Letter and letter-string processing in developmental dyslexia

cortex 46 (2010) 1272–1283

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Special issue: Research report

Letter and letter-string processing in developmental dyslexia Maria De Lucaa,*, Cristina Buranib, Despina Paizib,d, Donatella Spinellia,c and Pierluigi Zoccolottia,d a

Neuropsychology Unit, IRCCS Fondazione Santa Lucia, Rome, Italy Institute for Cognitive Sciences and Technologies (ISTC – CNR), Rome, Italy c Department of Education Sciences in Sport and Physical Activity, University «Foro Italico», Rome, Italy d Department of Psychology, Sapienza University of Rome, Italy b

article info

abstract

Article history:

This study evaluated letter recognition processing in Italian developmental dyslexics and

Received 29 October 2008

its potential contribution to word reading. Letter/bigram recognition (naming and match-

Reviewed 27 February 2009

ing) and reading of words and non-words were examined. A group of developmental

Revised 18 May 2009

dyslexics and a chronologically age-matched group of skilled readers were examined.

Accepted 23 June 2009

Dyslexics were significantly slower than skilled readers in all tasks. The rate and amount

Published online 3 July 2009

model (RAM, Faust et al., 1999) was used to detect global and specific factors in the performance differences controlling for the presence of over-additivity effects. Two global

Keywords:

factors emerged. One (‘‘letter-string’’ factor) accounted for the performance in all (and

Developmental dyslexia

only) word and non-word reading conditions, indicating a large impairment in dyslexics

Letter recognition

(more than 100% reaction time – RT increase as compared to skilled readers). All the letter/

Word/non-word reading

bigram tasks clustered on a separate factor (‘‘letter’’ factor) indicating a mild impairment

RAM

(ca. 20% RT increase as compared to skilled readers). After controlling for global factor influences by the use of the z-score transformation, specific effects were detected for the ‘‘letter-string’’ (but not the ‘‘letter’’) factor. Stimulus length exerted a specific effect on dyslexics’ performance, with dyslexics being more affected by longer stimuli; furthermore, dyslexics showed a stronger impairment for reading words than non-words. Individual differences in the ‘‘letter’’ and ‘‘letter-string’’ factors were uncorrelated, pointing to the independence of the impairments. The putative mechanisms underlying the two global factors and their possible relationship to developmental dyslexia are discussed. ª 2009 Elsevier Srl. All rights reserved.

1.

Introduction

The present study aims to evaluate letter recognition processing in Italian developmental dyslexics and its potential contribution to word reading. Psychophysical studies provide compelling evidence that letter recognition represents an unavoidable stage in reading

words (Pelli et al., 2003). Thus, in the presence of a reading deficit, it is important to know how much of the deficit can be ascribed to a deficiency in letter recognition. Neuroimaging studies (James and Gauthier, 2006; James et al., 2005) demonstrate the presence of separate networks dedicated to letter processing and orthographic string processing. Therefore, it is conceivable that processing of single letters and of letter

* Corresponding author. Neuropsychology Unit, IRCCS Fondazione Santa Lucia, Via Ardeatina 306, 00179 Rome, Italy. E-mail addresses: [email protected], [email protected] (M. De Luca). 0010-9452/$ – see front matter ª 2009 Elsevier Srl. All rights reserved. doi:10.1016/j.cortex.2009.06.007

cortex 46 (2010) 1272–1283

strings may be dissociated behaviourally in dyslexics; namely, defective letter-string processing can be observed in the presence of intact letter processing. In the case of acquired reading deficits, some disorders have been interpreted as predominantly due to the impaired ability to recognise letters. For example, the deficit of some of the patients who show letter-by-letter reading can be interpreted as due to an inefficiency in letter identification (e.g., Rosazza et al., 2007). In the developmental domain, spared letter recognition has been reported for letter position dyslexia (Friedmann and Rahamim, 2007) and developmental attentional dyslexia (Friedmann et al., 2010, this issue). It is also commonly accepted that letter recognition is spared in surface and phonological developmental dyslexics and that the impairment emerges at a later stage of processing (e.g., Jackson and Coltheart, 2001). This assumption is mostly due to results based on letter recognition accuracy measurements.1 However, tasks requiring letter identification may prove extremely simple. It is unlikely (though not impossible) that children who attend school regularly will make a substantial number of errors in reading single letters. Therefore, such a task is insensitive and may underestimate a potential difficulty. On the other hand, using time measures a number of studies have reported that dyslexic children have difficulty in reading letters. For example, using the Rapid Automatized Naming (RAN ) paradigm, children are asked to name rapidly sequences of visual stimuli: pictured objects, colours, letters and numbers (see Denckla and Rudel, 1974). Dyslexics are slow in naming letters as well as in naming digits or colours (Denckla and Rudel, 1976). Nevertheless, the actual contribution of these defective performances to the reading deficit is difficult to assess and general slowness in naming does not necessarily indicate that letter identification per se is deficient. Even if sensitive measures (such as time) are used, it is difficult to compare the level of impairment in two tasks that vary greatly in general difficulty, such as reading single letters and words. This problem is not restricted to letter recognition. A wealth of experimental studies (well beyond what can be reviewed here) have shown that proficient readers outperform dyslexics in a large variety of tasks (other than naming of orthographic materials), including naming of non-orthographic stimuli, lexical and orthographic decision, spelling, non-word repetition, phoneme segmentation, deletion and so on. Many of these findings have proven robust enough to be replicated. At the same time, such a large variety of defective performances clearly call into action very different mechanisms, thus making it difficult to propose a unitary interpretation of the disorder. Critical confounding is produced by the presence of ‘‘over-additivity’’ effects (Salthouse and Hedden, 2002): the more difficult conditions typically yield larger group differences in performance than the relatively simpler conditions, over and above the presence of task-specific impairments. Consequently, it would be important to obtain a reliable estimate of the specificity of the disturbance in any given task that discriminates dyslexics from proficient readers. A related question is to establish if some of these numerous differences 1 A letter recognition sub-test is part of many widely used reading batteries (e.g., in Italian, the Evaluation of Developmental Dyslexia and Dysgraphia by Sartori et al., (1995).

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can be ascribed to a smaller set of underlying dimensions. One effective approach for dealing with these issues is to refer to models that make explicit predictions about the general and specific components of individual differences in informationprocessing tasks. Faust et al. (1999) proposed the rate and amount model (RAM) that can be applied to measures of performance pertaining to speed (e.g., reaction times – RTs). This assumes that a) individuals possess a characteristic processing speed that remains relatively stable across different experimental conditions and b) that each condition requires a certain amount of information to be processed before an appropriate response can be initiated. Two characteristics of this model are critical here. First, it makes explicit predictions about the non-task-specific (hereafter, global) factor contributing to the individual performance. For example, it predicts a linear relationship between the condition means for a group and those of a different group varying for global processing ability. This allows testing hypotheses about which tasks probe the global factor. Second, the model allows disentangling the contribution of the global and task-specific factors on individual performance. It is proposed that group differences in a given task depend upon both general components in performance and selective variations of the influence played by the parameters manipulated (e.g., lexicality and length). In standard parametric analyses (such as analysis of variance – ANOVA), the former express as main effects and the latter as interactions between the parameter and group. However, overall differences in performance can spuriously inflate the size of the interaction by producing over-additivity effects. Faust et al. (1999) have proposed various data transformations (including z-scores) appropriate to control for this over-additivity effect. These data transformations allow disentangling the relative role of global influences from that of task specificity. Note that this perspective focuses on whether the size of the group effect indicates a systematic dimensional coherence across conditions, rather than on examining the presence of significant group differences in each single experimental condition separately. We have proposed that this approach has the potential to reduce the large number of difficult-tointerpret group differences between dyslexics and proficient readers to one, or a few, dimensions (Zoccolotti et al., 2008). An important methodological consideration concerns the number of global factors that can be envisaged. Often, reference is made to a global component in performance as a single factor indicating the contribution of ‘‘speed of processing’’ per se to higher cognitive functioning. Anderson (1992) has envisaged various theoretical options of how such a general factor could modulate dyslexia (pp. 189–195). Nevertheless, while speed of processing may be a useful concept for understanding cognitive differences on various tasks across the life span, with a general slowing of processing in the elderly (Faust et al., 1999; Kail and Salthouse, 1994), it is hard to imagine how such a general concept could account for the relatively specific deficits shown by dyslexics. Consistently, Bonifacci and Snowling (2008) have demonstrated that a measure of speed of processing accounted for differences between children of differing intelligence but not for differences between groups varying for reading skill. Thus, it seems erroneous to expect that the non-task-specific differences in performance between dyslexics and good readers can be

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captured by a single factor. This view is expressed quite clearly by Kail and Salthouse (1994, p. 202) in their statement that there is no reason to assume that there is only one single processing resource underlying all aspects of cognitive performance. Identifying the conditions that map onto the global factor (or factors) which account for the differences in performance between dyslexics and skilled readers may represent a venue to understanding the mechanisms generating the deficit. It should be kept in mind, however, that focusing on the face values of the single conditions (raw data) may be misleading. Indeed, a more convincing interpretation might be obtained from an analysis of the communality across conditions that contribute to the factor(s) and from the absence of a relationship with conditions that do not contribute to the factor(s). In a previous study, we applied the RAM to the vocal RTs of dyslexics and controls in naming pictures, words and nonwords of varying length (Zoccolotti et al., 2008). Dyslexics were slower in naming orthographic strings (both words and nonwords) but not in naming pictures corresponding to the same words. In the raw RTs, the group by lexicality interaction indicated larger RT differences in naming non-words than words in dyslexics as compared to controls. However, this effect vanished when data transformations apt to control for over-additivity were used. Zoccolotti et al. (2008) concluded that dyslexics’ impairment in reading non-words (often considered a specific marker of phonological dyslexia; e.g., Rack et al., 1992) could be most parsimoniously interpreted as due to a global deficit in ‘‘naming orthographic strings’’. Since the global deficit affected both words and non-words, we proposed that it could be localised at a pre-lexical stage of processing. We also stated that a full definition of the nature of the global component(s) that affect performance awaits further work on a larger variety of tasks and (stimuli) conditions. The present study, investigating single letter and bigram processing and using different tasks, aims to contribute to such a definition. Does a single factor explain the speed difference between dyslexics and skilled readers when dealing with any orthographic material? If present, would the group difference for single letters/bigrams have the same size as that for words (and non-words) after controlling for over-additivity effects? In considering multiple sources of impairment, it should be taken into account that different forms of developmental dyslexia have been reported (Temple, 2006); most likely, they refer to independent functional impairments. To evaluate performance with letters, we examined several conditions including: a) a letter naming task which requires retrieving the phonological code for the letter name; b) a syllable reading task which probes the ability to apply grapheme-tophoneme conversion rules; c) a sequential matching of consonant pairs which tests the ability to store and compare letter shapes and d) a simultaneous matching task of letters varying for case which requires abstract letter identity (Coltheart, 1981, 1987). Critically, these tasks allowed us to test a number of components underlying letter recognition that might contribute to the reading deficit. Thus, we may observe selective failure in dyslexic children at some specific level of processing (e.g., in terms of abstract phonemic or graphemic representation). Performances in these letter tasks were compared with those in tasks requiring the naming of longer letter strings (both

words and non-words). In view of the importance of the number of letters in modulating dyslexics’ performance, length was systematically manipulated in the letter-string conditions. Because of the great variety of tasks (a total of 20 experimental conditions) we expected large between-task variations in performances; this is an important pre-requisite for applying the RAM and for detecting global components in the data. Overall, the general aim of the study was to detect the conditions that contribute to generating a difference in performance between dyslexics and skilled readers in dealing with orthographic materials (single letters, bigrams, syllables and longer letter strings). It should be noted that our intention was not to identify all the factors contributing to reading performance, but was specifically aimed at discovering a source of the reading impairment. Different hypotheses can be advanced concerning the role of letter recognition in reading performance. If the global factor accounting for the difference between dyslexics and skilled readers refers to slowness in processing orthographic material as such, it is reasonable that letter processing, similarly to letter strings, participates fully in this deficit. Alternatively, one could posit that letter identification is intact or only partially impaired and that the orthographic deficit becomes severe only when strings of letters (i.e., words and non-words) are processed. Some observations from the visual psychophysical literature support the latter alternative. It has been proposed that visual crowding contributes to the genesis of dyslexia, with dyslexics showing stronger crowding effects than normal readers (e.g., Bouma and Legein, 1977). Crowding refers to the decrease in recognisability of a letter surrounded by other letters placed closer than a critical distance (Pelli et al., 2004); therefore, crowding is expected with closely spaced letter strings but not with isolated letters. Recently, a strong claim was made that the only limit to reading rate in normal readers is crowding (Pelli et al., 2007). If crowding contributes to developmental dyslexia, the performance in identifying single letters (and bigrams) is not expected to contribute to the same factor underlying processing of orthographic material.

2.

Methods

2.1.

Participants

A group of 18 dyslexics (9 girls and 9 boys) with a mean age of 11.3 years (standard deviation – SD ¼ .3) and a group of 36 (18 girls and 18 boys) skilled readers (mean age: 11.3 years, SD ¼ .3) were tested. Criteria for inclusion in the dyslexic group were scores of at least two SDs below the norm for either speed or accuracy in a standardised Italian reading level examination (MT reading test, Cornoldi and Colpo, 1995; see below). Performance was well within the normal range in reading comprehension and Raven’s Coloured Progressive Matrices for all children according to Italian normative data (Pruneti et al., 1996). All participants had normal or corrected to normal visual acuity. The two groups were matched for chronological age, sex and nonverbal IQ levels based on their scores on Raven’s Coloured Progressive Matrices (see Table 1).

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Table 1 – Summary of statistics (mean age in years and months, with range in parentheses; no. of male and female participants), mean scores on Raven’s test (with SD in parentheses), mean z-scores on reading speed, accuracy, and comprehension (with SD in parentheses) for the two groups of participants (dyslexic and skilled readers). Group

Chronological age Male Female Raven’s test Reading speed Reading accuracy Reading comprehension

Dyslexic readers Skilled readers

2.2.

11;3 (10;9–11;9) 11;3 (10;6–11;9)

9 18

9 18

30.5 (SD ¼ 3.2) 30.3 (SD ¼ 3.1)

Reading evaluation

In the MT reading test the child reads aloud a passage of text with a 4-min time limit; speed (s per syllable) and accuracy (number of errors, adjusted for the amount of text read) are scored. A comprehension sub-test was also given (but not used as part of the selection criteria). The participant reads a second passage silently, with no time limit, and then responds to 10 multiple-choice questions. Mean scores for the two groups of participants for reading speed, accuracy and comprehension are given in Table 1. Of the 18 dyslexic children, 4 were below the cut-off for both speed and accuracy and 14 for accuracy only according to standard reference data (Cornoldi and Colpo, 1995). Comprehension was generally spared, a common finding in Italian dyslexic children (Judica et al., 2002). Individual profiles of speed and reading responses are presented in Appendix A. The analysis of the error profile for each dyslexic child in reading the MT text was carried out based on the error scoring proposed by Hendriks and Kolk (1997); this error classification (developed for Dutch) may prove appropriate for an orthographically regular language such as Italian. The classification of responses considers three main categories: sounding-out behaviour (i.e., sounding-out parts of the word before uttering the whole word), word substitution and residual responses (such as the production of non-words); for an in-depth description of the categories, see Hendriks and Kolk (1997). An inspection of the individual performances indicates that most children were slow (on average of ca. 80% with respect to the skilled readers) and showed frequent sounding-out behaviour (in 5.8% of words; categories S1 to S4), nearly always producing a correct utterance. Word substitutions consisted mostly of visual errors, visual–context errors, function word substitution and derivational errors (involving 5.6% of words; categories W1 to W8). In a proportion of cases (2.1%) the utterance produced was a non-word. Some individual differences were also apparent. In particular, although some children show only mild speed impairment, also in these cases the general profile of errors held true. Overall, this pattern discloses the slow, laborious reading characteristic of these children with frequent recourse to sounding-out words before uttering them; the errors on words primarily represent visual approximations sometimes producing non-lexical responses. This profile appears consistent with the prevalent use of the grapheme-to-phoneme conversion routine (Zoccolotti et al., 1999). For the aim of the present study, this group of children appeared sufficiently homogeneous to be treated as a group.

2.3.

Apparatus and general characteristics of the tasks

Stimuli were presented on the screen of a PC and controlled by the E-Prime software. The stimuli were displayed in white on

1.3 (SD ¼ 1.0) .5 (SD ¼ .4)

2.8 (SD ¼ .6) .0 (SD ¼ .5)

.6 (SD ¼ .2) .4 (SD ¼ .4)

a black background and the font was Courier 18 points, a fixed-width font (i.e., all letters have the same width). At the viewing distance of 57 cm, each letter subtended .6 . The order of trials was randomised within each block by the software. For the naming conditions, a voice key connected to the computer measured vocal RTs in milliseconds (msec) at the onset of pronunciation. A fixation cross was displayed for 500 msec followed by an inter-stimulus interval of 300 msec before the display of each stimulus. Each stimulus disappeared at the onset of pronunciation or after 4000 msec had elapsed. An inter-trial interval of 1000 msec followed. The experimenter noted pronunciation errors. For the same–different judgements, the participant pressed one of two keys (YES–NO) connected to a serial response box. In all tasks, only RTs to correctly responded items were considered for the analyses.

2.4.

Experimental tests

2.4.1.

Test 1: naming letters

A list of 13 single upper case letters was used. Letters were the five vowels of the Italian alphabet and the eight consonants whose name corresponds to a V or CV Italian pronounceable two-letter syllable (A, B, C, D, E, G, I, O, P, Q, T, U, and V). There was one practice block consisting of five trials and three experimental blocks, each consisting of 13 experimental trials (corresponding to the 13 different letters). The order of presentation of the trials was different across blocks. Randomisation was fixed across participants. The participants had to say the name of the letter as quickly as possible. Mean vocal RTs to correctly named letters were measured.

2.4.2.

Test 2: reading syllables

A list of 30 upper case two-letter CV syllables was used. None of these syllables corresponded to an entry in the lexicon; their token mean frequency drawn from the Database II: Syllables (Stella and Job, 2001) was 4911 out of 1 million occurrences (SD ¼ 6085; range 2–25,438). There was one practice block of five trials and one experimental block of 30 trials. Randomisation was fixed across participants. The participant’s task was to name the syllable as quickly as possible. Mean vocal RTs to correctly named syllables were measured.

2.4.3.

Test 3: sequential identity letter judgement

A list of 40 upper case letter pairs was used. The two letters were displayed sequentially. The size of the letters was the same as in Test 1. They were arranged vertically, the first letter above and the second letter below a fixation cross, with a vertical gap of .8 between the two stimuli. There were 20 sequential pairs consisting of identical letters (AA, BB, etc.) plus 20 pairs consisting of different letters (AD, BQ, etc.). Twenty different letters were used to generate both the ‘‘same’’ and ‘‘different’’ trials. One practice block of 10 trials and one experimental block of 40 trials

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were given. Randomisation was fixed across participants. The first stimulus was displayed for 150 msec, immediately followed by the second stimulus, which remained on the screen until the participant’s response (or a 4000-msec time limit). The participant’s task was to decide whether the second letter was the same as or different from the previous one by pressing one of the two response keys as quickly as possible. Mean RTs to correct same and different letter pairs were measured.

2.4.4. Test 4: identity judgement of simultaneous letter pairs varying for case Stimuli were 48 bigrams not corresponding to a syllable (both letters were consonants). The size of the bigram was the same as in Test 2. The letters of each bigram could be either the same or different. Each bigram could be printed in upper case (BB, CD), lower case (bb, cd) or mixed case, with the lower case letter either presented first (bB, cD) or second (Bb, Cd). There were 4 bb-type, 4 BB-type, 8 bB, 8 Bb, 6 bc, 6 BC, 6 bC, and 6 Bc trials. There was one practice block of 10 trials and two experimental blocks of 24 trials each. The stimulus remained on the screen until the participant responded (or until a 4000 msec time limit expired). The participant’s task was to decide whether the two letters were the same or different, irrespective of case type, by pressing one of the two response keys as quickly as possible. The combination of two response types (YES–NO), four stimulus types (lower case, upper case, lower/upper case, upper/lower case) yielded a total of eight measures of performance.

2.4.5.

(N-size), bigram frequency and orthographic complexity (Burani et al., 2006). Sixty non-words were generated from the word set by changing one or two letters. Non-words were matched with words for length in letters (subtending the same visual angle), bigram frequency, orthographic complexity and initial phoneme. Words and non-words were presented mixed within the same block of trials; the order of the trials within each block was randomised. The participant was instructed to read aloud as fast and accurately as possible the stimuli that appeared in the centre of the computer screen. This task yielded eight performance measures: mean RTs to 4-, 5-, 6and 7-letter words and non-words.

2.5.

Procedure

Tests 1–4 were administered in one experimental session. The order of presentation of the tasks was counterbalanced across participants. Each block was preceded by a brief practice block and followed by a short pause. Test 5 was administered in one experimental session. A practice block of 10 trials preceded the test. Apart from the naming letters test, the items used for the practice blocks in each test were different from the items used for the experimental trials but had the same characteristics as the experimental items.

3.

Results

3.1.

Testing the RAM predictions

Test 5: reading words and non-words

Sixty words were used. Mean child written word frequency was 290.7 (SD ¼ 510.9; range ¼ 49–3584) in 1 million occurrences, drawn from the LEXVAR database (Barca et al., 2002). Words were 4-, 5-, 6- and 7-letters long (disyllabic and trisyllabic). All words were stressed with the most frequent stress in Italian (on the penultimate syllable). There were 15 items in each length condition. The four length conditions were matched for frequency, rated age of acquisition (AoA), familiarity, imageability, number of orthographic neighbours

Faust et al. (1999) predicted various linear relationships to define the presence of global components in the data. Here, we first tested the prediction of a linear relationship between the means of the two groups for conditions that varied in overall information-processing rate. Dyslexics’ and skilled readers’ condition means are plotted against each other in Fig. 1a. In the graph, a diagonal dotted line is plotted. Points lying on the

Fig. 1 – Test of RAM predictions based on results of dyslexics and skilled readers in several experimental conditions (specified in the middle inset): a) dyslexics’ condition means are plotted as a function of skilled readers’ means. Open circles report RTs for letter and bigram tasks (conditions 1–12). Filled circles report RTs for word and non-word tasks (conditions 13–20). The dotted line (slope [ 1) represents equal RTs for dyslexics and skilled readers. b) SDs across individuals (dyslexics and skilled readers) are plotted as a function of overall group means for the same conditions.

cortex 46 (2010) 1272–1283

diagonal indicate identical performance of the two groups and points above and below the line worse performance of dyslexics and skilled readers, respectively. Therefore, since all data points lie above the diagonal dotted line, dyslexics were slower than skilled readers in all conditions. Various observations can be made about this graph. First, it should be noted that in both groups of children there was a large variation in response time across conditions both in the case of the letter/bigram tasks (data points range from 618 msec to 1120 msec and from 537 msec to 927 msec for dyslexics and skilled readers, respectively) and in the case of the words/non-words reading (data points range from 702 msec to 1036 msec and from 563 msec to 701 msec for dyslexics and skilled readers, respectively). This large variation is a pre-requisite for the detection of global components in the data according to the RAM. Second and most critically, inspection of the figure indicates that the data can be best described by two linear relationships, one (plotted by filled circles) accounting for all word and non-word conditions (y ¼ 2.09x  447.3; r2 ¼ .96) and one (plotted by open circles) for all the conditions involving letters and bigrams (y ¼ 1.22x  17.3; r2 ¼ .98). In contrast, a solution with a single regression line yields a lower coefficient of determination (r2 ¼ .77). Third, the deficit of dyslexics was more marked in the case of the word/non-word conditions with a slope of b ¼ 2.09 (i.e., dyslexics were slower than skilled readers by about a factor of two, corresponding to a 109% difference) than in the case of the letter–bigram conditions (b ¼ 1.22, i.e., dyslexics were slower than skilled readers by 22%). Successively, we tested the prediction of a linear relationship between overall group means and SDs across individuals in the same conditions. To this aim, in Fig. 1b, we plotted the condition means of the total sample against the SDs of the same conditions. Note the general tendency for more difficult conditions to be associated with larger variability (SD) values. Again the best fit for the data is with two regression lines, one accounting for the word–non-word conditions (y ¼ .7x  296.7; r2 ¼ .97) and one for letter–bigram conditions (y ¼ .4x  134.1; r2 ¼ .94); the proportion of explained variance decreased when a single regression line was used to account for all the data (r2 ¼ .82).

3.1.1.

Comments: ‘‘letter’’ and ‘‘letter-string’’ global factors

Following Faust et al. (1999), we tested the presence of one or more global components in the data employing a variety of tasks/stimuli conditions. The results indicated that there are two separate global influences, one accounting for performances in the conditions with words and non-words (hereafter, called ‘‘letter-string’’ factor) and one accounting for performances on all letter–bigram tasks (hereafter, ‘‘letter’’ factor). It is noteworthy that the letter–bigram conditions did not always yield faster RTs than the word/ non-word reading task. In fact, when the task involved matching single letters (either simultaneous or sequential), processing times could be longer than in the case of reading a word, also in dyslexics. Therefore, the seemingly smaller speed deficit of dyslexics in the letter–bigram tasks cannot be attributed to a bias due to a difference in the overall difficulty of the tasks.

3.2.

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Testing for the presence of specific factors

The prediction tests (see Section 3.1) refer to large-scale components in performance and they do not exclude that the two critical groups are further discriminated by small-scale specific factors. To this aim, Faust et al. (1999) suggest comparing parametric analyses (such as ANOVAs) on raw versus transformed data. Interactions with the group factor, which were significant in both the raw score and transformed score analyses, indicate the selective influence of a given parameter; in contrast, interactions that were significant only in the raw data, but not on transformed values, indicate the presence of a spurious interaction (over-additivity effect). Therefore, raw data were transformed into z-scores by taking each individual’s condition means, subtracting their overall mean and dividing it by the SD of their condition means. z-scores indicate an individual participant’s performance in a given condition relative to all other conditions based on the individual means of all conditions (therefore, each individual has an average of 0 across conditions and an SD ¼ 1). This transformation re-scales individual performances to a common reference; hence, it allows controlling for global components while it preserves the information regarding individual variability across experimental conditions. Since two different global components were observed in the data, we carried out the z-score transformation twice, once for all letter–bigram tasks and once for all word and nonword conditions. It should be noted that these transformations may be applied to open scales, such as time, but they are not suited in the case of closed scales, such as accuracy. Consequently, analyses in this paper mostly focus on time measures. However, based on inspection of the data we found no indication of trade-offs between RTs and accuracy for any of the experimental conditions.

3.3.

Letter–bigram conditions

In the raw data analyses, dyslexics were slower than skilled readers in both Test 1 (naming letters: t(52) ¼ 4.78, p < .0001) and Test 2 (reading syllables: t(52) ¼ 5.11, p < .0001). In Test 3, dyslexics were slower than skilled readers in matching single letters [F(1,52) ¼ 12.17, p < .001]; same responses were faster than different responses [F(1,52) ¼ 29.39, p < .0001], with no group by response type interaction. In Test 4 (identity judgement), dyslexics were slower than skilled readers in matching bigrams [F(1,52) ¼ 9.92, p < .005] and same responses were faster than different responses [F(1,52) ¼ 128.57, p < .0001]. The stimulus type effect [F(3,156) ¼ 50.73, p < .0001] indicated faster responses for upper (774 msec) than lower case (881 msec, p < .0001, Tukey’s honest significant difference test – HSD test) and the two mixed case conditions (lower case first: 901 msec, p < .0001; upper case first: 897 msec, p < .0001). The response type by stimulus type interaction was significant [F(3,156) ¼ 45.89, p < .0001]: ‘‘different’’ responses were slower than ‘‘same’’ responses for the upper case and lower case stimuli (p < .0001) but not for the two mixed case conditions. Note that the raw data analyses showed no group by condition interactions. When the same analyses were carried out on z-scores, the effect of group washed out in all cases, including both t-test

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comparisons (Tests 1 and 2) and ANOVAs (Tests 3 and 4).2 The main effects of task were replicated. In both Tests 3 and 4, the same responses were faster than the different responses [F(1,52) ¼ 24.4, p < .0001; and F(1,52) ¼ 145.7, p < .0001, respectively]. In Test 4, the stimulus type effect was significant [F(3,156) ¼ 58.7, p < .0001] and the decomposition of the effect was similar to that of raw data. The response type by stimulus type interaction was significant [F(3,156) ¼ 60.29, p < .0001]: same responses were better than different responses for the upper case, lower case, and one of the mixed cases (lower case as the second letter) stimuli (all ps at least <.01).

3.3.1. Comments: absence of specific effects in letter processing Dyslexics were slower than skilled readers across all tests involving letter processing, including naming and same– different judgements. These group differences washed out in the case of the z-scores analyses, indicating that they are entirely explained by the global ‘‘letter’’ factor. The raw analyses did not show any group by condition interactions. Consequently, no critical test of the specificity assumption was carried out in these analyses. Clearly, the manipulations tested (one letter vs two letters; naming vs matching; lower vs upper case, etc.) did not impose a specific additional load on the dyslexic children over and above their overall slowness in processing letters. Furthermore, the relatively small slope (b ¼ 1.2) accounting for the ‘‘letter’’ factor was presumably not large enough to yield spurious interactions with the group factor in the ANOVAs on single task performances.

3.4.

Word and non-word conditions

In the ANOVA on raw data, the main effects of group [F(1,52) ¼ 41.15, p < .001], lexicality [F(1,52) ¼ 137.25, p < .001] and length [F(1,52) ¼ 77.42, p < .001] were significant, indicating slower RTs for dyslexics, non-words and longer stimuli, respectively. The lexicality by length interaction [F(3,156) ¼ 27.62, p < .001] indicated a more pronounced effect of length on nonwords than on words. All interactions involving the group were significant (at least p < .05). In particular, dyslexics showed a greater effect of length [F(3,156) ¼ 16.27, p < .001; see Fig. 2a] and a greater effect of lexicality [F(1,52) ¼ 12.27, p < .001; see Fig. 2b]. The group by length by lexicality interaction [F(3,156) ¼ 3.19, p < .05] indicated that the greater length effect for non-words was more marked in dyslexics than skilled readers. The ANOVA on z-scores indicated main effects of lexicality [F(1,52) ¼ 1515.80, p < .001] and length [F(1,52) ¼ 136.30, p < .001]. The group by length interaction was significant [F(3,156) ¼ 4.80, p < .005; see Fig. 2c], indicating more marked length effects for dyslexics than skilled readers. The group by lexicality 2

According to the RAM, the critical comparison is between group by condition interaction in raw versus transformed data and the group effect is expected to be nil in the z-score analyses. However, since the data transformation is carried out across a large number of conditions, residual effects of the group effect may indeed be found. These would indicate that the group is selectively impaired in that specific test as compared to all others. In this sense, also Student t comparisons are informative of the potential presence of selective task influences moderating the group difference.

interaction was significant [F(1,52) ¼ 10.22, p < .005; see Fig. 2d]. This indicated an opposite effect to that of raw RTs: dyslexics showed a less marked lexicality effect than skilled readers; the difference between words and non-words was 1.33 z units in skilled readers and 1.13 in dyslexics. The group by length by lexicality interaction was not significant.

3.4.1.

Comments: specific length and lexicality effects

The analysis of raw data for word and non-word conditions indicated greater effects of lexicality and length (and their interaction) in dyslexics than typically developing readers, a pattern of results that confirms previous observations (Ziegler et al. 2003; Zoccolotti et al., 2008; see also Marinus and de Jong, 2010, this issue). The analyses using z-score transformation allowed determining whether small-scale specific factors accounted for the difference between skilled readers and dyslexics over and above the large-scale difference in reading words and nonwords. When examined in terms of z-scores, the interaction between group and length remained significant, indicating a residual specific role of stimulus length over and above the influence of the global factor in performance. This finding closely confirms previous evidence (Zoccolotti et al., 2008). The results for the lexicality effect yielded opposite patterns in the two analyses. In the raw data ANOVA, the difference between words and non-words was larger in dyslexics; in the zscore analysis (which cancels out the over-additivity effect), the direction of the effect was the opposite, i.e., dyslexics had slightly less marked lexicality effects than skilled readers. This latter result points to a specific difficulty of dyslexics in the case of words (i.e., inefficiency in accessing/retrieving items from the orthographic lexicon) and to a spared ability in mastering the grapheme-to-phoneme conversion rules (known to be critical in non-word reading), a pattern generally consistent with surface dyslexia. In previous research, we proposed that the profile of Italian dyslexics presents several similarities with that of surface dyslexia (Zoccolotti et al., 1999). As stated above, also the profile of reading behaviour in the present group of dyslexics in reading a text passage indicates a prevalent reliance on the grapheme-to-phoneme conversion routine. In a previous study (Zoccolotti et al., 2008), the group by lexicality interaction washed out in the z-score analysis; i.e., the effect was entirely explained in terms of a global factor. This discrepancy may be due to the difference in stimuli selection. In that previous study, words of different length were matched only for frequency and initial phoneme and nonwords only for initial phoneme. In the present study, an additional set of variables was taken into account (words were also matched for rated AoA, familiarity, imageability, N-size, bigram frequency and orthographic complexity; non-words were also matched for N-size, bigram frequency, orthographic complexity and initial phoneme). An experimental condition characterized by better stimulus matching, such as the one carried out here, may be more sensitive in capturing a relationship that runs counter to what emerged in the analyses of the raw data. Therefore, the present results raise the interesting possibility that, contrary to what has been claimed most often in the case of English-speaking children (e.g., Rack et al., 1992), the difference in reading non-words versus words is actually less marked in Italian dyslexics than in skilled readers.

cortex 46 (2010) 1272–1283

1279

Fig. 2 – Group by length (a, c) and group by lexicality (b, d) interactions are presented separately for raw (RTs) and z-transformed data: a) RTs of dyslexics and skilled readers for naming strings of letters (both words and non-words) as a function of string length. In this and the following graphs, confidence intervals (p < .05) are reported. b) RTs of dyslexics and skilled readers for naming words and non-words. c) Performances of dyslexics and skilled readers in naming string of letters (words and non-words) as a function of string length. Positive values indicate better performance. d) Performances of dyslexics and skilled readers in naming words and non-words. Positive values indicate better performance.

Overall, in the case of words and non-words, raw RTs indicate generally larger effects in dyslexics. After controlling for over-additivity, the length effect remained significant, indicating an important role of length in modulating the reading deficit; the lexicality effect reverted with respect to the raw data; i.e., dyslexics had smaller lexicality effects than skilled readers.

3.5.

Individual differences

One potentially interesting question concerns the presence of individual differences regarding impairment in the ‘‘letter’’ and the ‘‘letter-string’’ factors, respectively. Kail and Salthouse (1994) proposed that a single parameter (the slope of the linear function) can effectively capture the individual variation in performance on a given global factor (see Eq. (3), p. 208). Accordingly, the condition means of the skilled readers were regressed on those of each individual dyslexic to compute the slope for each individual dyslexic. This calculation was carried out separately for the conditions characterising the ‘‘letter’’ factor and for those characterising the ‘‘letter-string’’ factor. The mean slope was b ¼ 2.10 (SD 1.43; range ¼ .72–5.89) for the ‘‘letter-string’’ factor and b ¼ 1.22 (SD ¼ .52; range ¼ .63–2.72) for the ‘‘letter’’ factor. The two sets of values were not correlated (r ¼ .09, n.s.). We also correlated the individual slopes for the two factors with the reading parameters (speed and accuracy) in the MT

test. The slope on the ‘‘letter-string’’ factor correlated highly with reading speed (r ¼ .69, p < .001): dyslexics with steeper slopes showed slower reading times. It also correlated with reading accuracy (r ¼ .48, p < .05), with dyslexics with steeper slopes making more errors than skilled readers. The slopes on the ‘‘letter’’ factor did not correlate with either reading speed (r ¼ .24, n.s.) or accuracy (r ¼ .21, n.s.). The slope of a dyslexic child’s performance in reading words and non-words captures the degree of severity of the reading impairment (in this vein, note that slope values varied considerably across dyslexics). The presence of a high correlation between individual slopes and reading speed (and accuracy) in a standard reading test is also consistent with this idea. It is noteworthy that, in the case of the ‘‘letter’’ factor, the individual slopes were not related to performance in reading (and inter-individual variation in slope values was also much smaller). Importantly, the two sets of slopes were entirely uncorrelated suggesting that the ‘‘letter’’ and ‘‘letterstring’’ factors are independent.

4.

Discussion

With respect to their typically developing peers, dyslexics had slower processing times in naming single letters, syllables and longer letter strings. These results show that Italian dyslexics

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are slower in processing orthographic material even at the single letter level. Interestingly, performances were slower not only in letter naming but in all tasks involving letters or bigrams. Matching was also slower, and this holds for a variety of stimuli (letters, pronounceable syllables, and bigrams not corresponding to Italian syllables). Performances in these conditions rely on different cognitive operations. Letter naming requires retrieval of the letter name; syllable reading probes the ability to convert graphemes into phonemes and is the closest of these tasks to actual reading. Matching sequentially presented letters requires the storage and retrieval of letter shapes. Finally, comparing same-case letters can be solved visually (‘‘physical’’ match in Posner’s terminology); however, judging the identity of letters that vary for case requires access to abstract grapheme representations (i.e., ‘‘name’’ matching; Posner, 1978). Yet, the main finding of the present study was that all letter conditions contributed to the ‘‘letter factor’’ and that no role for other specific components in accounting for the group differences in performance was detected. Thus, the requirements specific to each task do not appear relevant; i.e., letter naming versus visual matching, physical versus letter name, etc. Thus, it can be concluded that the deficit is located at a level of processing shared across these conditions. Visual identification of letters seems to be the basic processing common to all these conditions. Even though dyslexics were slow in all tasks involving naming or matching of either letters or bigrams, the ‘‘letter’’ factor accounting for all these performances was clearly distinguished from the ‘‘letter-string’’ factor. Importantly, this distinction of factors cannot be a spurious phenomenon. In fact, it cannot be ascribed to differences in the general difficulty of the tasks. For both groups of children, letter recognition tasks varied considerably in terms of processing time and yet are accounted for very accurately by a single regression line with a slope of b ¼ 1.22. As expected, dyslexics had slower vocal RTs than skilled readers in reading strings of letters (both words and nonwords). The data were well fitted by a single regression line with a slope of b ¼ 2.09. Thus, the mean difference with respect to skilled readers was large in size, more than 100% (as compared to the ca. 20% difference in the case of the letter/ bigram tasks). Based on previous research (Zoccolotti et al., 2008) and the present evidence, a tentative interpretation of the global factor accounting for performance in words and non-words can be sketched. First, it is selective for orthographic material: it does not include performances in picture naming tasks (Zoccolotti et al., 2008). Second, it refers to pronounceable orthographic strings independent of whether or not they represent entries in the lexicon: this indicates a pre-lexical locus of action of the factor (Zoccolotti et al., 2008; present work). Third, strings must be relatively ‘‘long’’ (present study). Tasks of letter/bigram recognition do not contribute to the same dimensional factor as words and non-words. It should be added that, even after controlling for the global ‘‘letter-string’’ factor, length still had a significant influence on the dyslexics’ performance. Therefore, both the nature of the global factor, i.e., strings of four or more letters, and the presence of the residual specific effect of length call for the action of mechanisms closely linked to the number of elements in the orthographic string. The exact nature of this letter-string

effect is not yet defined. As stated above, evidence indicates a pre-lexical locus; however, it is not clear whether this effect should be ascribed to visual or phonological defects; a selective deficit in integrating visual and phonological codes has also been suggested (Rusiak et al., 2007). In the Introduction, we mentioned the possible role of crowding as a visual mechanism specifically sensitive to stimulus length. The crowding hypothesis predicts perceptual difficulty in dealing with a letter string fixated in central vision when letters to be recognised are flanked by other letters (i.e., starting from three-letter strings). Dyslexics should have enhanced crowding (Atkinson, 1991; Bouma and Legein, 1977; Martelli et al., 2009; O’Brien et al., 2005; Spinelli et al., 2002) both in the fovea and in the periphery, where portions of long-string stimuli impinge. Interestingly, the crowding mechanism is not expected to be active (or is minimal) when a target is presented in the absence of flankers, as in the case of single letter (or bigram) tasks. A main question of the study concerns the relationship between performance in the letter–bigram tasks and the reading deficit. Dyslexic children showed small, but reliable, deficits in letter tasks. However, the existence of two different regression lines for performances in tasks with single letter/ bigram and tasks with strings of letters militates against the idea that defective letter recognition is a core part of their reading disturbance (if this were the case, one single regression line would have fit all data points well). Further, analysis of individual performances indicated that deficits in the two sets of tasks were entirely uncorrelated, confirming that the factors contributing to letter and letter-strings processing are distinct. Overall, deficits in letter and letter-string tasks appear to point to the derangement of independent mechanisms. Admittedly, the origin of the letter deficit is not clear. A general speed processing interpretation appears unlikely in view of Bonifacci and Snowling’s (2008) results. Alternatively, since dyslexics have less practice with orthographic materials, this may well be sufficient to generate the group difference that was observed. In this vein, it is well known that performances on simple, automatized tasks change with experience (e.g., Pelli et al., 2006). In the visual domain, recent studies on letter recognition have shown improvements of performance across years of practice, reaching adult performance late in development (e.g., Giaschi and Regan, 1997; Martelli et al., 2002). Further research is certainly needed; however, based on the available information, we propose the working hypothesis that the performance of the group of dyslexics on letter– bigram tasks is limited by practice, while performance on longer letter strings could be limited by crowding. Some conditions should be taken into account with respect to this hypothesis: first, the present results refer to a transparent orthography; further research is needed to extend them to less regular orthographies. Second, the group of dyslexics was relatively homogenous and no clear-cut differences in the individual profiles of reading behaviour were apparent. However, it is well established that there are different forms of reading disturbance (Temple, 2006). Therefore, while the observed pattern is presumably prevalent among Italian children, the present results do not exclude the possibility that specific children may have problems at the single letter level. After a few decades of intensive empirical research, much has been established on developmental reading deficits.

(2.7) (.1) 80.8 .4

84.3 .04

(SD)

Relative % of error

84.5 0 80.8 0 86.3 0 80.8 0 88.2 0 86.7 0 86.7 0 82.3 0 85.6 0 87.8 .4 83 0 83 0 88.2 0 80.1 0 82.7 0 84.9 0 84.5 0 Correct utterances C1: correct U; repetitions of correct U C2: more or less correct U, slightly deviating from the standard Italian form (dialectal variant)

.39 78 .36 64 .3 36 .41 86 .32 45 .43 95 .51 132 .25 14 .36 64 .5 129 .38 73 .54 144 .3 35 .44 98 .32 47 .59 166 .39 77 .35 57

AA PF RS BS DI DG BG SV GF SF PA PG VF LP PG FA PJ

Participants

Mean

(.09) (42)

(SD) Mean SC

.33 50 Response category and description

This study was supported by a grant from Italian Department of Health. Cristina Burani, Despina Paizi and Pierluigi Zoccolotti are members of the Marie Curie Research Training Network: ‘‘Language and Brain’’ (RTN:LAB; European Commission, MRTN-CT-2004-512141).

Speed Seconds per syllable Reading time increase (percentage)

Acknowledgments

Appendix A Individual data of dyslexic participants in the MT reading test (Cornoldi and Colpo, 1995). The first two rows report the reading time (sec/ syllable) and the reading time increase (in percentages with respect to the control group of the present study). The following rows report the percentages of utterances (U) according to 24 categories (for an in-depth description of the categories, see Hendriks and Kolk, 1997). Mean (and SD) performances are also reported. The last column reports the mean percentage of errors for each category based on the total number of errors (sum of all categories except correct utterances).

Nevertheless, most research has focused on a single or a few experimental manipulations. The resulting large body of evidence does not easily fit into a single coherent framework and there is some difficulty as to which stimuli/tasks generate the impairments that represent the core symptoms of the disturbance. To this end, we used the approach of varying several stimuli/task parameters to identify the conditions that allow for systematic dimensional coherence in comparing dyslexics’ and skilled readers’ performances. Here, we have confirmed previous evidence (Zoccolotti et al., 2008) that performance in reading words and non-words is explained by a global factor (i.e., the ‘‘letter-string’’). This accounts for the large-scale difference that distinguishes dyslexics from skilled readers. Further, we have shown that tasks requiring letter recognition do not probe the same factor (even though they do generate reliable group differences). These findings are in keeping with the crucial role of stimulus length in modulating the reading disturbance. Therefore, we believe that the approach described here is able to isolate the conditions that have a core relationship with the disorder. Indeed, this may prove effective in bridging behavioural studies on developmental dyslexia to those using different methodologies, such as brain imaging and psychophysics. Recent imaging studies in reading have focussed on the role of the fusiform gyrus in the left occipito-temporal junction, a region often referred to as the visual word form area (VWFA; Cohen et al., 2000, 2002). Interestingly, these studies have shown that the VWFA is activated by the presentation of both words and non-words while it is not activated (or minimally activated) by the presentation of single letters. In fact, it has been demonstrated that, different from orthographic strings, letter processing activates areas anterior to the VWFA (James and Gauthier, 2006; James et al., 2005). Research on developmental dyslexics has shown that these children have a marked underactivation of VWFA (for a presentation of these results see Wimmer et al., 2010, this issue). Psychophysical research in good readers has demonstrated that reading speed is limited by the number of letters that can be acquired within each fixation (visual span, Legge et al., 2001). Recently, Pelli et al. (2007) proposed that the visual span is simply the number of characters that are not crowded (uncrowded span) and that reading rate is proportional to the uncrowded span. The uncrowded span would thus be the sole determinant of reading speed. Studies from these different perspectives appear to converge on the critical role of the perceptual integration processes (presumably mediated by areas in the left occipital-temporal lobe) allowing the mastery of strings of orthographic symbols. Merging evidence from these different approaches may be the key to the understanding of developmental reading disturbances.

(continued on next page)

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Appendix A (continued) Participants PJ

FA

PG

LP

VF

PG

PA

SF

GF

SV

BG

DG

Sounding-out behaviour S1: with correct U S2: with stress error S3: with phonetization error S4: with non-word response S5: residuals; other responses

4.8 0 0 0 0

5.5 0 0 0 0

8.1 0 0 0 .4

5.2 0 0 .4 0

5.5 0 0 0 .4

5.9 0 0 0 .4

4.4 0 0 .4 0

1.1 0 0 0 0

6.3 0 0 0 0

7.7 0 0 0 0

4.8 0 0 0 0

3.3 0 0 0 0

Word substitutions W1: visual error W2: semantic error W3: context error W4: visual–semantic error W5: visual–context error W6: function word substitution W7: derivational error W8: residual error

1.5 0 0 0 1.8 1.5 1.1 0

.4 0 0 0 1.5 1.8 1.5 0

.4 0 0 0 .4 1.1 1.8 0

.7 0 0 0 1.5 .7 4.4 0

1.1 0 0 0 .7 1.5 .7 0

1.5 0 0 0 1.1 1.1 3 0

1.1 0 0 0 .4 2.2 3.7 0

1.1 0 0 .4 1.8 1.8 3.3 0

.7 0 0 .4 .7 .4 1.1 .4

3 0 0 0 1.1 1.8 1.1 0

.4 0 0 .4 .7 1.8 .4 0

0

0

0

0

0

0

0

0

0

.4

0 2.2 0

0 1.1 0

0 1.5 0

0 3 0

0 .4 0

0 .7 0

0 1.5 0

0 .4 0

0 2.6 0

.4 0 0

1.1 0 0

0 1.8 0

.4 .7 0

.4 .4 0

0 0 0

0 0 0

1.5 0 0

0

0

0

.4

0

0

0

2.2

2.2

1.8

2.6

.7

3.3

3.3

Residual responses R1: U has wrong assignment of stress, but is correct otherwise R2: U contains a phonetization error R3: U is a visually related non-word R4: U is a non-word that is not visually related R5: word omission R6: word addition R7: word transposition: different order of words R8: ambiguous U; sounding-out behaviour with word substitution. R9: ambiguous U

(SD)

DI

BS

RS

PF

AA

3 0 0 0 0

9.2 0 0 0 0

6.6 0 0 0 0

9.2 0 0 0 0

4.4 0 0 0 0

7.7 0 0 0 0

5.7 0 0 .04 .1

(2.2) (0) (0) (.1) (.1)

36.5 0 0 .3 .4

0 0 0 0 .0 4.1 1.8 0

.4 0 0 0 .7 1.1 2.6 0

1.1 0 0 0 1.1 .7 1.5 0

.7 0 0 0 .0 1.1 1.5 0

.4 0 0 0 .7 1.1 3.7 0

.7 0 0 0 1.8 1.1 4.1 0

.7 0 .4 0 .7 2.6 1.1 0

.9 0 .02 .1 .9 1.5 2.1 .02

(.7) (0) (.1) (.1) (.6) (.8) (1.3) (.1)

5.6 0 .1 .4 6 9.8 13.6 .1

.4

.4

.4

0

.4

.4

.4

.4

.2

(.2)

1

0 2.6 0

0 3.7 0

0 1.5 0

0 3 0

0 3.7 0

0 1.8 0

0 1.1 0

0 1.5 0

0 4.8 0

0 2.1 0

(0) (1.2) (0)

0 13.1 0

.4 .4 0

0 0 0

0 0 0

1.1 .4 0

.4 .4 0

0 0 0

.7 .4 0

1.1 .7 0

.4 0 0

0 0 0

.4 .3 0

(.5) (.5) (0)

2.7 1.8 0

0

0

0

0

0

0

0

0

0

0

0

.02

(.1)

.1

.4

1.1

0

.7

.7

0

1.8

.4

.7

1.1

.4

1.3

(1.1)

8.4

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SC

Mean

cortex 46 (2010) 1272–1283

references

Anderson M. Intelligence and Development: a Cognitive Theory. Oxford, UK: Blackwell, 1992. Atkinson J. Review of human visual development: Crowding and dyslexia. In Stein JF (Ed), Vision and Visual Dyslexia. Houndmills: MacMillan Press, 1991: 44–77. Barca L, Burani C, and Arduino LS. Word naming times and psycholinguistic norms for Italian nouns. Behavior Research Methods, Instruments & Computers, 34: 424–434, 2002. Available at: http://www.istc.cnr.it/material/database/lexvar.shtml. Bonifacci P and Snowling MJ. Speed of processing and reading disability: A cross-linguistic investigation of dyslexia and borderline intellectual functioning. Cognition, 107: 999–1017, 2008. Bouma H and Legein CHP. Foveal and parafoveal recognition of letters and words by dyslexics and by average readers. Neuropsychologia, 15: 69–80, 1977. Burani C, Barca L, and Ellis AW. Orthographic complexity and word naming in Italian: Some words are more transparent than others. Psychonomic Bulletin and Review, 13: 346–352, 2006. Cohen L, Dehaene S, Naccache L, Lehe´ricy S, Dehaene-Lambertz G, He´na VMA, et al. The visual word form area. Spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain, 123: 291–307, 2000. Cohen L, Lehe´ricy S, Chochon F, Lemer C, Rivaud S, and Dehaene S. Language-specific tuning of visual cortex? Functional properties of the visual word form area. Brain, 125: 1054–1069, 2002. Coltheart M. Disorders of reading and their implications for models of normal reading. Visible Language, 15: 245–286, 1981. Coltheart M. Functional architecture of the language-processing system. In Coltheart M, Sartori G, and Job R (Eds), The Cognitive Neuropsychology of Language. Hove, London: Erlbaum, 1987: 1–25. Cornoldi C and Colpo G. Nuove prove di lettura MT per la scuola media inferiore, manuale. Firenze: O.S. Organizzazioni Speciali, 1995. Denckla MB and Rudel R. Rapid ‘‘automatized’’ naming of pictured objects, colors, letters and numbers by normal children. Cortex, 10: 186–202, 1974. Denckla MB and Rudel RG. Rapid ‘‘automatized’’ naming (R.A.N): dyslexia differentiated from other learning disabilities. Neuropsychologia, 14: 471–479, 1976. Faust ME, Balota DA, Spieler DH, and Ferraro FR. Individual differences in information-processing rate and amount: Implications for group differences in response latency. Psychological Bulletin, 125: 777–799, 1999. Friedmann N, Kerbel N, and Shvimer L. Developmental attentional dyslexia. Cortex, 46(10): 1216–1237, 2010. Friedmann N and Rahamim E. Developmental letter position dyslexia. Journal of Neuropsychology, 1: 201–236, 2007. Giaschi D and Regan D. Development of motion-defined figureground segregation in preschool and older children, using a letteridentification task. Optometry and Vision Science, 74: 761–767, 1997. Hendriks AW and Kolk HJ. Strategic control in developmental dyslexia. Cognitive Neuropsychology, 14: 321–366, 1997. Jackson N and Coltheart M. Routes to Reading Success and Failure. Hove: Psychology Press, 2001. James KH and Gauthier I. Letter processing automatically recruits a sensory–motor brain network. Neuropsychologia, 44: 2937–2949, 2006. James KH, James TW, Jobard G, Wong ACN, and Gauthier I. Letter processing in the visual system: Different activation patterns for single letters and strings. Cognitive, Affective and Behavioral Neuroscience, 5: 452–466, 2005. Judica A, De Luca M, Spinelli D, and Zoccolotti P. Training of developmental surface dyslexia modifies performance and eye fixation duration in reading. Neuropsychological Rehabilitation, 12: 177–197, 2002.

1283

Kail R and Salthouse TA. Processing speed as a mental capacity. Acta Psychologica, 86: 199–225, 1994. Legge GE, Mansfield JS, and Chung STL. Psychophysics of reading: XX. Linking letter recognition to reading speed in central and peripheral vision. Vision Research, 41: 725–743, 2001. Marinus E and de Jong PF. Variability in the word-reading performance of dyslexic readers: Effects of letter length, phoneme length and digraph presence. Cortex 46(10): 1259–1271, 2010. Martelli M, Baweja G, Mishra A, Chen I, Fox J, Majaj NJ, et al. How efficiency for identifying objects improves with age. Perception, 31: 111, 2002 (supplement). Martelli M, Di Filippo G, Spinelli D, and Zoccolotti P. Crowding, reading and developmental dyslexia. Journal of Vision, 9: 1–18, 2009. O’Brien BA, Mansfield JS, and Legge GE. The effect of print size on reading speed in dyslexia. Journal of Research in Reading, 28: 332–349, 2005. Pelli DG, Burns CW, Farell B, and Moore-Page DC. Feature detection and letter identification. Vision Research, 46: 4646–4674, 2006. Pelli DG, Farell B, and Moore DC. The remarkable inefficiency of word recognition. Nature, 423: 752–756, 2003. Pelli DG, Palomares M, and Majaj NJ. Crowding is unlike ordinary masking: Distinguishing feature integration from detection. Journal of Vision, 4: 1136–1169, 2004. Pelli DG, Tillman KA, Freeman J, Su M, Berger TD, and Majaj NJ. Crowding and eccentricity determine reading rate. Journal of Vision, 7: 1–36, 2007. Posner MI. Chronometric Explorations of Mind. Hillsdale, NJ: Lawrence Erlbaum Associates, 1978. Pruneti C, Fenu A, Freschi G, Rota S, Cocci D, Marchionni M, et al. Aggiornamento della standardizzazione italiana del test delle Matrici Progressive Colorate di Raven (CPM). Bollettino di Psicologia Applicata, 217: 51–57, 1996. Rack JP, Snowling MJ, and Olson RK. The nonword reading deficit in developmental dyslexia: A review. Reading Research Quarterly, 27: 29–52, 1992. Rosazza C, Appollonio I, Isella V, and Shallice T. Qualitatively different forms of pure alexia. Cognitive Neuropsychology, 24: 393–418, 2007. Rusiak P, Lachmann T, Jaskowski P, and van Leeuwen C. Mental rotation of letters and shapes in developmental dyslexia. Perception, 36: 617–631, 2007. Salthouse TA and Hedden T. Interpreting reaction time measures in between-group comparisons. Journal of Clinical and Experimental Neuropsychology, 24: 858–872, 2002. Sartori G, Job R, and Tressoldi PE. Batteria per la valutazione della dislessia e della disortografia evolutiva. Firenze: Organizzazioni Speciali, 1995. Spinelli D, De Luca M, Judica A, and Zoccolotti P. Crowding effects on word identification in developmental dyslexia. Cortex, 38: 179–200, 2002. Stella V and Job R. Le sillabe PD/DPSS. Una base di dati sulla frequenza sillabica dell’italiano scritto. Giornale Italiano di Psicologia, 28: 633–639, 2001. Available at: http://dpss.psy.unipd.it/files/strumenti.php. Temple CM. Developmental and acquired dyslexias. Cortex, 42: 898–910, 2006. Wimmer H, Schurz M, Sturm D, Richlan F, Klackl J, Kronbichler M, et al. A dual-route perspective on poor reading in a regular orthography: An fMRI study. Cortex, 46(10): 1284–1298, 2010. Ziegler JC, Perry C, Ma-Wyatt A, Ladner D, and Schulte-Ko¨rne G. Developmental dyslexia in different languages: Languagespecific or universals. Journal of Experimental Child Psychology, 86: 169–193, 2003. Zoccolotti P, De Luca M, Di Pace E, Judica A, Orlandi M, and Spinelli D. Markers of developmental surface dyslexia in a language (Italian) with high grapheme–phoneme correspondence. Applied Pyscholinguistics, 20: 191–216, 1999. Zoccolotti P, De Luca M, Judica A, and Spinelli D. Isolating global and specific factors in developmental dyslexia: A study based on the rate and amount model (RAM). Experimental Brain Research, 186: 551–560, 2008.