c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 3

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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

aNeuropsychology Unit, IRCCS Fondazione Santa Lucia, Rome, ItalybInstitute for Cognitive Sciences and Technologies (ISTC – CNR), Rome, ItalycDepartment of Education Sciences in Sport and Physical Activity, University «Foro Italico», Rome, ItalydDepartment of Psychology, Sapienza University of Rome, Italy

a r t i c l e i n f o

Article history:

Received 29 October 2008

Reviewed 27 February 2009

Revised 18 May 2009

Accepted 23 June 2009

Published online 3 July 2009

Keywords:

Developmental dyslexia

Letter recognition

Word/non-word reading

RAM

* Corresponding author. Neuropsychology UnE-mail addresses: m.deluca@hsantalucia.

0010-9452/$ – see front matter ª 2009 Elsevidoi:10.1016/j.cortex.2009.06.007

a b s t r a c t

This study evaluated letter recognition processing in Italian developmental dyslexics and

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

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

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

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

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

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

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

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

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

(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 words (Pelli et al., 2003). Thus, in the presence of a reading

The present study aims to evaluate letter recognition pro-

cessing 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

it, IRCCS Fondazione Sait, maria.deluca@uniromer Srl. All rights reserved

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) demon-

strate 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

nta Lucia, Via Ardeatina 306, 00179 Rome, Italy.a1.it (M. De Luca)..

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 3 1273

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 regu-

larly will make a substantial number of errors in reading single

letters. Therefore, such a task is insensitive and may underes-

timate 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 repeti-

tion, 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 differ-

ences 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 esti-

mate 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 usedreading batteries (e.g., in Italian, the Evaluation of DevelopmentalDyslexia and Dysgraphia by Sartori et al., (1995).

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 information-

processing 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 pro-

cessing 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 contrib-

uting 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 disen-

tangling the contribution of the global and task-specific factors

on individual performance. It is proposed that group differ-

ences 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-addi-

tivity effect. These data transformations allow disentangling

the relative role of global influences from that of task speci-

ficity. 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-to-

interpret 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, refer-

ence 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 differ-

ences 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

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 31274

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 relation-

ship 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 non-

words of varying length (Zoccolotti et al., 2008). Dyslexics were

slower in naming orthographic strings (both words and non-

words) but not in naming pictures corresponding to the same

words. In the raw RTs, the group by lexicality interaction indi-

cated larger RT differences in naming non-words than words in

dyslexics as compared to controls. However, this effect van-

ished when data transformations apt to control for over-addi-

tivity 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, investi-

gating 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 func-

tional 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-to-

phoneme conversion rules; c) a sequential matching of conso-

nant 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 representa-

tion). 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 perfor-

mance in identifying single letters (and bigrams) is not

expected to contribute to the same factor underlying pro-

cessing 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 – SD¼ .3) and a group of 36 (18

girls and 18 boys) skilled readers (mean age: 11.3 years,

SD¼ .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).

Table 1 – Summary of statistics (mean age in years and months, with range in parentheses; no. of male and femaleparticipants), mean scores on Raven’s test (with SD in parentheses), mean z-scores on reading speed, accuracy, andcomprehension (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 11;3 (10;9–11;9) 9 9 30.5 (SD¼ 3.2) �1.3 (SD¼ 1.0) �2.8 (SD¼ .6) .6 (SD¼ .2)

Skilled readers 11;3 (10;6–11;9) 18 18 30.3 (SD¼ 3.1) .5 (SD¼ .4) .0 (SD¼ .5) .4 (SD¼ .4)

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 3 1275

2.2. 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). Compre-

hension 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 ortho-

graphically 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 differ-

ences were also apparent. In particular, although some chil-

dren 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

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 judge-

ments, the participant pressed one of two keys (YES–NO) con-

nected 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 lettersA 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. Ran-

domisation 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 syllablesA 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

(SD¼ 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 judgementA 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 con-

sisting 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

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 31276

were given. Randomisation was fixed across participants. The

first stimulus was displayed for 150 msec, immediately fol-

lowed 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 pairsvarying for caseStimuli 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. Test 5: reading words and non-wordsSixty words were used. Mean child written word frequency

was 290.7 (SD¼ 510.9; range¼ 49–3584) in 1 million occur-

rences, drawn from the LEXVAR database (Barca et al., 2002).

Words were 4-, 5-, 6- and 7-letters long (disyllabic and tri-

syllabic). 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

Fig. 1 – Test of RAM predictions based on results of dyslexics a

(specified in the middle inset): a) dyslexics’ condition means are

report RTs for letter and bigram tasks (conditions 1–12). Filled c

13–20). The dotted line (slope [ 1) represents equal RTs for dys

(dyslexics and skilled readers) are plotted as a function of over

(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-, 6-

and 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

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

nd skilled readers in several experimental conditions

plotted as a function of skilled readers’ means. Open circles

ircles report RTs for word and non-word tasks (conditions

lexics and skilled readers. b) SDs across individuals

all group means for the same conditions.

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 3 1277

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 condi-

tions 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 indi-

cates that the data can be best described by two linear

relationships, one (plotted by filled circles) accounting for

all word and non-word conditions (y¼ 2.09x� 447.3; r2¼ .96)

and one (plotted by open circles) for all the conditions

involving letters and bigrams (y¼ 1.22x� 17.3; r2¼ .98). In

contrast, a solution with a single regression line yields

a lower coefficient of determination (r2¼ .77). Third, the

deficit of dyslexics was more marked in the case of the

word/non-word conditions with a slope of b¼ 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 (b¼ 1.22, i.e., dyslexics were

slower than skilled readers by 22%).

Successively, we tested the prediction of a linear relation-

ship 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 (y¼ .7x� 296.7;

r2¼ .97) and one for letter–bigram conditions (y¼ .4x� 134.1;

r2¼ .94); the proportion of explained variance decreased when

a single regression line was used to account for all the data

(r2¼ .82).

3.1.1. Comments: ‘‘letter’’ and ‘‘letter-string’’ global factorsFollowing 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. 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 perfor-

mance 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

SD¼ 1). This transformation re-scales individual perfor-

mances to a common reference; hence, it allows controlling

for global components while it preserves the information

regarding individual variability across experimental condi-

tions. 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 non-

word 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 indi-

cation 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

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 31278

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, respec-

tively]. 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 letterprocessingDyslexics 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 rela-

tively small slope (b¼ 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 non-

words 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 betweengroup by condition interaction in raw versus transformed dataand the group effect is expected to be nil in the z-score analyses.However, since the data transformation is carried out acrossa large number of conditions, residual effects of the group effectmay indeed be found. These would indicate that the group isselectively impaired in that specific test as compared to all others.In this sense, also Student t comparisons are informative of thepotential presence of selective task influences moderating thegroup 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 effectsThe 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 observa-

tions (Ziegler et al. 2003; Zoccolotti et al., 2008; see also

Marinus and de Jong, 2010, this issue).

The analyses using z-score transformation allowed deter-

mining 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 non-

words. 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 z-

score 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 reli-

ance 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, wordsof different length were

matched only for frequency and initial phoneme and non-

words only for initial phoneme. In the present study, an addi-

tional 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 rela-

tionship that runs counter to what emerged in the analyses of

the raw data. Therefore, the present results raise the inter-

esting 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.

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.

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 3 1279

Overall, in the case of words and non-words, raw RTs indi-

cate generally larger effects in dyslexics. After controlling for

over-additivity, the length effect remained significant, indi-

cating 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 Salt-

house (1994) proposed that a single parameter (the slope of the

linear function) can effectively capture the individual varia-

tion 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 b¼ 2.10 (SD 1.43; range¼ .72–5.89) for

the ‘‘letter-string’’ factor and b¼ 1.22 (SD¼ .52; range¼.63–2.72) for the ‘‘letter’’ factor. The two sets of values were

not correlated (r¼ .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 (r¼�.69, p< .001): dyslexics with steeper

slopes showed slower reading times. It also correlated with

reading accuracy (r¼�.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

(r¼ .24, n.s.) or accuracy (r¼ .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 corre-

lation 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 ‘‘letter-

string’’ 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

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 31280

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 repre-

sentations (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 diffi-

culty of the tasks. For both groups of children, letter recogni-

tion tasks varied considerably in terms of processing time and

yet are accounted for very accurately by a single regression

line with a slope of b¼ 1.22.

As expected, dyslexics had slower vocal RTs than skilled

readers in reading strings of letters (both words and non-

words). The data were well fitted by a single regression line

with a slope of b¼ 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 ortho-

graphic 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 regres-

sion 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 devel-

opment (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 chil-

dren, 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.

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c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 3 1281

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 differ-

ences). 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 psycho-

physics. 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 demon-

strated 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 propor-

tional 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) allow-

ing the mastery of strings of orthographic symbols. Merging

evidence from these different approaches may be the key to

the understanding of developmental reading disturbances.

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Acknowledgments

This study was supported by a grant from Italian Department

of Health. Cristina Burani, Despina Paizi and Pierluigi Zocco-

lotti are members of the Marie Curie Research Training

Network: ‘‘Language and Brain’’ (RTN:LAB; European

Commission, MRTN-CT-2004-512141).

Appendix A (continued)

Participants Mean (SD)

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

Sounding-out behaviour

S1: with correct U 4.8 5.5 8.1 5.2 5.5 5.9 4.4 1.1 6.3 7.7 4.8 3.3 3 9.2 6.6 9. 4.4 7.7 5.7 (2.2) 36.5

S2: with stress error 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0) 0

S3: with phonetization error 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0) 0

S4: with non-word response 0 0 0 .4 0 0 .4 0 0 0 0 0 0 0 0 0 0 0 .04 (.1) .3

S5: residuals; other responses 0 0 .4 0 .4 .4 0 0 0 0 0 0 0 0 0 0 0 0 .1 (.1) .4

Word substitutions

W1: visual error 1.5 .4 .4 .7 1.1 1.5 1.1 1.1 .7 3 .4 0 .4 1.1 .7 .4 .7 .7 .9 (.7) 5.6

W2: semantic error 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0) 0

W3: context error 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .4 .02 (.1) .1

W4: visual–semantic error 0 0 0 0 0 0 0 .4 .4 0 .4 0 0 0 0 0 0 0 .1 (.1) .4

W5: visual–context error 1.8 1.5 .4 1.5 .7 1.1 .4 1.8 .7 1.1 .7 .0 .7 1.1 .0 .7 1.8 .7 .9 (.6) 6

W6: function word substitution 1.5 1.8 1.1 .7 1.5 1.1 2.2 1.8 .4 1.8 1.8 4.1 1.1 .7 1.1 1. 1.1 2.6 1.5 (.8) 9.8

W7: derivational error 1.1 1.5 1.8 4.4 .7 3 3.7 3.3 1.1 1.1 .4 1.8 2.6 1.5 1.5 3. 4.1 1.1 2.1 (1.3) 13.6

W8: residual error 0 0 0 0 0 0 0 0 .4 0 0 0 0 0 0 0 0 0 .02 (.1) .1

Residual responses

R1: U has wrong assignment of stress, but

is correct otherwise

0 0 0 0 0 0 0 0 0 .4 .4 .4 .4 0 .4 .4 .4 .4 .2 (.2) 1

R2: U contains a phonetization error 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0) 0

R3: U is a visually related non-word 2.2 1.1 1.5 3 .4 .7 1.5 .4 2.6 2.6 3.7 1.5 3 3.7 1.8 1. 1.5 4.8 2.1 (1.2) 13.1

R4: U is a non-word that is not visually

related

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0) 0

R5: word omission .4 1.1 0 .4 .4 0 0 1.5 .4 0 0 1.1 .4 0 .7 1. .4 0 .4 (.5) 2.7

R6: word addition 0 0 1.8 .7 .4 0 0 0 .4 0 0 .4 .4 0 .4 .7 0 0 .3 (.5) 1.8

R7: word transposition: different order of

words

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0) 0

R8: ambiguous U; sounding-out behaviour

with word substitution.

0 0 0 .4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .02 (.1) .1

R9: ambiguous U 2.2 2.2 1.8 2.6 .7 3.3 3.3 .4 1.1 0 .7 .7 0 1.8 .4 .7 1.1 .4 1.3 (1.1) 8.4

co

rt

ex

46

(2

01

0)

12

72

–1

28

31

28

2

S

2

1

7

1

1

c o r t e x 4 6 ( 2 0 1 0 ) 1 2 7 2 – 1 2 8 3 1283

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