ORIGINAL RESEARCH ARTICLE
published: 04 July 2014
doi: 10.3389/fnhum.2014.00468
HUMAN NEUROSCIENCE
Tracking orthographic learning in children with different
profiles of reading difficulty
Hua-Chen Wang*, Eva Marinus , Lyndsey Nickels and Anne Castles
Department of Cognitive Science, ARC Centre of Excellence in Cognition and its Disorders, Macquarie University, Sydney, NSW, Australia
Edited by:
Peter F. De Jong, University of
Amsterdam, Netherlands
Reviewed by:
Wim Van Den Broeck, Vrije
Universiteit Brussel, Belgium
David L. Share, University of Haifa,
Israel
*Correspondence:
Hua-Chen Wang, Department of
Cognitive Science, ARC Centre of
Excellence in Cognition and its
Disorders, Macquarie University,
Sydney, NSW 2109, Australia
e-mail: huachen.wang@mq.edu.au
Previous studies have found that children with reading difficulties need more exposures to
acquire the representations needed to support fluent reading than typically developing
readers (e.g., Ehri and Saltmarsh, 1995). Building on existing orthographic learning
paradigms, we report on an investigation of orthographic learning in poor readers using
a new learning task tracking both the accuracy (untimed exposure duration) and fluency
(200 ms exposure duration) of learning novel words over trials. In study 1, we used the
paradigm to examine orthographic learning in children with specific poor reader profiles
(nine with a surface profile, nine a phonological profile) and nine age-matched controls.
Both profiles showed improvement over the learning cycles, but the children with surface
profile showed impaired orthographic learning in spelling and orthographic choice tasks.
Study 2 explored predictors of orthographic learning in a group of 91 poor readers using
the same outcome measures as in Study 1. Consistent with earlier findings in typically
developing readers, phonological decoding skill predicted orthographic learning. Moreover,
orthographic knowledge significantly predicted orthographic learning over and beyond
phonological decoding. The two studies provide insights into how poor readers learn
novel words, and how their learning process may be compromised by less proficient
orthographic and/or phonological skills.
Keywords: orthographic learning, developmental dyslexia, subtypes, phonological decoding, orthographic
knowledge
INTRODUCTION
Orthographic learning has been defined as the transition from the
slow sounding out of an unfamiliar new word to the rapid automatic recognition of the same word. It is widely acknowledged
that beginning readers need to make this transition in order to
become proficient readers (e.g., Ehri and Wilce, 1983; Share, 1995;
Castles and Nation, 2008). In this study, we explored orthographic
learning in children with poor reading ability and investigated
the factors that are associated with their success in acquiring new
orthographic representations.
Most developmental theories propose that the sounding out
of words, phonological decoding, is an important mechanism for
reaching the final stage of automatic reading (for a review, see
Ehri, 2005). Among these theories, the self-teaching hypothesis is
associated with a strong claim for the importance of phonological
decoding in orthographic learning (Share, 1995, 1999). It proposes that phonological decoding is the first and most important
step of orthographic learning, providing an opportunity for this
learning to take place. The act of phonological decoding is proposed to allow the reader access to a word’s spoken form, as well
as to draw their attention to the order and identity of the letters.
This, together with repeated exposure to the new word, assists the
reader in establishing an orthographic representation. According
to the self-teaching hypothesis, although phonological decoding
is crucial in orthographic learning, it is not the only factor: there
is a secondary, orthographic processing component, which also
Frontiers in Human Neuroscience
determines the success of orthographic learning, although the
nature of this mechanism is little understood (Share, 1995, 2011).
If phonological decoding is important for acquiring orthographic representations, proficient phonological decoding processes should increase the likelihood of successful orthographic
learning. Conversely, impaired phonological decoding processes
should be expected to lead to difficulties in orthographic learning.
Indeed, abundant studies seem to support the view that deficits
in phonological processing skills may be a primary cause of reading difficulties (e.g., Rack et al., 1992; Stanovich and Siegel, 1994)
as well as orthographic learning difficulties (Share and Shalev,
2004).
However, a large body of evidence on heterogeneity within
the dyslexic population and on the existence of different subtypes of developmental dyslexia suggests that the relationship
between phonological decoding skills and orthographic learning may not be straightforward (Castles and Coltheart, 1993;
Manis et al., 1996; Stanovich et al., 1997; Valdois et al., 2003;
Castles et al., 2010b; Jones et al., 2011; McArthur et al., 2013;
Peterson et al., 2013). The outcomes of these studies show that
impairment in phonological decoding and impairment in automatic whole-word recognition can occur selectively—one aspect
of reading can be impaired while the other develops normally.
Namely, children with a surface dyslexia profile struggle to read
irregular words (e.g., yacht) but are not impaired in reading
nonwords (e.g., grep). This indicates that they are specifically
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Wang et al.
Tracking orthographic learning in children with dyslexia
impaired in recognizing whole words, whilst their phonological
decoding skills are intact. Conversely, children with a phonological dyslexia profile show impairments in nonword reading
but not irregular word reading, indicating a specific impairment
in phonological decoding processes while being intact in sight
word recognition skills. Note that children with these profiles
are seen as falling at the ends of a normal continuum of ability on the relevant reading subskills, not as qualitatively distinct
subtypes.
In sum, based on the view that phonological decoding is the
primary factor in successful orthographic learning, the reading
profiles of surface dyslexia and phonological dyslexia seem to be
somewhat of a paradox. How are some beginning readers able
to build up orthographic knowledge despite poor decoding skills
(phonological dyslexics) and why do some children with proficient decoding skills fail to build up orthographic representations
(surface dyslexics)? In order to address these questions we need to
examine how orthographic learning occurs in these two subtype
profiles.
To date, only two studies have explicitly contrasted differences
in orthographic learning in children with phonological and surface dyslexia. Castles and Holmes (1996) found that, as expected
by their specific reading profiles, children with a surface dyslexia
profile were poorer at learning novel irregular words (measured
by an orthographic choice task in which the child had to choose
the target item from its distracters) than children with a phonological dyslexia profile. However, since the novel words in their
study were all irregular, it may be that children with a surface
profile performed more poorly than children with a phonological profile because their usual phonological decoding strategy is
not effective for such items (e.g., how would one phonologically
decode a word like “laugh”?).
Bailey et al. (2004) built on the results of Castles and Holmes
(1996) by comparing orthographic learning of both regular and
irregular words in children with profiles of phonological dyslexia
and surface dyslexia. They found that overall, the two profiles
were no different from each other, but were both more impaired
than chronological age controls in orthographic learning (as measured by reading accuracy). In addition, they found that both
children with a surface profile and the controls showed an advantage in learning regular words as compared to irregular words. In
contrast, children with a phonological profile showed no difference between regular and irregular words, suggesting that phonological decoding was not relied on during orthographic learning.
Although this study provided more insight into the orthographic
learning processes of these two profiles of dyslexia, there are
still some limitations. First, orthographic learning results were
based on reading accuracy only1 . Thus, the finding that children with a surface profile were more accurate in reading regular
than irregular words may have been a function of the “decodability” of the words rather than orthographic learning per se
(e.g., cat can be read correctly by phonological decoding, whereas
1 Bailey et al. (2004) also used a spelling task to measure orthographic learning,
however, due to the fact that accuracy of spelling was at floor, only reading
accuracy results were discussed.
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this is not possible for yacht). Hence, to determine whether children with surface dyslexia are indeed better at acquiring (and not
just decoding) regular orthographic representations than irregular ones, improved measures of orthographic learning with
minimal influence from decoding ability are required. Second,
selection of the subgroups was based on a relatively lax criterion. Instead of selecting children with a surface profile that
were impaired on irregular word reading only, and phonological profile children that were impaired on nonword reading only,
Bailey and colleagues based their selection on a discrepancy score
between nonword and irregular word reading. Hence, for example, a phonological profile child in their study could have been
poor on both nonword and irregular word reading, but with
irregular word being relatively better than nonword reading. The
design of the study presented here allows us to address these
problems.
The first aim of the present study was to further extend our
understanding of orthographic learning in children with surface
and phonological profiles. By studying orthographic learning in
these two subgroups we also aimed to bridge the gap between
previous work on orthographic learning (mostly conducted with
normal readers) and the extensive literature on subtypes in
dyslexia. Building on the studies of Castles and Holmes (1996)
and Bailey et al. (2004), we used a more stringent subgroup criterion in selecting participants and developed a novel paradigm to
explore orthographic learning. Just like Bailey et al. we included
a sample of typical readers as controls so that we could not
only compare the performance of the children with different
profiles, but also contrast their performance to that of normal
readers.
We also included a broader range of measures of orthographic
learning than in the previous studies. Given that spelling tasks
are often difficult for poor readers, we included an orthographic
choice task. Finally, we developed a new learning paradigm that
assesses reading accuracy under both untimed and time-limited
exposure conditions. Time-limited exposure reading accuracy is
interpreted here as a fluency measure of item specific orthographic knowledge, as rapid recognition of words is considered a
hallmark of the acquisition of orthographic representations (Yap
and van der Leij, 1993; Marinus et al., 2012). This reasoning is
similar to the idea that the time that is required to read a word
is reduced when a word is read as a whole unit rather than by
phonological decoding (e.g., Coltheart, 1983; Ehri, 2005). An
additional benefit of this paradigm is that it allowed us to tap
orthographic learning by tracking improvement of fluency over
learning cycles. Hence, we were able to monitor orthographic
learning in a dynamic and ongoing fashion. Finally, just like Bailey
and colleagues, we included both regular and irregular words in
order to see if we could replicate the regularity effect for children
with a surface profile, and the absence of a regularity effect for
children with a phonological profile.
In our novel word-learning paradigm, novel letter strings
were assigned regular or irregular pronunciations and presented
in three learning cycles. After each cycle, we measured reading accuracy under both untimed and time-limited stimulus
exposure duration. After the three cycles were completed, traditional spelling and orthographic choice tasks were administered.
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Tracking orthographic learning in children with dyslexia
The untimed reading condition provided the opportunity for
children to decode and build up orthographic representations of
the novel words. In order to measure whether orthographic learning had taken place, each untimed reading block was followed by
a time-limited exposure block in which items were presented for
only 200 ms.
As mentioned earlier, this paradigm not only allows us to
explore whether the two groups with contrasting reading profiles differ in their orthographic learning performance, but also
to examine whether and to what extent orthographic learning
improves with number of learning exposures. Previous studies
examining the transition from decoding to rapid word recognition (as measured by increases in reading speed) have found
that children with dyslexia need many more exposures to acquire
novel word representations than typically developing readers
(Reitsma, 1983; Manis, 1985; Ehri and Saltmarsh, 1995). Reitsma
(1983, Experiment 3) reported that even six exposures to novel
words was not enough to result in any increase in reading speed
(taken as an index of orthographic learning) in a group of children with dyslexia. In contrast, in the same experiment, a group
of younger readers without reading difficulties showed a steep
increase in word reading speed. Note that none of these studies
made a distinction between different profiles of reading difficulty.
Hence, we used the current paradigm to monitor orthographic
learning within two groups of poor readers with contrasting
reading profiles.
Using the same paradigm, but with a larger sample of poor
readers, we conducted a second study to explore to what extent
different reading and language skills predict orthographic learning. For this purpose, we drew on an explicit model of component
processes involved in skilled reading, the dual-route model of
reading aloud (Coltheart et al., 1993, 2001). The six components
of this model include: letter analysis; letter-sound conversion,
phonemic buffer, orthographic lexicon, semantics, phonological
lexicon. We used regression analyses to investigate the association
between these components and orthographic learning. Reading
and language skills mapping onto the six different components
were used as predictors, and the orthographic learning results
were used as outcome measures.
STUDY 1
As outlined in the Introduction, the existence of children with
surface and phonological reading profiles challenges the role of
phonological decoding in orthographic learning. The aim of
Study 1 was to investigate how orthographic learning takes place
in poor readers with contrasting reading profiles. Study 1 consisted of two parts. The first part aimed to validate the group
membership of the phonological and surface profiles. In order to
do this we measured language and reading skills involved in reading processes based on the dual-route model of reading aloud. In
the context of the dual route model, we expect the children with
a phonological profile to be impaired in the letter-sound knowledge process of the nonlexical route. In contrast, children with a
surface profile are thought to be impaired in the lexical route, the
orthographic lexicon in particular.
In the second part of Study 1, we used the novel word learning
paradigm to investigate orthographic learning of the two profiles.
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The questions of interest were: (1) Are these children able to learn
novel words at all? If so, is their learning rate slower than controls?
This part of the study aimed to replicate previous studies suggesting that children with dyslexia are impaired at orthographic
learning (Reitsma, 1983, 1989) (2). Will children with a phonological profile, having impaired phonological decoding skills, be
less efficient at learning novel words than control children and
those with a surface profile? Alternatively, will children with a
phonological profile learn novel words faster than children with
a surface profile as predicted by their subtype reading profiles?
(3) Will children with phonological and surface profiles differ in
the size of the regularity effect? Typically developing children have
been shown to learn regular words better than irregular words as
regular words are more “phonologically decodable” than irregular words (Wang et al., 2012). However, as suggested by Bailey
et al. (2004), children with a phonological profile may show no
effect of regularity on orthographic learning due to their impaired
phonological decoding skill. Instead, they may learn novel words
via some kind of rote association between the sound and the form
of the novel words bypassing the phonological decoding process.
Children with a surface profile, in contrast, may show a normal word regularity effect on orthographic learning as they have
average phonological decoding skills.
PARTICIPANTS
Ninety-one poor readers (average age 9.3, range 7.2–12.3) were
recruited from schools, clinics or via newspaper advertisements
to participate in a reading training study at Macquarie University.
Children were included in the study if they scored at least
one standard deviation below average for their age on one or
both subscales (irregular word and nonword reading) of the
Castles and Coltheart 2 test (CC2; Castles et al., 2010a). All
poor readers scored within the normal range on non-verbal IQ
(Kaufman Brief Intelligence Test, K-Bit; Kaufman and Kaufman,
1990).
From this larger sample we selected two groups of poor readers: one with a surface profile and one with a phonological profile.
We will from here refer to them as the “surface group” and the
“phonological group.” The criteria for a surface profile were performance within the normal range (z-score > −1.00) on nonword
reading accuracy and below average performance on irregular
word reading (z-score < −1.00, which is equivalent to the bottom 15% of the norms). In addition, to ensure a discrepancy in
skills, the z-score difference between nonword and irregular word
reading had to be more than 0.5. The same test was administered twice in two sessions that were 8 weeks apart. Only children
with consistent reading profiles across the two sessions were
included. Nine poor readers fitted our stringent criteria of a surface profile on both testing sessions. Next, we identified children
showing consistent profiles of phonological dyslexia (nonword
z-score < −1.00, irregular word > −1.00, with a difference of
more than 0.5), resulting in a subsample of 22. From this sample we selected nine participants with a phonological profile,
matching the surface group in age, IQ and level of impairment
on the relevant reading subtest. Finally, we recruited nine agematched typical readers that were participating in reading studies
at Macquarie University as controls. The reading accuracy of
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Wang et al.
Tracking orthographic learning in children with dyslexia
these controls was within one standard deviation below the average range and 1.5 standard deviation above the average range
scores on all three subscales of the CC2 (please see Table 1 for
the characteristics of the three groups).
SUBGROUP VALIDATION
In this first part of Study 1, we validated subgroup membership by examining the language and reading skills of the two
groups with contrasting reading profiles. We designed tasks that
aimed to tap different components of the dual route model
of reading aloud (see Figure 1). This model proposes a lexical route through which words are directly recognized as whole
units and a nonlexical route through which words are decoded
phonologically.
As can be seen from Figure 1, each of these routes consists of a
number of processing components, some shared across the routes
and some separate. When a reader sees a printed word, the letters will first be recognized in the letter analysis component. Then
in the nonlexical route, the graphemes of the word are phonologically decoded by the letter-sound knowledge component (also
referred to as the “grapheme-to-phoneme conversion” component). In the lexical route, the orthography of known words is
activated as a whole unit in the orthographic lexicon. Subsequently,
in the semantic system, the meaning of the word is activated and
then in the phonological lexicon the sound of the word is activated.
The final component of the model is the phonemic buffer where
phonemes are activated and temporarily stored before they are
spoken.
Letter analysis
Letter analysis was measured with a cross-case copying task
(McArthur et al., 2013). This task consists of 14 letters, 7 in upper
case and 7 in lower case. For lower case letters the child was asked
to write down the upper case of the same letters (e.g., t − T), and
vice versa for upper case letters. Test–retest reliability, r = 0.75.
Letter-sound knowledge
The ability to convert letters or letter strings into sounds was
tested with the Letter-Sound Test (LeST, Larsen et al., 2011).
Each child was asked to produce the appropriate sound for 51
single-letter and multiletter graphemes. The items were presented
on individual flash cards. The graphemes were selected as being
consistent, in other words they had the same pronunciation in
more than 75% of occurrences of that grapheme according to
Measures of reading processes
Each of the six basic components in the dual-route model was
assessed with one test as described in the sections below. Test–
retest reliability (Pearson’s r) is reported for each measure based
on scores over two testing sessions that are 8 weeks apart, with
a sample of 115 children, aged 7–12 in a larger reading training
study (McArthur et al., 2013).
FIGURE 1 | The dual route model and its six basic components.
Table 1 | Characteristics and reading processing skills of the control, phonological, and surface groups.
Controls
Phonological profile
Surface profile
Mean (SD)
Mean (SD)
Mean (SD)
Phon. vs. Surf.
t
p (2-tailed)
CHARACTERISTICS
Age
Nonverbal IQ (K-Bit, standardized score/100)
9.31 (1.52)
NA
9.42 (1.62)
0.11
0.92
105.22 (8.60)
9.49 (1.35)
101.44 (13.48)
0.71
0.49
Nonword reading (CC2, z-score)
0.15 (0.66)
−1.58 (0.25)**
−0.51 (0.44)*
−6.31
0.00**
Irregular word reading (CC2, z-score)
0.29 (0.55)
−0.57 (0.21)**
−1.53 (0.24)**
8.94
0.00**
0.73
READING PROCESSING SKILLS
Letter analysis (Cross-case matching, raw score/14)
13.57 (1.13)
13.78 (0.44)
13.00 (1.58)
0.34
Letter-sound knowledge (LeST, raw score/51)
41.67 (3.61)
34.11 (7.22)*
40.78 (4.06)
2.42
0.03*
Orthographic lexicon (DOOR/DOAR, raw score/30)
22.89 (4.89)
24.44 (2.01)
18.89 (3.72)+
3.94
0.00**
Semantics (PPVT, standardized score/100)
104.57 (13.97)
96.56 (7.99)
96.56 (12.82)
0.00
1.00
Phonological Lexicon (ACE, standardized score/10)
8.86 (2.04)
7.89 (1.54)
7.67 (2.96)
0.20
0.85
Phonemic Buffer (NEPSY, standardized score/10)
9.43 (3.65)
8.22 (2.22)
10.11 (1.45)
2.13
0.05+
The asterisks in the “phonological profile” and “surface profile” columns indicate significant differences compared to control group.
*p < 0.05, **p < 0.01, + p < 0.1.
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Tracking orthographic learning in children with dyslexia
the CELEX database (Baayen et al., 1993). Test–retest reliability,
r = 0.84.
Orthographic lexicon
Word-specific orthographic knowledge was assessed with the
DOOR/DOAR lexical decision test (McArthur et al., 2013). Thirty
target words, ranging in frequency from 3 to 625 instances per
million words, were selected from the Children’s Printed Word
Database (CPWD, Masterson et al., 2003). All words were selected
to have alternative, homophonic spellings with adjustments of
the vowel (e.g., FLAME changed to FLAIM) or a consonant (e.g.,
CURL changed to KURL). Each item was presented paired with its
alternative homophonic spelling (e.g., DOOR and DOAR). The
child was asked to circle the correct spelling. Test–retest reliability,
r = 0.57.
that the phonological group had inferior letter-sound knowledge in the nonlexical route and the surface group showed
lower proficiency of the orthographic knowledge in the lexical
route.
ORTHOGRAPHIC LEARNING TASK
Materials
The ability to access the phonological lexicon was measured
with the Naming subtest of the Assessment of Comprehension
and Expression (ACE6–11 test; Adams et al., 2001). The child
was asked to name 25 pictures. No stopping rule was applied.
Test–retest reliability, r = 0.87.
This task consisted of eight four to five-letter nonwords (e.g.,
vack), four of which were assigned with regular pronunciations
and the other four with irregular pronunciations (please see
Supplementary Material). The nonwords were created in the same
way as the items used in Wang et al. (2011), but the items are
not identical to Wang et al. due to differences in the experimental design. The regular items were pronounced according to a set
of typical grapheme-phoneme correspondence rules (Rastle and
Coltheart, 1999). “Typical” was defined on the basis that the pronunciation of the vowel occurred in more than 50% of words
containing that vowel grapheme in both the CELEX database
(Baayen et al., 1993) and the CPWD (Masterson et al., 2003).
The irregular nonwords had pronunciations that did not follow typical letter-sound rules: the allocated pronunciation of the
vowel in the target word occurred in fewer than 50% of words in
the CELEX and the CPWD. All of the irregular pronunciations
were nevertheless existing grapheme-phoneme correspondences
in English. However, the pronunciations were infrequent and did
not occur in the context of the vowels and the final consonants
(bodies) of the irregular nonwords that were used in this task.
For example, the nonword cleap was assigned a pronunciation
“claip”; ea is pronounced this way in, for example, great, break,
but is always pronounced “ee” when followed by –p (e.g., heap,
leap).
Phonemic buffer
Procedure
We tested the phonological output buffer with a standardized
nonword repetition task, a subtest of the NEuroPSYchology
(NEPSY) test (Korkman et al., 1998). In this task, the child
was asked to listen to and orally repeat digitally recorded nonwords (e.g., crumsee). Scores were standard scores with a mean
of 10 and a standard deviation of 3. Test–retest reliability,
r = 0.72.
Children were tested individually in a quiet room. They learned
four regular items followed by four irregular items. For both
regular and irregular words, the same procedure was used an
initial exposure phase, learning trials and two post-tests (see
Figure 2).
During the initial exposure phase, the child was first presented
with a picture with elves and was told they were going to learn
the names of some of these elves. Next, the tester introduced the
spoken forms of the four target nonwords (“elves names”) to the
children (initial exposure). This was necessary in order to expose
the children to the pronunciations of the irregular nonwords.
After this, the child was seated in front of a computer and the
nonwords appeared on the screen one at a time. During the first
presentation on the computer screen the tester said: “The name
of this elf is ____.” The children were not asked to read or repeat
the novel words at this point and no accuracy was recorded. After
a nonword had been introduced to the child orally and in print in
the exposure phase, the first cycle of the learning trials began and
reading accuracy was recorded. The four nonwords would appear
on the screen one by one in a randomized order, and the child was
asked to read them aloud. This was the untimed exposure reading.
Feedback was provided regardless of whether the child read the
target word correctly or not, to give an equal number of phonological exposures to each word. For example, after each response
Semantics
Semantic knowledge was measured with the Peabody Picture
Vocabulary Test 4 (PPVT-IV, Dunn and Dunn, 2007). For each
item the child was presented with four pictures and asked to point
to the picture that was named by the tester. The administration of
the test was stopped when the child made more than eight errors
in a set of 12 items. Scores were standard scores with a mean of 100
and a standard deviation of 15. Test–retest reliability, r = 0.84.
Phonological lexicon
RESULTS: SUBGROUP VALIDATION
Table 1 presented the performance of the surface, phonological and control groups on the selection measures and the other
measures of reading processing skills. The two groups were significantly different on the selection measures: nonword and irregular
word reading accuracy. In addition, and as would be predicted,
the phonological group performed significantly more poorly on
the letter-sound knowledge test and the surface group performed
significantly more poorly on the orthographic knowledge test
(DOOR/DOAR). In addition, the difference on the nonword repetition test (NEPSY) approached significance (p = 0.05), with
the surface group appearing to outperform the phonological
group. However, as both groups still performed within the normal range on this task, this result is not discussed further. The
two groups did not differ on any other measure. The results of
the assessment of reading processing skills therefore confirmed
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Tracking orthographic learning in children with dyslexia
FIGURE 2 | Procedure of the orthographic learning task.
was given by the child, the experimenter said, “that’s correct, it’s a
ferb” for correct responses; or “not quite, it is a ferb” for incorrect
responses.
All three rounds of untimed reading were followed by a block
of time-limited reading (200 ms presentation, with #### as backward masks) of the target words. This set up allows us to obtain an
ongoing measure of orthographic learning (i.e., the ability to recognize words instantaneously) after each exposure (i.e., untimed
reading with plenty of time to decode the word plus feedback).
Again, all target words were presented in random order. This step
was introduced to the child as the “speed reading game.” One
block of untimed reading followed by one block of time-limited
exposure duration reading was considered a cycle, and this cycle
was repeated three times.
Post-test measures
After the three learning cycles were completed, two post-tests were
conducted to measure orthographic learning using both spelling
and orthographic choice tasks. For the spelling task, the tester
dictated all trained words in a random order. The children were
asked to write down the elves’ names exactly as they had learned
them on the computer. For the orthographic choice task, each target item (e.g., ferb) was presented together with its homophonic
foil (e.g., furb) and two visual distractors (e.g., ferq, furq) on one
A4 sheet of paper. The children were asked to choose the correct spelling of the elf ’s name that they had learned from those
four options. These two tasks were measured immediately after
the learning trials and again after an hour to increase assessment
reliability and statistical power. Thus, eight was the maximum
score across two testing points for the orthographic choice task
and spelling task for each word type—regular and irregular.
RESULTS: ORTHOGRAPHIC LEARNING
Learning cycles: untimed and time-limited exposure duration
measures
Table 2 summarizes results of the orthographic learning trials for
untimed and time-limited exposure duration reading for the two
profiles of poor readers and the controls. We aimed to examine
the improvement in learning over cycles for regular and irregular items between the three groups of children with different
reading profiles. We ran a repeated measures ANOVA with cycle
(1, 2, 3), regularity (regular items, irregular items), and exposure duration (untimed, time-limited) as within-subject factors,
and group (phonological profile, surface profile, controls) as a
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between-subject factor. We specified two planned contrasts on the
between-subject factor in order to compare the performance of
the three different groups. The first contrast compared the performance of the two poor reader groups with the controls and
the second contrast compared the performance of the two poor
reader groups.
We found a main effect of cycle, F(2, 25) = 19.46, p < 0.01,
η2p = 0.45, but no interaction between cycle and group, Fs < 1;
nor were any of the higher level interactions between cycle and
group with either regularity or/and exposure duration significant (Fs < 1). This indicates that across regular and irregular
words, untimed and time-limited exposure conditions, all three
groups improved over the learning cycles and that the degree of
improvement did not differ between the groups.
The main effect of regularity was significant, F(1, 24) = 28.39,
p < 0.01, η2p = 0.54, but the interaction between regularity and
group was not, F(1, 24) = 1.79, p = 0.19. All three groups performed better on regular words than on irregular words. However,
the interaction between regularity and exposure duration was
significant, F(1, 24) = 5.48, p = 0.03, η2p = 0.19. Considering the
patterns of means across the conditions, this interaction indicated
that for regular words, performance did not differ for untimed
and time-limited exposure duration, t(1, 26) = 0.77, p = 0.45.
However, for irregular words, performance was be better under
the time-limited condition than under the untimed exposure
duration, t(1, 26) = −2.23, p = 0.03. There was also an interaction between exposure duration and group, F(2, 24) = 4.33, p =
0.03. The interaction reflected the fact that for the control and
surface group, there were no differences between exposure duration [control: t(1, 8) = 0.54, p = 0.61; surface: t(1, 8) = 1.04, p =
0.33]; but for the phonological group performance was better in
the time-limited condition compared to the untimed condition,
t(1, 8) = 3.46, p < 0.01.
Finally, there was a main effect of group, F(2, 24) = 8.56,
p < 0.01, η2p = 0.42. The first planned contrast (both poor
reader groups vs. controls) showed that, across conditions (regular/irregular, untimed/time-limited), the controls performed better than the poor reader groups, F(1, 24) = 17.12, p < 0.01, η2p =
0.42. However, there was no difference in overall performance
between the two poor reader groups, Fs < 1.
It should be noted that the performance of the control group
is at ceiling on the regular items at the later cycles and hence did
not meet the statistical assumption of equal variance. Therefore,
we ran a nonparametric randomization test that does not make
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July 2014 | Volume 8 | Article 468 | 6
Wang et al.
Tracking orthographic learning in children with dyslexia
Table 2 | Reading accuracy across learning cycles.
Reading accuracy
Regular
Phonological
Surface
profile
profile
Irregular
Controls
Phonological
Surface
profile
profile
Controls
CYCLE 1 (RAW SCORE/4)
Untimed
2.78 (1.20)
2.56 (1.13)
3.33 (0.71)
1.22 (0.83)
1.78 (1.20)
2.78 (0.97)
Time-limited
2.67 (1.41)
2.56 (1.13)
3.89 (0.33)
1.89 (0.78)
1.78 (1.56)
3.11 (1.05)
Untimed
3.11 (1.17)
3.33 (0.71)
4.00 (0.00)
1.33 (0.87)
2.11 (1.17)
3.56 (0.53)
Time-limited
3.44 (0.73)
2.67 (1.00)
3.89 (0.33)
2.22 (0.44)
2.11 (1.36)
3.22 (0.83)
CYCLE 2 (RAW SCORE/4)
CYCLE 3 (RAW SCORE/4)
Untimed
3.33 (0.87)
3.33 (0.71)
4.00 (0.00)
2.00 (1.12)
2.11 (1.36)
3.22 (0.97)
Time-limited
3.33 (1.12)
3.22 (0.97)
3.67 (0.50)
2.33 (1.32)
2.11 (1.17)
3.56 (0.53)
TOTAL (RAW SCORE/12)
Untimed
9.33 (3.00)
9.33 (2.24)
11.33 (0.71)
4.56 (2.40)
6.00 (3.35)
9.78 (1.56)
Time-limited
9.44 (3.00)
8.44 (2.60)
11.44 (0.88)
6.44 (2.07)
6.00 (3.71)
10.00 (1.58)
any assumptions about the distribution of the data (Lunneborg,
2001). This randomization test was conducted for the regular as
well as the irregular items on the main effect of group. The results
confirmed that across cycle and exposure duration conditions,
the controls performed better than the two poor reader groups
(regular: p = 0.03; irregular: p < 0.01).
In summary, it was found that all three groups improved over
learning cycles, but across learning cycles, the controls performed
better than both poor reader groups. Importantly, the performance of the children with phonological and surface profiles did
not differ. In addition, all three groups performed better on items
with regular pronunciations than those with irregular pronunciations, and there was no difference in this regularity effect between
the surface and phonological profiles. However, we need to interpret the results with caution as the controls were at ceiling for
the regular items in the untimed reading condition. Lastly, it
was found that for irregular words but not regular words, the
performance was better under the time-limited exposure duration condition than under the untimed condition, particularly
for the phonological group. This can be explained by the fact
that the untimed condition provides an opportunity to decode a
word, and in the case of irregular items, decoding results in incorrect responses. This result indicated that the timed condition has
minimal influence from phonological decoding.
After learning cycles: spelling and orthographic choice measures
Table 3 summarizes results of the spelling and orthographic
choice measures. We ran repeated measures ANOVAs with word
regularity (regular items, irregular items) as a within-subject factor and group (phonological profile, surface profile, controls) as
a between-subject factor. We specified the same two planned contrasts on the between-subject factor to compare the performance
of the three different groups. Analyses were conducted separately
for the spelling and orthographic choice tasks.
For the spelling task, the main effect of regularity approached
significance, F(1, 24) = 4.20, p = 0.052, η2p = 0.15, and there was
no interaction between regularity and group (Fs < 1). The main
Frontiers in Human Neuroscience
Table 3 | Accuracy on Spelling and Orthographic Choice Measured
after the Learning Cycles (with SDs in brackets).
Phonological
Surface
profile
profile
Controls
REGULAR ITEMS (RAW SCORE/8)
Spelling
6.67 (1.58)
5.33 (2.00)
7.33 (1.12)
Orthographic choice
7.67 (0.50)
5.44 (1.94)
7.67 (0.71)
IRREGULAR ITEMS (RAW SCORE/8)
Spelling
6.44 (0.88)
4.44 (2.19)
6.33 (1.12)
Orthographic choice
7.11 (1.05)
4.89 (2.15)
7.22 (0.97)
effect for group was significant, F(2, 24) = 6.11, p < 0.01, η2p =
0.34. The planned contrasts showed that this group main effect
was reflecting the significantly lower performance of the surface
group compared to the controls, F(1, 24) = 10.44, p < 0.01, η2p =
0.30, as well as the phonological group, F(1, 24) = 7.67, p = 0.01,
η2p = 0.24. The phonological group on the other hand, performed
equally as well as the controls, Fs < 1.
For the orthographic choice task, the difference between regular and irregular items was not significant, F(1, 24) = 2.61, p =
0.12, nor was the interaction between word regularity and group,
Fs < 1. As with spelling performance, there was a main effect
of Group, F(2, 24) = 12.97, p < 0.01, η2p = 0.52. The planned
contrasts showed that the surface group was worse on the orthographic choice task compared to the controls, F(1, 24) = 19.93,
p < 0.01, η2p = 0.45, and compared to the phonological group,
F(1, 24) = 18.97, p < 0.01, η2p = 0.44. Again, the phonological
group performed at the same level as the controls, Fs < 1. An
additional analysis confirmed that all three groups performed
above chance level (25% accuracy), even the surface group,
t(1, 8) = 7.54, p < 0.01.
In summary, the pattern of results of the spelling task was
consistent with that of the orthographic choice task. For both
tasks, the phonological group did not perform differently from
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July 2014 | Volume 8 | Article 468 | 7
Wang et al.
Tracking orthographic learning in children with dyslexia
the controls whereas the surface group was worse than both the
phonological group and the control group. However, in contrast
to the findings of the learning trials, the children did not perform better on the regular items than on the irregular items for
the orthographic choice task and the difference in performance
only approached significance for the spelling task.
DISCUSSION
The first question of interest in Study 1 was whether children with
reading difficulties are able to learn novel words at all or to learn
at the same pace as typically developing readers. We found that,
just like the controls, children with both types of reading profile
showed learning over the learning cycles as evidenced by untimed
and time-limited reading accuracy, and the rate of improvement
was no different from controls. This is in contrast with previous studies that have found little or no evidence of orthographic
learning in children with dyslexia (Reitsma, 1983, 1989; but also
see Staels and van den Broeck, 2013). The difference between the
results of the present and previous studies may be explained by
the sensitivity of the measure of orthographic learning. In the
current study orthographic learning was monitored online during the exposure trials, whereas Reitsma (1983, 1989) found that
Dutch poor readers showed no evidence of orthographic learning measured by an orthographic choice task after learning took
place. However, we found that although the two dyslexic groups
showed evidence of orthographic leaning, their reading accuracy
overall was worse than the controls, and this is in line with previous studies using a group of mixed dyslexics (e.g., Manis, 1985;
Share and Shalev, 2004).
The second question of interest was to explore the contrasting
predictions made by the hypothesis of phonological decoding as
the primary factor in orthographic learning vs. that of the children’s reading profiles. According to the phonological decoding
hypothesis, the phonological group, with their impaired phonological decoding skill, would be predicted to perform less well
than the controls and the surface group. In contrast, the subtype
profiles of the two groups predict that the phonological group
would be better at orthographic learning than the surface group
based on their superior sight word reading ability. The results of
the present study did not fully support either of the hypotheses but was more consistent with the children’s subtype profiles.
The performance of the two poor reader groups did not differ on either untimed or time-limited exposure duration reading
accuracy. However, the phonological group was found to be no
different from the control group and to outperform the surface
group on the subsequent spelling and orthographic choice task.
The surface group was also significantly worse than the controls
on the spelling and orthographic choice tasks. It should be noted
that both the control and the phonological group performed close
to ceiling on the orthographic choice tasks, hence making the
differences between the two groups hard to detect. Nevertheless,
the orthographic learning results of these two groups were in
line with the selective difficulties in their reading profiles within
the framework of the dual route model. The results of Study 1
thus showed that the difference in phonological decoding skill
between the two subtype profiles did not directly translate into
differences in the ability to acquire novel word representations,
Frontiers in Human Neuroscience
which is in contrast with the hypothesis that phonological
decoding is the primary determinant of successful orthographic
learning.
It is important to note that the surface group did not outperform the phonological group on untimed reading accuracy and
did perform worse than the control group. We did not expect
this result as we preselected the surface group to have average
phonological decoding skills. The finding that the two poor reader
groups did not differ in untimed reading accuracy might be
explained by the test items that were used. For this task we used
only four, regular, four to five letter nonwords. It might be the
case that the test was not sensitive enough and/or might not have
had enough statistical power to differentiate between the two poor
reader groups. In addition, the finding that the surface group was
worse at decoding the novel words than the control group could
be due to the superior decoding ability of the control group. That
is, although the surface group was within the average range on
the nonword reading selection measure (z = −0.57, SD = 0.21),
they were still on average worse than controls (z = 0.15, SD =
0.66), t(1, 17) = −2.47, p = 0.03.
The last aim of Study 1 was to examine the effect of word regularity on orthographic learning. All groups showed higher reading
accuracy when learning regular items than when learning irregular items. This implies that phonological decoding was used by
both poor reader groups, as well as the typical readers, during
the orthographic learning process. Together these results suggest
that phonological decoding plays a role in orthographic learning
for both subtype groups, yet it is also clear that this skill is not
sufficient to fully account for the success of orthographic learning.
If phonological decoding skill cannot fully explain successful
orthographic learning then what are the other factors determining this learning process? In order to further explore orthographic
learning in poor readers, the second half of this study investigated
the predictors of orthographic learning beyond phonological
decoding. It has been proposed that poor readers could be relying on alternative learning strategies in order to compensate for
poor phonological decoding skills (Stanovich and Siegel, 1994;
Siegel et al., 1995; Castles et al., 1999). For example, it is possible that for children who have difficulties with phonological
decoding, vocabulary knowledge is relied on more heavily during orthographic learning. In support of this, previous studies
have found that when decoding can only be partially successful,
in the case of irregular novel words, contextual information and
vocabulary knowledge play a role in orthographic learning (Wang
et al., 2011, 2013; Duff and Hulme, 2012). Similarly, word meaning has also been found to assist the reading of irregular words
(e.g., Nation and Snowling, 1998; Ouellette, 2006; Bowey and
Rutherford, 2007; Ricketts et al., 2007; McKay et al., 2008; Nation
and Cocksey, 2009).
In addition to phonological decoding skills and vocabulary knowledge, pre-existing orthographic knowledge has also
been considered an important factor in orthographic learning
(Cunningham et al., 2002; Conners et al., 2010). Share (1995) suggested that although phonological decoding is the primary component of orthographic learning, the secondary, orthographic
component determines how quickly and accurately orthographic
representations are acquired.
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July 2014 | Volume 8 | Article 468 | 8
Wang et al.
Tracking orthographic learning in children with dyslexia
In sum, we aimed to develop a more detailed picture of
the reading processes associated with orthographic learning by
exploring the strengths of the relationship between different reading and language skills and orthographic learning of regular and
irregular words.
STUDY 2
For the purpose of exploring how well different skills involved
in reading predict orthographic learning of regular and irregular words within a larger group of poor readers, we again drew on
the language and reading skills in the dual route model as we did
for subgroup validation in Study 1 (see Figure 1 earlier).
As noted earlier, phonological decoding is often assumed to
be the key to orthographic learning. Based on this hypothesis, we
predicted that skills reflecting nonlexical processing (phonological decoding in particular) would be important for orthographic
learning of both regular words and irregular words. However,
when accurate decoding is compromised or not possible (such as
when phonological decoding skill is impaired or when words are
irregular), knowledge of semantics, phonology or orthography
may become more important.
METHODS
Participants
The same cohort of 91 poor readers screened in Study 1 participated in Study 2. As mentioned in Study 1, these children scored
at least one standard deviation below average for their age on one
or more of the two subscales (irregular word and nonword reading) of the Castles and Coltheart 2 word reading test (CC2; Castles
et al., 2010a). On average, the children scored −1.59 (SD = 0.65)
on nonword reading; and −1.40 (SD = 0.67) on irregular word
reading.
Materials and procedure
We assessed the poor readers on tasks tapping the six basic components of the dual-route model. In addition, all children completed the same orthographic learning task described in Study 1.
In order to increase statistical power for the analyses used in
Study 2, we created another set of nonword stimuli, consisting
of an additional four regular and four irregular items. The extra
set of items was created in the same way as described in Study 1.
The same procedure of the orthographic learning task was applied
in a separate session 8 weeks after the first set of nonwords were
learnt. Each child was tested individually in a quiet room, and
the children took approximately 100–120 min to complete all the
assessments. The results of the six reading and language skill
measures were used as predictors, and the orthographic learning
performances were used as outcome measures.
RESULTS
To investigate how well each reading subcomponent predicts
orthographic learning, a set of correlations, followed by stepwise
multiple regressions, was conducted with the dual route processing components as predictors, and the various orthographic
learning measures as the dependent variables. Regressions were
carried out in addition to correlations as the predictor tasks are
themselves intercorrelated, in order to identify the relationship
Frontiers in Human Neuroscience
between these factors and orthographic learning outcomes when
the intercorrelations between the variables are controlled.
Table 4 shows the results of a series of correlations and partial
correlations controlling for age and non-verbal IQ between the
outcomes of the orthographic learning task (outcome measures:
no. 1–8) and the components involved in lexical, nonlexical, and
both routes (predictors: no. 9–14). Before the effects of age and
non-verbal IQ were partialled out, all of the components involved
in the reading routes correlated with almost all measures of orthographic learning. After controlling for age and IQ, the main
difference is that the associations between orthographic learning measures and semantic knowledge (PPVT), and phonological
lexicon functioning (ACE) were no longer significant.
The results of the regression analyses are summarized in
Table 5. In the first step, age and non-verbal IQ were entered,
followed by all the other potential predictor variables at step 2.
Overall, letter-sound knowledge and orthographic knowledge
seemed to be the best predictors of orthographic learning.
Untimed exposure duration reading accuracy was predicted by
letter-sound knowledge and phonemic buffer efficiency. Timelimited exposure duration reading accuracy was predicted by
letter-sound knowledge, phonemic buffer efficiency, and wordspecific orthographic knowledge (functioning of orthographic
lexicon). Spelling accuracy was predicted by letter-sound knowledge and orthographic knowledge, whereas orthographic choice
accuracy was only predicted by orthographic knowledge. For
irregular items, orthographic knowledge was the only significant predictor for all measures except untimed exposure duration
reading accuracy, which was predicted by both orthographic
knowledge and letter-sound knowledge.
It should be noted that although it seemed that letter-sound
knowledge was a better predictor of performance for the regular
items than the irregular items, and that orthographic knowledge
was a better predictor for irregular items than regular items,
these correlational differences between regular and irregular items
did not reach significance. However, across the two predictors,
orthographic knowledge was a significantly better predictor than
letter-sound knowledge for scores on spelling and orthographic
choice measures, regardless of word regularity (regular spelling:
z = −2.17, p = 0.03; irregular spelling: z = −2.30, p = 0.02;
regular orthographic choice: z = −2.82, p < 0.01; irregular
orthographic choice: z = −3.67, p < 0.01).
DISCUSSION
The results showed that letter-sound knowledge predicted the
outcomes of all measures assessing regular word learning except
for orthographic choice. Letter-sound knowledge also predicted
the untimed reading accuracy during irregular word learning, but
it did not predict any other measures assessing irregular word
learning. The ability to repeat nonwords was used as an index
of phonemic buffer proficiency, and performance on this task
predicted orthographic learning of regular words when learning was measured by accuracy in reading aloud (both timed and
untimed).
Our measure of orthographic knowledge predicted success on
our dynamic measures of orthographic learning of both regular and irregular words except for untimed reading accuracy of
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July 2014 | Volume 8 | Article 468 | 9
Wang et al.
Tracking orthographic learning in children with dyslexia
Table 4 | Correlations of the outcome measures and the predictors with (below the diagonal line) and without (above the diagonal line) age
and nonverbal IQ controlled.
Regular
Irregular
Predictors
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
–
0.93
0.68
0.44
0.60
0.69
0.63
0.55
0.45
0.60
0.61
0.41
0.41
0.52
0.53
0.43
2. Time-limited reading
0.89
–
0.71
0.52
0.56
0.69
0.65
0.59
0.46
0.55
0.64
0.40
0.43
0.48
0.52
0.40
3. Spelling
0.57
0.65
–
0.60
0.42
0.61
0.71
0.62
0.48
0.47
0.65
0.35
0.32
0.38
0.45
0.45
4. Orthographic choice
0.38
0.48
0.62
–
0.47
0.56
0.56
0.62
0.46
0.40
0.46
0.19
0.29
0.39
0.17
0.28
1. Untimed reading
5. Untimed reading
0.53
0.47
0.33
0.45
–
0.85
0.63
0.56
0.32
0.45
0.49
0.29
0.31
0.34
0.36
0.18
6. Time-limited reading
0.58
0.58
0.53
0.53
0.83
–
0.73
0.65
0.50
0.47
0.58
0.36
0.36
0.41
0.44
0.28
7. Spelling
0.48
0.53
0.59
0.52
0.60
0.69
–
0.73
0.53
0.53
0.62
0.37
0.31
0.32
0.40
0.46
8. Orthographic choice
0.38
0.44
0.53
0.61
0.50
0.56
0.68
–
0.51
0.46
0.63
0.41
0.32
0.30
0.41
0.39
9. Letter analysis
0.25
0.27
0.36
0.38
0.21
0.38
0.39
0.37
–
0.36
0.44
0.27
0.27
0.31
0.39
0.44
10. Letter-sound know.
0.45
0.38
0.33
0.32
0.36
0.33
0.39
0.34
0.19
–
0.41
0.38
0.38
0.32
0.39
0.33
11. Orthog. lexicon
0.41
0.47
0.52
0.44
0.35
0.43
0.49
0.51
0.27
0.22
–
0.52
0.49
0.41
0.55
0.38
12. Semantics
0.09
0.08
0.05
0.07
0.09
0.11
0.11
0.14 −0.01
0.20
0.26
–
0.67
0.30
0.22
0.48
13. Phono. lexicon
0.09
0.11
0.07
0.17
0.14
0.13
0.03
0.06 −0.01
0.16
0.24
0.50
–
0.31
0.49
0.44
14. Phonemic buffer
0.51
0.44
0.39
0.35
0.30
0.36
0.28
0.24
0.25
0.26
0.39
0.25
0.23
–
0.16
0.13
15. Age
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
0.48
16. Non-verbal IQ
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
Predictors 9–14 refer to the six reading components tested by: Cross-case copying (Letter Analysis, 9); Letter-sound test (Letter-sound knowledge; 10); Door/Doar
Lexical Decision (Orthographic lexicon, 11); PPVT (Semantic knowledge, 12); ACE (Phonological lexicon, 13); NEPSY (Phonemic buffer, 14); Age (15); Non-verbal IQ
(K-Bit, 16).
Values represent Pearson’s correlation coefficients; numbers in bold indicate p < 0.05, 2-tailed.
regular words. The fact that orthographic knowledge predicted
time-limited but not untimed accuracy in reading the regular
items is interesting as it suggests that, when rapid and fluent access
to the orthographic representation is required, orthographic
knowledge may play a more important role than when reading is
untimed. In addition, orthographic knowledge was a better predictor than letter-sound knowledge when orthographic learning
was measured by spelling and orthographic choice tasks. Finally,
in contrast to the prediction that poor readers may use alternative
skills such as vocabulary knowledge when learning to read, better
functioning of the semantic system and/or phonological lexicon
did not predict better orthographic learning.
GENERAL DISCUSSION
In this paper we examined orthographic learning in poor readers. Study 1 focused on orthographic learning of regular and
irregular novel words in children with surface and phonological
reading profiles. We developed a novel paradigm to track orthographic learning online. Participants were first asked to read the
presented novel words untimed, then the items were presented
under time-limited exposure duration of 200 ms. This cycle was
repeated three times and followed by spelling and orthographic
choice tasks. This set up allowed us to track orthographic learning more dynamically (i.e., untimed and time-limited reading
accuracy) than traditional measures (such as spelling and orthographic choice), that typically take place after learning has taken
place. With our novel and traditional measures of orthographic
learning, we aimed to examine the role of phonological decoding
in orthographic learning.
More specifically, we wanted to investigate whether phonological decoding is primary in orthographic learning as is widely
Frontiers in Human Neuroscience
proposed (e.g., Brady and Shankweiler, 1991; Byrne, 1992, 1998;
Share, 1995). In this context, the orthographic learning of two
subgroups is particularly interesting: children with specific difficulties with phonological decoding (a phonological profile)
and those with specific difficulties in orthographic knowledge
(a surface profile). If phonological decoding is indeed primary
to orthographic learning, this should result in poorer orthographic learning in children with a phonological profile and
normal orthographic learning in children with a surface profile.
In addition to our measures of orthographic learning, we also
examined the degree to which the different poor reader groups
relied on phonological decoding by comparing the difference in
their performance on regular and irregular word learning.
We found that children with phonological and surface profiles showed the same amount of orthographic learning on the
dynamic measures (scores on untimed and time-limited trials).
However, orthographic learning was still less efficient overall
compared to that of the age-matched controls. This finding is
consistent with previous studies suggesting that poor readers take
longer to learn to read novel words (e.g., Manis, 1985; Share and
Shalev, 2004). The results of our study add evidence that this less
efficient orthographic learning is already apparent during online
learning trials. The finding that children with a surface profile
have superior phonological decoding ability but did not outperform children with a phonological profile seems to be inconsistent
with the view of phonological decoding as the primary factor for
orthographic learning. However, a key feature of the self-teaching
hypothesis can also explain this finding. According to this
hypothesis, orthographic learning is item based. Consequently,
what is relevant to the success of orthographic learning is the
correct decoding of the items to be learnt rather than one’s
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July 2014 | Volume 8 | Article 468 | 10
Wang et al.
Tracking orthographic learning in children with dyslexia
Table 5 | Summary of regression results predicting orthographic learning of regular and irregular words from lexical and nonlexical processing
components.
Step
R2
Beta
Age
Nonverbal
Letter
Letter-sound
Orthogr-aphic
Semantic
Phonological
Phonological
IQ
analysis
knowledge
knowledge
knowledge
knowledge
buffer
0.17
0.43*
0.18
−0.19
0.14
0.34*
0.11
0.34*
0.32*
−0.25
0.06
0.28*
0.05
0.27*
0.54*
−0.13
−0.10
0.17
0.01
0.15
0.52*
−0.06
−0.07
0.15
0.21
0.29*
0.40*
−0.23
0.10
0.14
0.11
0.18
0.37*
−0.12
0.20
0.21
0.10
0.20
0.50*
−0.04
0.00
0.07
0.09
0.08
0.56*
0.05
0.01
−0.05
REGULAR ITEMS
Untimed reading
Time-limited reading
Spelling
Orthographic choice
1
0.22
0.34*
0.23
2
0.66
0.20
0.04
1
0.19
0.35*
2.57
2
0.54
0.24
0.04
1
0.22
0.15
0.40*
2
0.57 −0.07
0.29*
1
0.15
0.05
0.37*
2
0.43 −0.16
0.28*
IRREGULAR ITEMS
Untimed reading
Time-limited reading
Spelling
Orthographic choice
1
0.13
0.30*
0.12
2
0.46
0.15
0.00
1
0.17
0.33*
0.16
2
0.51
0.15
0.02
1
0.28
0.19
0.44*
2
0.57 −0.06
0.31*
1
0.31
0.25
0.42*
2
0.59 −0.04
0.32*
*p < 0.05.
phonological decoding ability in general. On the untimed trials
in our dynamic measure, the children with a surface profile did
not decode the words better than the children with a phonological
profile. In other words: they did not show superior decoding skills
on this task to start with. Hence, in this regard it is not surprising
that they did not do better than the children with phonological
dyslexia on the time-limited exposure duration trials.
Our findings on the traditional measures (spelling and orthographic choice) painted a different picture. Here the children with
a surface profile performed more poorly than both the children
with a phonological profile and the controls, which is consistent
with what was found by Castles and Holmes (1996). This result
is also consistent with the prediction based on the two groups’
reading difficulties within the framework of the dual route model:
the phonological group had normal sight word reading ability
and the surface group had impaired sight word reading ability.
Moreover, the children with a phonological profile performed as
well as the controls despite their poorer performance on reading accuracy during the learning trials. This imbalance between
decoding performance and orthographic learning results suggests
again that orthographic learning ability cannot be explained by
phonological decoding ability alone.
One possible explanation for the inconsistent performance
of the phonological group across dynamic and traditional measures is that different task demands are imposed by the different
Frontiers in Human Neuroscience
orthographic learning tasks used for this study. More specifically, the dynamic measures (untimed and time-limited reading
aloud) required verbal output whereas the traditional measures
(spelling and orthographic choice) did not. As phonological
impairment in reading is often associated with deficit in verbal output (Hulme and Snowling, 1992; Szenkovits and Ramus,
2005), it is not surprising that the phonological group performed
worse than typical readers on measures requiring verbal output
compared to those not requiring verbal output. This explanation is also consistent with findings from Study 2 (results will be
discussed in more detail later), where performance on dynamic
measures was more strongly associated with letter-sound knowledge and phonemic buffer functioning than was performance on
traditional measures.
The role of phonological decoding in orthographic learning
was also examined by manipulating word regularity. We found
word regularity effects for the dynamic measures (untimed and
time-limited reading) but not for the traditional post-test measures (spelling and orthographic choice). Moreover, these effects
were the same for all groups. The regularity effect found in
dynamic measures suggests that phonological decoding does play
a role during the learning process, even for children with a
phonological profile. However, the word regularity effect was not
significant for any of the post-test measures. The absence of a regularity effect for spelling and orthographic choice is in line with
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July 2014 | Volume 8 | Article 468 | 11
Wang et al.
Tracking orthographic learning in children with dyslexia
outcomes of studies examining regularity effects in word recognition. These studies showed that regularity effects are restricted
to tasks involving reading aloud and typically not found in word
recognition tasks, such as lexical decision (e.g., Coltheart et al.,
1979; Seidenberg et al., 1984; Schmalz et al., 2013; but see Parkin,
1982).
Study 2 explored to what degree the different skills that underlie reading predicted orthographic learning of regular and irregular words in poor readers. We selected the underlying component
skills from the dual-route model of reading as predictors, and as
outcome variables we used the dynamic and traditional orthographic learning measures. According to the view that phonological decoding is the primary factor to successful orthographic
learning, we expected that skills reflecting nonlexical processing
(letter-sound knowledge in particular) would be stronger predictors of orthographic learning of both regular and irregular words
than skills reflecting lexical processing. We found that lettersound knowledge is indeed a strong predictor of orthographic
learning.
However, we did not find letter-sound knowledge to be a
stronger predictor of orthographic learning than skills reflecting
lexical processing. In fact, we found that orthographic knowledge,
a lexical processing factor, was a good predictor of both regular
and irregular word learning, and an even better predictor than
letter-sound knowledge for spelling and orthographic choice. The
association between orthographic knowledge and orthographic
learning appeared to be particularly evident when the measure of
learning directly tapped word-specific (spelling and orthographic
choice) and fluent (timed-limited reading) access of orthographic
representations.
Thus, both Study 1 and 2 showed that orthographic knowledge was associated with success in orthographic learning. In
Study 1, we found that children with average phonological
decoding skill but good orthographic knowledge showed normal orthographic learning on the traditional learning measures.
In contrast, children with an opposite reading profile—impaired
orthographic knowledge but good phonological decoding skill
showed impaired orthographic learning. In Study 2, we found
that orthographic knowledge significantly predicted orthographic
learning even after phonological decoding skills were controlled
for. Orthographic knowledge also appeared to be a stronger
predictor than phonological decoding skill when orthographic
learning was measured by traditional measures (spelling and
orthographic choice). Together these findings suggest that orthographic knowledge is not only important in orthographic learning, but also that having impaired orthographic knowledge
could be more detrimental than having impaired phonological decoding skill when learning new words. The importance
of orthographic knowledge in orthographic learning has also
been reported in previous studies with typically developing readers (Cunningham, 2006; Conners et al., 2010). Similarly, the
self-teaching hypothesis suggests that, although phonological
decoding provides the opportunity for orthographic learning
to take place, orthographic knowledge is the secondary factor
required for successful orthographic learning (Share, 1995, 2011).
Our results support the view that orthographic knowledge is
important in orthographic learning, but challenge the view that
orthographic knowledge is a “secondary” factor. It seems that
Frontiers in Human Neuroscience
orthographic knowledge may actually be equally important or
even more important than phonological decoding in building up
orthographic representations.
However, it must be considered in this context that there
are two alternative explanations as to why orthographic knowledge might be a significant predictor of orthographic learning.
First, the way orthographic knowledge was measured in this
study could be seen as a measure of the children’s historic ability
to acquire orthographic representations. Hence, the relationship between orthographic knowledge and orthographic learning
could simply be that both are a reflection of the children’s ability to acquire lexical representations: children with better abilities
to acquire orthographic representations will be better at both
the task tapping the orthographic lexicon (as a result of past
orthographic learning) and at our orthographic learning task
(current orthographic learning). Second, it could be that existing
orthographic representations contribute to the actual process of
acquiring new representations with children using this knowledge
during the learning process. This might occur by using analogies
of known words or utilizing familiarity of orthographic patterns.
Further research is required to investigate how exactly existing
orthographic knowledge assists orthographic learning of novel
words.
As mentioned earlier, one might expect that for poor readers, the success of orthographic learning might rely on alternative
skills, such as semantics, compensating for poor phonological
decoding skills. In contrast to this prediction, this study did
not find any evidence that pre-existing semantic and phonological knowledge (measured by PPVT-IV and ACE6-11) predicted
orthographic learning of regular or irregular words. However, the
orthographic learning paradigm in this study did not provide
word-specific vocabulary (semantic and phonological) knowledge for the novel words. Hence, there was little opportunity for
the children to use such skills. It would be interesting for future
studies to explore whether word-specific vocabulary knowledge
affects orthographic learning in poor readers. Further investigation using a design that provides vocabulary knowledge of the
novel words prior to written exposure, such as that used in Wang
et al. (2011, 2013), is needed.
There are a number of limitations of the present study that
require further consideration. First, due to the manipulation of
word regularity, the items were read to the children as an initial exposure before the learning cycles started, and during the
learning cycles feedback was provided. Consequently, although
after the initial exposure the children were asked to first read
the target words by themselves to simulate a partial self-teaching
paradigm, it was not an independent learning environment.
Therefore, it is possible that the results would have been different had the children learned the words without the experimenter’s input. For example, the children with a phonological
dyslexia profile may have benefited from the input and feedback to compensate for their poor decoding of the regular items,
resulting in their untimed and time-limited reading accuracy not
being different from the children with a surface dyslexia profile.
Although providing feedback is still ecologically valid, as children
often receive feedback when learning to read, particularly with
irregular words, we cannot interpret the results in the context of
pure self-teaching.
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July 2014 | Volume 8 | Article 468 | 12
Wang et al.
Tracking orthographic learning in children with dyslexia
Second, it is possible that factors beyond phonological decoding and orthographic knowledge influenced the pattern of
impaired orthographic learning in surface dyslexia and normal
orthographic learning in phonological dyslexia. According to the
self-teaching hypothesis (Share, 1995), phonological decoding
draws the reader’s attention to the order and identity of the letters in the word, and produces the phonology. This then allows
bonding to occur between the phonology and the orthography
via some kind of associative learning procedure. It is possible
that differences in the ability to establish associations between
phonology and orthography are the source of the difference in
orthographic learning skill between the two subtypes of dyslexia.
In studies that did not make distinctions between subtypes,
children with dyslexia were found to have difficulties in learning paired associations (Gascon and Goodglass, 1970; Vellutino
et al., 1975; Messbauer and de Jong, 2003; Litt et al., 2013). In
addition, other abilities may also contribute to individual differences in learning to read, such as mapping the output of lettersound correspondences to existing phonology of a word (e.g.,
was: from/w..aa..ss/. . . to /woz/; Elbro et al., 2012), and capitalizing contextual and syntactic information (Tunmer et al., 1987;
Tunmer and Chapman, 2004). Future studies are required to further investigate orthographic learning in children with subtypes
of reading profiles.
In sum, Study 1 used a novel paradigm that allowed us to
explore the role of phonological decoding and track orthographic
learning in two groups of poor readers who had contrasting
reading impairments. The two poor reader groups showed orthographic learning patterns that were consistent with their reading
profiles, which suggested that phonological decoding skill alone
is insufficient for acquiring orthographic representations. Study
2 was the first to break down components of reading processes
based on the dual route model of reading and to use these components to explore factors associated with orthographic learning.
The results of this study indicated that, in addition to phonological decoding (letter-sound knowledge), prior orthographic
knowledge also predicted the success of orthographic learning.
Together, the outcomes of the two studies suggest that phonological decoding plays a role in orthographic learning of both
regular and irregular words, and for children with and without
phonological decoding difficulties. Orthographic knowledge was
also found to be important in orthographic learning, especially
when the measures of learning directly tapped word-specific and
fluent access to orthographic representations.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: http://www.frontiersin.org/journal/10.3389/fnhum.
2014.00468/abstract
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Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 19 December 2013; accepted: 08 June 2014; published online: 04 July 2014.
Citation: Wang H-C, Marinus E, Nickels L and Castles A (2014) Tracking orthographic learning in children with different profiles of reading difficulty. Front. Hum.
Neurosci. 8:468. doi: 10.3389/fnhum.2014.00468
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July 2014 | Volume 8 | Article 468 | 14