Whole-word shape effect in dyslexia
Michal Lavidor
Department of Psychology, University of Hull, UK
The research question here was whether whole-word shape cues might facilitate
reading in dyslexia following reports of how normal-reading children benefit from
using this cue when learning to read. We predicted that adults with dyslexia would
tend to rely more on orthographic rather than other cues when reading, and therefore
would be more affected by word shape manipulations. This prediction was tested in a
lexical decision task on words with a flat or a non-flat outline (i.e. without or with
letters with ascending/descending features). We found that readers with dyslexia
were significantly faster when reading non-flat compared with flat words, while
typical readers did not benefit from whole-word shape cues. The interaction of
participants’ group and word shape was not modulated by word frequency; that is
word outline shape facilitated reading for both rare and frequent words. Our results
suggest that enhanced sensitivity to orthographic cues is developed in some cases of
dyslexia when normal, phonology-based word recognition processing is not
exploited.
It has been suggested that up to 15% of the population may suffer from some form of
dyslexia. The exact figures vary due to diagnostic and cultural differences (Snowling,
2000). Developmental dyslexia has traditionally been defined as a discrepancy between
reading ability and intelligence (Ramus et al., 2003). Definition s of dyslexia still
emphasise difficulties with written language, although this is only one of its many
manifestations (American Psychological Association, 1994). Several theories have been
suggested to account for the origin of dyslexia, including phonological-deficit theory
which has garnered much empirical support (Ramus et al., 2003).
The phonological-deficit theory postulates that reading problems in people with
dyslexia can be ascribed to the fact that to read proficiently an individual needs to learn
the way in which graphemes and phonemes correspond to enable successful decoding of
text. If the correspondence between letters and sequences of letters and the sounds of
letters or combinations of letters is poorly stored or retrieved, this will undermine the
whole read ing process (Bradley & Bryant, 1978; Vellutino, 1979). The phonological-
deficit theory postulates that there is a straightforward link between this cognitive deficit
and the behavioural problems seen in dyslexia (Ramus et al., 2003; Snowling, 2000).
There are other non-visual theories such as the rapid auditory processing theory (Tallal,
1980) and the anchoring-deficit theory (Ahissar, 2007).
Because dyslexia has many manifestations and phenotypes, it is not surprising that in
addition to phonological processing theories there is accumulating evidence for visual
(Lovegrove, Bowling, Badcock & Blackwood, 1980; Stein, 2003; Stein & Walsh, 1997)
Journal of Research in Reading, ISSN 0141-0423 DOI: 10.1111/j.1467-9817.2010.01444.x
Volume 34, Issue 4, 2011, pp 443–454
r United Kingdom Literacy Association 2010. Published by Blackwell Publishing, 9600 Garsington Road,
Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA
Journal of
Research in Reading
Journal of
Research in Reading
and visual-attentional deficits (Facoetti et al., 2003) that may point to orthographic
processing difficulties in dyslexia. Some studies have described dissociations between
phonological and orthographic deficits in cases of dyslexia (Howard & Best, 1996;
Lavidor, Johnston & Snowling, 2006; Romani, Ward & Olson, 1999; Valdois et al.,
2003), thus highlighting the multi-causal nature of dyslexia and the importance of
orthographic processing.
Here we examined one specific orthographic variable that was reported to affect early
reading development – the outline shape of the whole word. The research question was
whether word shape might facilitate reading in dyslexia, following reports that normal-
reading children benefit from using this cue whe n learning to read (Webb, Beech, Mayall
& Andrews, 2006).
Most theorists assume that there is some degree of visual featural analysis in the initial
stages of reading before the focus shifts to other cognitive processes as reading progresses.
The notion of featural analysis has had a long history and is part of many models of skilled
reading such as the interactive activation model of word recognition (McClelland &
Rumelhart, 1981) and the Local Combination Detector or LCD model (Dehaene, Cohen,
Sigman & Vinckier, 2005). In addition, a recent study by Dehaene and Cohen’s group
(Cohen, Dehaene, Vinckier, Jobert & Montavont, 2008) explored the role of the two visual
pathways, the ventral and the dorsal streams, in visual-orthographic processing. From a
developmental point of view, Frith (1985) suggested that visual features (or cues) are
important during the first stages of learning to read, with children initially relying on a
rudimentary analysis of features. As more words are learned, this method is gradually
subsumed by phonological processing, and the visual-orthographic processing is no longer
the main process in skilled reading (Ehri & Wilce, 1985; Frith, 1985).
According to Mayall (2002) who employed a CaSe-MiXiNg paradigm (mixing upper
and lower case) that distorted overall word shape, 6–9-year-olds appear to rely more on
purely visual aspects of text initially but then develop other types of processing by
roughly the age of 8 or 9 as their vocabularies increase. Other paradigms emphasising a
sensitivity to certain visual features beyond letter identity within words were employed
and found that children made use of distinctive visual information (Ehri & Wilce, 1985),
fragmentary visual features such ascending and descending letters (Johnston, Anderson &
Duncan, 1991) and holistic word cues (Masterson, Laxon & Stuart, 1992). As a whole,
these findings may indicate that salient visual features such as the distinctive features of
the outline shape of words could provide an effective visual cue to word recognition.
Mayall (2002), Webb et al. (2006) and Johnston et al. (1991) concluded that reliance
on visual information (in the sense of sensitivity to perip heral visual features) declines
when reading skills such as holistic processing give way to other strategies. The reading
development model thus implies that adults who have no reading difficulties should not
be affected by word shape as they do not (or rarely) use visual reading. Nonetheless, other
investigators continue to claim that supraletter features such as word shape play a role in
visual word recognition (e.g. Allen, Wallace & Weber, 1995; Healy & Cunningham,
1992; Healy, Oliver & McNamar a, 1987). Healy and Cunningham found that the number
of proofreading errors was affected by word shape in lower-case passages, but not in all-
upper-case passages. Allen et al.’s (1995) holistic-biased hybrid model suggests that
words can be formed either via letter-level codes or via word-level codes. This
‘horse-race’ model predicts that high-frequency words can be identified by the fast and
frequency-sensitive word-level channel, whereas low-frequency words will be identified,
on many occasions, by the frequency-insensitive letter-level channel. Perea and Rosa
444 LAVIDOR
r United Kingdom Literacy Association 2010
(2002) found a reliable effect for word shape, manipulated using MiXeD CaSe letters,
which was greater in normal readers for low-frequency words than for high-frequency
words. Interestingly these results, although reporting word shape effects, contrasted the
predictions of the holistic biased hybrid model regarding word frequency (Allen et al.,
1995), casting doubts regarding the role of visual-orthographic cues in adult skilled
reading.
To sum up the disparities in the findings, Mayall (2002; see also Webb et al., 2006)
argues that visual reading occurs only at the initial stages of reading development; hence
word format cues such as whole word outline or case alternation will not affect adult
readers. By contrast Perea and Rosa (2002) and Allen et al. (1995) reported word shape
effects on normal adult readers, though with different theoret ical accounts. Note that
these contrasting views both fail to address the question of word shape effects in the case
of dyslexia (in adults). Assuming that standard reading strategies are impaired in
dyslexia, and in particula r phonological processing, it may be the case that adults with
dyslexia would tend to rely more on orthographic cues whe n reading (see Howard &
Best, 1996), and therefore would be more affected by word shape manipulations.
To test this prediction we generated two lists of lower-case words that were matched
by their length, frequency and other lexical variables. They differed only by their outline
shape: the ‘flat’ list was composed solely of letters without ascending or desce nding (off-
line) features, such as ‘r’, ‘u’, ‘s’, etc., whereas the ‘non-flat’ list was made up of
ascending and descending letters (‘l’, ‘p’, ‘d’). The prediction was that readers with
dyslexia would read the non-flat list better than the flat list as they make use of the word
shape cues, which are more distinctive when ascending and descending letters are
incorporated in them. This mild manipulation was selected rather than the more common
letter case alternation (Ellis, Ansorge & Lavidor, 2007), so as to test the prediction under
(more) natural word recognition conditions.
Method
Participants
Sixteen participants (10 males) formed the dyslexia group and ranged in age from 19 to
29 (M 5 22). There were three left-handed participants. The control group was also made
up of 16 participants (10 males) aged between 19 and 27 (M 5 22). The groups were
matched for gender, handedness and age. All participants had English as their first
language and were students at the University of Hull.
Participants with dyslexia had to be tested within the University of Hull testing service
in the previous 2 years and have had a history of reading problems. Control participants
reported no reading problems at any stage in their lives. In addition, both groups were
assessed on a battery of literacy and cognitive skills as detailed below.
Assessment battery
Literacy skills. One of the subtests from the Wide Range Achievement Test (WRAT;
Jastak & Wilkinson, 1993) was used to measure word reading. The test consists of a list
of words organised from easy to hard to read.
Both subtests of the Test of Word Reading Efficiency (TOWRE) were also used
(Torgesen, Wagner & Rashotte, 1999). The sight reading efficiency test consisted of a list
WORD SHAPE IN DYSLEXIA 445
r United Kingdom Literacy Association 2010
of words that need to be read as fast and accurately as possible within 45 seconds. The
phonetic decoding efficiency subtest consisted of a list of pronounceable nonwords
(pseudowords). This task also requires participants to read as fast and accurately as
possible within 45 seconds.
Phonological skills. Rapid naming: The rapid object naming task from the Comprehen-
sive Test of Phonological Processing (CTOPP, Wagner, Torgesen & Rashotte, 1999)
comprised two cards with pictures of objects (e.g. boat, star, pencil, etc.). Participa nts
were asked to name the objects in order, from left to right, as fast and accurately as they
could. Response times and errors were recorded.
Spoonerisms: The spoonerism task (Perin, 1983) consisted of a list of word pairs. The
task was to swap the words’ initial phonemes (e.g. Key-Chain/Chee Kain). There were 18
word pairs taken from Perin (1983). Response times and errors were recorded.
Cognitive ability: Two subtests of the Wechsler Abbreviated Scale of Intelligence
(WASI; Wechsler, 1997) were used to assess verbal and nonverbal skills.
Verbal ability: The vocabulary subtest requires participants to give the meaning of 42
words presented in a list. The responses were scored according to the goodness of fit of
the response to standard responses (i.e. scores of 0, 1 or 2 points). Words are increasingly
difficult and the test is discontinued after a series of zero responses (i.e. stop rule).
Nonverbal ability: In the matrix of reasoning subtest, participants are presented with
pictures where a piece is missing. Participants are required to observe the picture and
need to say which of a series of options fits the missing bit. This subtest has a stop rule as
well.
Cognitive processing skills. Working memory test. The digit span subtest from the
Wechsler Adult Intelligence Scale III (WAISIII; Wechsler, 1997) battery was used. In this
test, the examiner presents a random sequence of digits and the participant has to repeat it.
There are two tasks in this subtest. The digit forward task requires the participants to
repeat the digits in the same order they were presented. In the backward task, the sequence
of digits has to be reversed. The number of digits increases by one for every successful
trial. The score reflects the maximum number of digits that were correctly repeated.
Results of group comparisons
Group means, standard deviations and significance values are present ed in Table 1. As
expected, the dyslexia group had poorer performance than controls on the WRAT reading
scores, t(30) 5 6.35, po.001, the TOWRE for words, t(30) 5 3.88, po.001, the TOWRE
for nonwords, t(30) 5 7.40, po.001. There were no differences for verbal and nonverbal
skills, p4.1. The only predicted difference that showed a trend rather than a clear
difference was the digit span test, t(30) 5 1.60, p 5 .068.
Stimuli: There were two lists of stimuli with 40 words of four to six letters each (see
Appendix A). Half of the strings were high-frequency words (mean frequency 5 126
according to the Celex Database; Baayen, Piepenbrock & van Rijn, 1993) and the other
half were low-frequency words (mean 5 19). The words in the lists were balanced across
several parameters; that is, frequency (Celex), orthogra phic neighbourhood (according to
the English Lexicon Project [ELP]; Balota et al., 2007) and length. Crucially, the words
in the two lists were matched for RT to a lexical decision task conducted with these
words as collected from 817 subjects (Balota et al., 2007). The fact that the lists were
446 LAVIDOR
r United Kingdom Literacy Association 2010
matched for normative RT implies that even if there are additional lexical parameters that
are not matched between the lists, any difference in RT found in the current experiment
can be safely interpreted as reflecting differences in the manipulated experimental
conditions but not confounding factors. Eighty nonwords, orthographically legal but not
pseudohomophones, matched in length and ‘flatness’ to the words, were gener ated to
allow lexical decision task, and their lexical properties were carefully matched based on
the ELP-rich database (Balota et al., 2007). Half of the nonwords included only flat
letters, and the other half non-flat letters. The average number of letters (4.87 in the flat
list, 5.02 letters in the non-flat list), mean orthographic neig hbourhood (five in each
group), mean bigram frequency (1,662 and 1,685, respectively) and mea n RT to a lexical
decision task with these nonwords as collected from 817 subjects (Balota et al., 2007) that
were 761 ms in the flat list and 768 ms in the non-flat list, did not differ significantly on
any property. All letter strings were presented in Courier New font size 15 points.
Procedure
All the tests were performed in a single session, which lasted approximately 50 minutes.
The experimental task was completed first, which was then followed by the assessment
tasks. Each participant completed a total of 80 word and 80 nonword trials on the lexical
decision task. All stimuli were presented in lower case; half had no ascending or
descending letters (i.e. ‘flat’) and the rest contained such letters (‘non-flat’). Words and
nonwords, of high or low frequency and with a flat or a non-flat outline were presented in
a random order in screen centre.
Each trial began with a fixation cross (1) appearing in the centre of the screen for
500 ms. A word or nonword was then presented until a response was made, or until 3
seconds had elapsed without a response. Participants were asked to decide as quickly and
as accurately as possible whether each stimulus was a word or a nonword and to respond
by pressing one of two keys on a standard QWERTY keyboard with the index or middle
finger of their right hand. Half the participants made ‘word’ responses by pressing the N
key and ‘nonword’ responses by pressing the V key. The remaining participants made
‘word’ responses by pressing the V key and ‘nonword’ responses by pressing the N key.
The pres entation of the stimuli and recording of accuracy and RTs was controlled by
Eprime v1 software. The experimental session began with 12 practice trials in which 6
Table 1. The literacy and cognitive skills comparisons for participants with dyslexia and controls.
Dyslexia Control
MSDMSDComparison
Literacy skills WRAT Reading 103 7 112 5 po.001
TOWRE Words 83 8 92 7 po.001
Nonwords 85 10 105 9 po.001
Phonological skills Rapid naming 7 3 11 2 po.001
Spoonerism 9 5 17 3 po.001
Cognitive ability Verbal skills 58 11 59 9 p4.1
Nonverbal skills 48 9 48 7 p4.1
Abbreviated scales IQ 107 13 107 11 p4.1
Cognitive processing skills Working memory Digit span 9.1 2.1 10.7 2.8 p 5 .07
TOWRE, Test of Word Reading Efficiency; WRAT, Wide Range Achievement Test.
WORD SHAPE IN DYSLEXIA 447
r United Kingdom Literacy Association 2010
words and 6 nonw ords were presented centrally (half flat, half non-flat). Filler words and
nonwords were used for these practice trials.
Results
Two mixed-design analyses were carried out with stimuli lexicality (high-frequency
word, low-frequency word and nonword), stimuli shape (flat or non-flat) as the within-
subject variables and group (dyslexia, controls) as the between-subject variable, one for
RT (for correct responses) and one for accuracy. These F1 analyses were followed by the
corresponding item analysis.
A main effect of lexicality was found, F1(2, 60) 5 59.79, po.0001; F2(1, 154) 5 4.21,
po.05. Responses to nonwords (mean 5 865 ms) were the slowest, followed by faster
RTs to rare words (mean 5 753 ms) and fastest RT to frequent words (733 ms). There was
a nearly significant interaction between lexicality and group, F1(2, 60) 5 3.01, p 5 .057;
F2(1, 154) 5 2.14, ns. Bonferroni post hoc comparisons (po.05) did not reveal any
significant differences; however, the patterns reflected a greater advantage of 34 ms for
frequent words compared with rare words in the dyslexia group, whereas word frequency
had no significant effect in the control group (11 ms advantage). Nonwords were
processed faster in the control group (841 ms) when compared with the dyslexia group
(889 ms). However, these are only trends as they failed to reach significance.
The novel finding of this study was the significant three-way interaction between
stimuli shape, lexicality and group, F1(2, 60) 5 6.04, po.01; F2(2, 154) 5 4.12, po.01.
Because the predictions and the experimental design regarded words only, the
interpretation of the three-way analysis required separate further analysis for words (of
high and low frequency) and nonwords. For words, there was a significant interaction of
shape and group, F1(1, 30) 5 6.32, po.05; F2(1, 76) 5 4.51, po.05. Bonferroni post hoc
comparisons revealed only one significant difference between participants’ groups and
word shape categories: while there was no word shape effect in the control group, non-flat
words (mean 5 727 ms) were responded to faster than flat words (746 ms) in the dyslexia
group. This pattern is p lotted in Figure 1. For nonwords, there was a main effect of group,
F1(1, 30) 5 8.49, po.01; F2(1, 76) 5 6.34, po.05, reflecting that control participants
responded faster (841 ms) than participants in the dyslexia group (889 ms). Nonword
shape, however, did not affect latency in any group.
690
Control
Dyslexia
Group
Flat
*
Non-flat
700
710
720
730
RT (ms)
740
750
760
Figure 1. Lexical decision latencies (and error bars) to correct words as a function of word shape and
participants’ group.
448 LAVIDOR
r United Kingdom Literacy Association 2010
The accuracy measure did not reveal many effects. There was a significant group effect,
F1(1, 30) 5 13.1, po.01; F2(1, 154) 5 6.41, po.01, with higher accuracy (91.7%) in the
control group compared with the dyslexia group (82.5%). There was a significant interaction
of lexicality and group, F1(2, 60) 5 5.35, po.01; F2(1, 154) 5 2.51, ns. Bonferroni post hoc
comparisons (po.05) revealed that while the participants in the dyslexia group had roughly
the same accuracy level for all stimuli types, in the control group responses to nonwords
(mean 5 84.5%) were significantly less accurate than to rare or frequent words (both were
responded to with 97% accuracy). Stimuli shape, however, did not affect accuracy of lexical
decision: flat items were responded to with 88.3% accuracy (94.3% for control, 82.3% for
dyslexia group), with similar accuracy for non-flat items (87.9%) with 92.1% correct
responses in the control group and 82.3% in the dyslexia group.
No other sign ificant main effects or interactions were found regarding accuracy.
Discussion
We manipulated word shape cues in order to assess whether visual featural word
recognition takes place in adult reading, in typical and atypica l (dyslexic) readers.
Facilitation effects of distinctive word shape (Mayall, 2002) were found only for readers
with dyslexia but not for typical readers. Both groups were faster and more accurate for
frequent words; however, word frequency did not modulate the selective word shape
effects in the dyslexia group. Word shape did not affect processing of nonwords, which
suggests the visual-orthographic analysis involves top-down support during word
recognition (Lavidor et al., 2006). These results are the first to demonstrate subtle word
shape effects in a group of adults with dyslexia, adding therefore to Mayall’s (2002)
studies (see also Webb et al., 2006) with children that predicted use of visual word outline
only in impaired but not normal reading.
Our findings did not replicate the use of orthographic cues in the control group (Perea
& Rosa, 2002); however, this could be due to the subtler man ipulation of word shape we
used compared with case alternation or all upper-case presentation (Allen et al., 1995).
Previous studies that employed extreme manipulations of word shape reporte d sensitivity
to visual word format in typical readers (Ellis et al., 2007).
Arguably, peopl e with d yslexia are relatively advantaged in processing non-flat words
that might have a more distinctive appearance due to a preference for globa l, holistic
processing that capitalises on the coarse coding of orthography–phonology mappings
(Lavidor et al., 2006). Such a processing strategy is indeed the likely outcome of learning
to read in the absence of good phonology or the capacity to create fine-grained mappings
between graphemes and phonemes (Harm, McCandliss & Seidenberg, 2003). If, as is
widely accepted, deficits at the level of phonological representation in children with
dyslexia compromise the mappings created between phonology and orthography in the
phonological pathway (Harm & Seidenberg, 1999; Snowling, 2000), these mappings will
not be at the fine segmental level that is optimal for reading in English. The argument is
that the three representational forms of words (phonological, orthographic and semantic)
capture different proportions of processing, and the balance between them may be shifted
in dyslexia. As our study show s, there might be more reliance on orthographic cues in
dyslexia, hence the larger word shape effect reported here.
Other studies have highlighted the importance of the analysis of visual features.
Gibson, Gibson, Pick and Osser (1962) showed that each letter of the alphabet is
composed of a pattern of differ ent visually distinctive features, and proposed that
WORD SHAPE IN DYSLEXIA 449
r United Kingdom Literacy Association 2010
detecting distinctive features within words is a key factor in children’s perception of
words. Beech and Mayall (2005) concluded that, far from bein g evenly distributed, there
is a concentration of stimulus information on the periphery of words; hence, priming
words with these areas has a more potent effect than priming words with their internal
features, which are relatively more impoverished. Moreover, as a result of this richer
informational composition, readers have a predi sposition to process outer information
(i.e. the external shape of the word) over internal information (the visual information
inside the physical borders of the printed word). Perhaps, as Beech and Mayall (2005)
suggested, prior access to a word’s external features is an obligatory process in the course
of word recognition, but it also seems that the outer features of words contain elements
that are more potent.
Previous research has found that salient visual features such as the distinctive features
of the outline shape of words provide an effective visual cue to word recognition in
children when beginning to read (Ehri & Wilce, 1985; Johnston et al., 1991; Masterson
et al., 1992; Mayall, 2002). In addition, Mayall (2002), Webb et al. (2006) and Johnston
et al. (1991) concluded that reliance on visual information (in the sense of sensitivity to
peripheral visual features) declines when reading skill as holistic processing gives way to
other strategies. The current results show that this visual processing continues to have an
effect in cases of impaired reading in adults, but not in typical readers.
It is important to note that the word shape manipulation we conducted was very subtle
and did not alter the appearance of the word, unlike previous studies that employed
alternating case (Perea & Rosa, 2002), all upper-case letters (Allen et al., 1995) or other
explicit visual manipulations (Webb et al., 2006). Yet even this implicit cue of word
shape, which is more distinctive in non-flat than flat words, was sufficient to mak e word
recognition easier for readers with dyslexia. Overall, the control group was more accurate
than the dyslexia group; however, for words with ascending and descending features,
readers with dyslexia improved their lexical decision speed compared with flat words and
did not differ from controls. The high accuracy rates in the control group in all conditions
probably reflec t the ease of the task, as there was no time limit and words were presented
until responses were recorded. The subtle manipulation we employed explains the lack of
potential word shape effects on typical reading reported previously (e.g. Allen et al.,
1995; Healy & Cunningham, 1992; Healy et al., 1987; Perea & Rosa, 2002).
Thus, in a typical healthy reading development, the effects of visual word features have
only a minimal effect on performance in a lexical decision task, because the task utilises
efficient mappings between orthographic and phonological representations. However,
when the development of such mappings is compromised (as is the case in developmental
dyslexia), we can expect anomalies in the way orthographi c and phonological factors
affect reading.
References
Ahissar, M. (2007). Dyslexia and the anchoring-deficit hypothesis. Trends in Cognitive Sciences, 11, 458–465.
Allen, P.A., Wallace, B. & Weber, T.A. (1995). Influence of case type, word frequency, and exposure duration
on visual word recognition. Journal of Experimental Psychology: Human Perception & Performance, 21, 914–
934. Available from: http://psycnet.apa.org/index.cfm?fa=buy.optionToBuy&id=1995-42716-001&CFID=
27480351&CFTOKEN=41841710.
American Psychological Association (1994). Diagnostic and statistical manual of mental disorders. (4th edn).
Washington, DC: American Psychological Association.
450 LAVIDOR
r United Kingdom Literacy Association 2010
Baayen, R.H., Piepenbrock, R. & Van Rijn, H. (1993). The CELEX lexical database (CD-ROM): Linguistic
Data Consortium. Philadelphia, PA: University of Pennsylvania.
Balota, D.A., Yap, M.J., Cortese, M.J., Hutchison, K.A., Kessler, B., Loftis, B. et al. (2007). The English
Lexicon Project. Behavior Research Methods, 39, 445–459. Available from: http://brm.psychonomic-
journals.org/content/39/3/445.long.
Beech, J.R. & Mayall, K. (2005). The word shape hypothesis re-examined: Evidence for an external feature
advantage in visual word recognition. Journal of Research in Reading, 28, 302–319.
Bradley, L. & Bryant, P.E. (1978). Difficulties in auditory organisation as a possible cause of reading
backwardness. Nature, 271, 746–747.
Cohen, L., Dehaene, S., Vinckier, F., Jobert, A. & Montavont, A. (2008). Reading normal and degraded words:
Contribution of the dorsal and ventral visual pathways. Neuroimage, 40, 353–366.
Dehaene, S., Cohen, L., Sigman, M. & Vinckier, F. (2005). The neural code for written words: A proposal.
Trends in Cognitive Sciences, 9, 335–341.
Ehri, L.C. & Wilce, L.S. (1985). Movement into reading: Is the first stage of printed word learning visual or
phonetic? Reading Research Quarterly, 20, 163–179. Available from: http://www.jstor.org/pss/747753.
Ellis, A.W., Ansorge, L. & Lavidor, M. (2007). Words, hemispheres, and dissociable subsystems: Effects of
exposure duration, case alternation, priming and continuity of form on word recognition in the left and right
visual fields. Brain and Language, 103, 292–303.
Facoetti, A., Lorussa, M.L., Paganoni, P., Cattaneo, C., Galli, R., Umilta, C. et al. (2003). Auditory and visual
automatic attention deficits in developmental dyslexia. Cognitive Brain Research, 16, 185–191.
Frith, U. (1985). Beneath the surface of developmental dyslexia. In K.E. Patterson, J.C. Marshall & M. Coltheart
(Eds.), Surface dyslexia: Neuropsychological and cognitive studies of phonological reading. (pp. 301–330).
Hillsdale, NJ: Lawrence Erlbaum.
Gibson, E.J., Gibson, J.J., Pick, A.D. & Osser, H.A. (1962). A developmental study of the discrimination of
letter-like forms. Journal of Comparative and Physiological Psychology, 55, 897–906.
Harm, M.W., McCandliss, B.D. & Seidenberg, M. (2003). Modeling the successes and failures of interventions
for disabled readers. Scientific Studies of Reading, 7, 155–183.
Harm, M.W. & Seidenberg, M.S. (1999). Phonology, reading acquisition, and dyslexia: Insights from
connectionist models. Psychological Review, 106, 491–528. Available from: http://psycnet.apa.org/
index.cfm?fa=search.displayRecord&uid=1999-03499-003.
Healy, A.F. & Cunningham, T.F. (1992). A developmental evaluation of the role of word shape in word
recognition. Memory and Cognition, 20, 141–150.
Healy, A.F., Oliver, W.L. & McNamara, T.P. (1987). Detecting letters in continuous text: Effects of display size.
Journal of Experimental Psychology: Human Perception & Performance, 13, 279–290. Available from:
http://psycnet.apa.org/index.cfm?fa=search.displayRecord&uid=1987-23957-001.
Howard, D. & Best, W. (1996). Developmental phonological dyslexia: Real word reading can be completely
normal. Cognitive Neuropsychology, 13, 887–934. Available from: http://books.google.co.uk/books?hl=en&lr
=&id=vT1ZfmfFrKQC&oi=fnd&pg=PA887&dq=dyslexia+%22D+Howard%22&ots=zib6pDI56g&sig=Qeb
Ln3sHIdPdNDlJafCgIaj1UM0#v=onepage&q=&f=false.
Jastak, J. & Wilkinson, G.S. (1993). Wide range achievement test—revision 3. Wilmington, DE: Jastak
Associates.
Johnston, R.S., Anderson, M. & Duncan, L. (1991). Phonological and visual segmentation problems in poor
readers. In M. Snowling & M. Thomson (Eds.), Dyslexia: Integrating theory and practice. (pp. 154–164).
London: Whurr.
Lavidor, M., Johnston, R.S. & Snowling, M.J. (2006). When phonology fails: Orthographic neighbourhood
effects in dyslexia. Brain and Language, 96, 318–329.
Lovegrove, W.J., Bowling, A., Badcock, D. & Blackwood, M. (1980). Specific reading disability: Differences in
contrast sensitivity as a function of spatial frequency. Science, 210, 439–440.
Masterson, J., Laxon, V. & Stuart, M. (1992). Beginning reading with phonology. British Journal of Psychology,
83, 1–12.
Mayall, K.M. (2002). Case mixing effects on children’s visual word recognition: Lexical feedback and
development. Quarterly Journal of Experimental Psychology A, 55, 525–542.
McClelland, J.L. & Rumelhart, D.E. (1981). An interactive activation model of context effects in letter
perception: I. An account of basic findings. Psychological Review, 88, 375–407.
Perea, M. & Rosa, E. (2002). Does ‘whole-word shape’ play a role in visual word recognition? Perception and
Psychophysics, 64, 785–794. Available from: http://app.psychonomic-journals.org/content/64/5/785.long.
Perin, D. (1983). Phonemic segmentation and spelling.
British Journal of Psychology, 74, 129–144.
WORD SHAPE IN DYSLEXIA 451
r United Kingdom Literacy Association 2010
Ramus, F., Rosen, S., Dakin, S C., Day, B.L., Castello, J.M., White, S. et al. (2003). Theories of developmental
dyslexia: Insights from a multiple case study of dyslexic adults. Brain, 126, 841–865.
Romani, C., Ward, J. & Olson, A. (1999). Developmental surface dysgraphia: What is the underlying cognitive
impairment? Quarterly Journal of Experimental Psychology A, 52, 97–128.
Snowling, M. (2000). Dyslexia. Oxford: Blackwell Publishing.
Stein, J. (2003). Visual motion sensitivity and reading. Neuropsychologia, 41, 1785–1793.
Stein, J. & Walsh, V. (1997). To see but not to read: The magnocellular theory of dyslexia. Trends in
Neuroscience, 20, 147–152.
Tallal, P. (1980). Auditory temporal perception, phonics and reading disabilities in children. Brain and
Language, 9, 187–201.
Torgesen, J., Wagner, R. & Rashotte, C. (1999). Test of Word Reading Efficiency (TOWRE). Austin, TX:
Pro-Ed.
Wechsler, D. (1997). Wechsler Adult Intelligence Scale—III (WAISIII). San Antonio, TX: The Psychological
Corporation.
Valdois, S., Bosse, M.L., Ans, B., Zorman, M., Carbonnel, S., David, D. et al. (2003). Phonological and visual
processing deficits are dissociated in developmental dyslexia: Evidence from two case studies. Reading and
Writing, 16, 543–572.
Vellutino, F.R. (1979). Dyslexia: Theory and research. London: The MIT Press.
Wagner, R.K., Torgesen, J.K. & Rashotte, C.A. (1999). Comprehensive Test of Phonological Processing
(CTOPP). Austin, TX: Pro-Ed.
Webb, T.M., Beech, J.R., Mayall, K.M. & Andrews, A.S. (2006). It’s what’s on the outside that matters: An
advantage for external features in children’s word recognition. Journal of Experimental Child Psychology, 94,
163–181.
452 LAVIDOR
r United Kingdom Literacy Association 2010
Appendix A
Table A1. Word stimuli; norms for RT are taken from the ELP project (Balota et al., 2007).
Flat word Length Freq. Ortho_N RT Non-flat word Length Freq. Ortho_N RT
Access 6 24 0 677 Abrupt 6 18 0 690
Accuse 6 10 0 626 Ballet 6 45 6 622
Answer 6 152 0 572 Belt 4 29 12 595
Avenue 6 46 1 644 Bishop 6 18 0 588
Camera 6 36 0 631 Bold 4 21 12 661
Cancer 6 25 5 616 Chip 4 17 8 579
Care 4 162 24 580 Date 4 103 17 637
Common 6 223 0 602 Deadly 6 19 1 592
Cone 4 13 18 670 Design 6 114 1 561
Core 4 37 23 628 Direct 6 129 0 587
Corner 6 115 3 683 Disk 4 25 5 647
Crew 4 36 5 591 Drying 6 29 3 671
Earn 4 16 6 585 Employ 6 12 0 678
Ease 4 42 6 616 Fall 4 147 12 709
Excess 6 42 1 605 Father 6 183 4 656
Excuse 6 27 1 625 Fish 4 35 4 574
Manner 6 124 4 641 Gently 6 31 2 615
Mass 4 110 15 594 Hang 4 26 10 602
Mean 4 199 9 634 Hidden 6 20 1 592
Mere 4 47 5 727 Hold 4 169 11 613
Mess 4 22 9 615 Island 6 167 1 608
Move 4 171 9 611 Keys 4 34 1 616
Museum 6 32 0 698 Lead 4 129 12 585
Near 4 198 13 582 Leaf 4 12 6 559
News 4 102 4 636 Liking 6 11 4 756
None 4 108 13 621 Living 6 194 6 611
Noon 4 25 8 619 Myself 6 129 0 627
Reason 6 241 1 579 Path 4 44 7 603
Rescue 6 15 0 584 Pink 4 48 14 568
Season 6 105 1 588 Pretty 6 107 0 638
Secure 6 30 0 680 Sigh 4 11 4 613
Series 6 130 1 657 Simply 6 170 3 613
Severe 6 39 2 659 Superb 6 14 0 701
Soon 4 199 7 683 Talk 4 154 8 749
Sore 4 10 15 585 Tide 4 11 10 637
Summer 6 134 3 670 Toilet 6 13 1 630
Warn 4 11 12 592 Type 4 200 2 630
Worn 4 23 12 608 Wall 4 160 13 563
Zone 4 11 8 575 Yearly 6 12 3 613
Resume 6 16 0 577 Play 4 200 6 664
Total 5 77.7 6.1 624 Total 5 75.25 5.25 626
WORD SHAPE IN DYSLEXIA 453
r United Kingdom Literacy Association 2010
Michal Lavidor is a reader at the Department of Psychology, University of Hull, UK. During her
D.Phil. (in experimental psychology at Bar Ilan University, Israel), she specialized in visual word
recognition, in particular hemispheric differences in processing written words. She moved to the
University of York as a Marie Curie Research Fellow and developed further her research interests
to investigate brain structures involved in orthographic processing of words and letters. She now
has her own Transcranial Magnetic Stimulation (TMS) laboratory in Hull, with the aim of
investigating the neural pathways of word processing, from the retina to the frontal cortex.
Lavidor’s research is funded by the Wellcome trust, the BBSRC, the Royal Society and the
European Commission.
Received 3 February 2010; revised version received 3 February 2010.
Address for correspondence: Michal Lavidor, Department of Psychology, University of
454 LAVIDOR
r United Kingdom Literacy Association 2010
Copyright of Journal of Research in Reading is the property of Wiley-Blackwell and its content may not be
copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written
permission. However, users may print, download, or email articles for individual use.
