognition 57 (2005) 189–194 193

(c) visual–visual processing accounted for a significantproportion of the variance when entered at Step 1 orat Step 2 after visual or auditory–verbal processing,but not when entered after visual–verbal processing.The results show that auditory–verbal and visual–verbalprocessing accounted for significant unique variance,which was not the case for the visual and visual–visualmodalities.

4. Discussion

The aim of this paper was to determine whether writ-ten language at the end of Grade 1 was best predicted byauditory–verbal, visual–verbal, visual–visual, or visualprocessing speed. The auditory–verbal modality wasassessed through a phoneme elision timed task, whichrequires the ability to orally perceive, isolate and manip-ulate phonemic units, and to produce verbal units. Thevisual–verbal modality was assessed through three nam-ing-speed tasks, involving digits, letters, and colors. Thevisual–visual ability was assessed through a codingtimed task, which requires visual matching. The visualmodality was assessed through a visual attentional task,which requires visual scanning and fine-grain visualanalysis.

The subjects in the study were 124 children; 69 ofthem exhibited average reading and spelling perfor-mance and 55 poor or very poor performance.

The correlation matrix supported the hypothesis thatwritten language implicates auditory–verbal, visual–ver-bal, visual–visual, and visual processing speed. Never-theless, comparative data between the two groups ofchildren showed that, although poor readers performedsignificantly worse and more slowly than average read-ers in the auditory–verbal, visual–verbal, and visual–vi-sual modalities, the two groups performed similarly inthe visual modality. The multiple regression analysis re-stricted the area of hypothesis, demonstrating that writ-ten language skills were significantly linked to theauditory–verbal and visual–verbal modalities, which ac-counted for 66% of the variance. Visual and visual–visu-al abilities, by contrast, failed to account for anysignificant unique variance once auditory–verbal and vi-sual–verbal modalities were partialed out.

The strong involvement of the auditory–verbalmodality was not surprising, inasmuch as phonologicalawareness, and particularly phonemic awareness, isknown to be both a strong predictor of reading and aconsequence of reading efficiency in alphabetic lan-guages. Young children who have not yet begun to readmay easily perceive and manipulate syllables, which areperceptually salient, but they do not clearly perceive pho-nemes, which are more ‘‘abstract’’ units (Plaza, 2001).

For a long time, researchers have subsumed readingspeed under phonological processes. However, many

authors now claim that the processes underlying namingspeed and phonological awareness are dramatically dif-ferent. In naming-speed tasks, the child first becomesfamiliar with the visual and phonological features offour items, which occur 12 times. Thus, the subject isnot required to retrieve phonological codes for wordsfrom long-term memory, but only to refresh them inworking memory. Secondly, naming-speed tasks arecross-modal; they require simultaneous visual andverbal processing. Finally, naming-speed tasks requirerapid transitions from one item to another, with con-stant processing of inhibition and activation. Namingspeed and reading share the second and third featureslisted.

The visual–visual task and the naming-speed tasksshare the same emphasis on working memory and visualprocessing. This probably explains why the visual–visualmodality (coding matching task) significantly correlatedwith reading when entered in the regression equation atStep 2, after the auditory–verbal modality, and was nolonger significant when entered after visual–verbal nam-ing-speed tasks.

The pure visual modality did not differentiate be-tween average and poor readers. Although fine-grain vi-sual analysis is required in the process of reading, thisvariable did not appear in our sample as a significantvariable for explaining individual differences betweenaverage and poor readers. The absence of any influenceby visual modalities is in accordance with data concern-ing visual processing and visual–visual paired associatelearning, which showed that poor readers perform simi-larly to normal readers (Rapala & Brady, 1990; Vellu-tino et al., 1995).

These findings, which confirm the independence ofnaming speed from phonological processing, alsoemphasize the influence of visual–verbal and auditory–verbal processing speed on reading. Cross-modal vi-sual–verbal processing speed seems to be of particularrelevance for learning to read—as much as, or evenmore than, phonological awareness.

References

Bakker, D. J. (1972). Temporal order in disturbed reading: develop-

mental and neuropsychological aspects in normal and reading

retarded children. Rotterdam: Rotterdam University Press.Breznitz, Z. (2002). Asynchrony of visual-orthographic and auditory-

phonological word recognition processes: an underlying factor indyslexia. Reading and Writing: An Interdisciplinary Journal, 15,15–42.

Bryant, P., Nunes, T., & Bindman, M. (1998). Awareness of languagein children who have reading difficulties. Historical comparisons ina longitudinal study. Journal of Child Psychology and Psychiatry,

39, 501–510.Catts, H. (1993). The relationships between speech-language impair-

ments and reading disabilities. Journal of Speech and Hearing

Research, 36, 948–958.

194 M. Plaza, H. Cohen / Brain and Cognition 57 (2005) 189–194

Demb, J., Bynton, G. M., & Heeger, D. J. (1998). Functional magneticresonance imaging of early visual pathways in dyslexia. The Journalof Neuroscience, 18, 6939–6951.

Denckla, M. B. (1979). Childhood learning disabilities. In K. M.Heilman & E. E. Valenstein (Eds.), Clinical neuropsychology

(pp. 535–576). New York: Oxford University Press.Denckla, M. B., & Rudel, R. O. (1974). Rapid automatized naming of

pictured objects, colors, letters, and numbers by normal children.Cortex, 10, 186–202.

Hulme, C. (1988). The implausibility of low-level visual deficits as acause of children�s reading difficulties. Cognitive Neuropsychology,

5, 369–374.Inizan, A. (2000). Le temps d�apprendre a lire. Paris: Editions et

Applications Psychologiques.Khomsi, A. (1999). LMC-R. Paris: les Editions du Centre de

Psychologie Appliquee.Klein, R. (2002). Observations on the temporal correlates of reading

failure. Reading and Writing: An Interdisciplinary Journal, 15,207–232.

Liberman, I. Y., Mann, V., Shankweiler, D., & Werfman, M. (1982).Children�s memory for recurring linguistic and non-linguisticmaterial in relation to reading ability. Cortex, 18, 367–375.

Liberman, I. Y., & Shankweiler, D. (1991). Phonology and beginningto read. In L. Rieben & C. A. Perfetti (Eds.), Learning to read

(pp. 3–17). Hillsdale, NJ: Erlbaum.Lovegrove, W. J., & Slaghuis, W. (1989). How reliably are visual

differences found in dyslexics?. Irish Journal of Psychology, 10,542–550.

Mayringer, H., & Wimmer, H. (2000). Pseudoname learning byGerman speaking children with dyslexia: evidence for a phonolog-ical learning deficit. Journal of Experimental Child Psychology, 75,116–133.

Nelson, H. E., & Warrington, E. K. (1980). An investigation ofmemory functions in dyslexic children. British Journal of Psychol-

ogy, 71, 487–503.Plaza, M. (2001). The interaction between phonological processing,

syntactic awareness and reading: a longitudinal study fromKindergarten to Grade 1. First Language, 21, 03–24.

Plaza, M., & Cohen, H. (2003). The interaction between phonologicalprocessing, syntactic sensitivity and naming-speed in the readingand spelling performance of first-grade children. Brain and Cogni-

tion, 53, 287–292.Plaza, M., & Cohen, H. (2004). Predictive influence of phonological

processing, morphological/syntactic skills and naming-speed onspelling performance. A longitudinal study from Grade 1 to Grade2. Brain and Cognition, 55, 368–373.

Rapala, M. M., & Brady, S. (1990). Reading ability and short-termmemory. Reading and Writing: An Interdisciplinary Journal, 2,1–25.

Slaghuis, W. L., Lovegrove, W. J., & Davidson, J. A. (1993). Visualand language processing deficits are concurrent in dyslexia. Cortex,29, 601–615.

Stein, J. F., Talcott, J., & Walsh, V. (2000). Controversy about thevisual magnocellular deficit in developmental dyslexia. Trends in

Cognitive Neuroscience, 4, 209–211.Tallal, P. (1980). Auditory temporal perception, phonics, and

reading disabilities in children. Brain and Language, 9, 182–198.

Tallal, P., Miller, S., & Fitch, R. H. (1993). Neurobiological basis ofspeech: A case of the preeminence of temporal processing. In P.Tallal, A.M. Galaburda, R.R. Llinas, & C. Von Eurler (Eds.),Temporal information processing in the nervous system. Annals of the

New York Academy of Sciences (Vol. 682, pp. 27–47). New York:New York Academy of Sciences.

Valdois, S., Bosse, M.-L., Ans, B., Carbonnel, S., Zorman, M., David,D., et al. (2003). Phonological and visual processing deficits candissociate in developmental dyslexia: Evidence from two casestudies. Reading and Writing: An Interdisciplinary Journal, 16,541–572.

Vellutino, F. R., Scanlon, D. M., & Spearing, D. (1995). Semantic andphonologic coding in poor and normal readers. Journal of

Experimental Child Psychology, 59, 76–123.Vellutino, F. R., Steger, J. A., Harding, C. J., & Phillips, F. (1975).

Verbal vs non-verbal paired-associate learning in poor and normalreaders. Neuropsychologia, 13, 75–82.

Vellutino, F. R., Steger, J. A., & Pruzek, R. M. (1973). Inter- vs intra-sensory deficits in paired associate learning in poor and normalreaders. Canadian Journal of Behavioural Science, 5, 111–123.

Windfuhr, K. L., & Snowling, M. J. (2001). The relationship betweenpaired associate learning and phonological skills in normallydeveloping readers. Journal of Experimental Child Psychology, 80,160–173.

Wagner, R., & Torgesen, J. (1993). The nature of phonologicalprocessing and its causal role in the acquisition of reading skills.Psychological Bulletin, 101, 192–212.

Wagner, R., Torgesen, J., & Rashotte, C. (1994). Development ofreading-related phonological processing abilities: New evidence ofbidirectional causality from a latent variable longitudinal study.Developmental Psychology, 30, 73–87.

Watson, B. U., & Miller, T. K. (1993). Auditory perception,phonological processing and reading ability/disability. Journal ofSpeech and Hearing Research, 36, 850–863.

Wolf, M., Bowers, P., & Biddle, K. (2000). Naming-speed processes,timing and reading: a conceptual review. Journal of Learning

Disabilities, 33, 387–407.Wolf, M., Goldberg O�Rourke, A., Gidney, C., Lovett, M., Cirino, P.,

& Morris, R. (2002). The second deficit: an investigation of theindependence of phonological and naming-speed deficits in devel-opmental dyslexia. Reading and Writing: An Interdisciplinary

Journal, 15, 43–72.Wolff, P., Michel, G., Drake, C., & Ovrut, M. (1990). Rate and timing

precision of motor coordination in developmental dyslexia. Devel-

opmental Psychology, 23, 349–359.