The neural bases of the pseudohomophone effect: phonological ... · Accepted Manuscript The neural...
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Accepted Manuscript
The neural bases of the pseudohomophone effect: phonological constraints onlexico-semantic access in reading
Mario Braun, Florian Hutzler, Thomas F. Münte, Michael Rotte, MichaelDambacher, Fabio Richlan, Arthur M. Jacobs
PII: S0306-4522(15)00262-6DOI: http://dx.doi.org/10.1016/j.neuroscience.2015.03.035Reference: NSC 16145
To appear in: Neuroscience
Accepted Date: 17 March 2015
Please cite this article as: M. Braun, F. Hutzler, T.F. Münte, M. Rotte, M. Dambacher, F. Richlan, A.M. Jacobs,The neural bases of the pseudohomophone effect: phonological constraints on lexico-semantic access in reading,Neuroscience (2015), doi: http://dx.doi.org/10.1016/j.neuroscience.2015.03.035
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The neural bases of the pseudohomophone effect:
phonological constraints on lexico-semantic access
in reading.
Mario Braun1, Florian Hutzler
1, Thomas F. Münte
3, Michael Rotte
4, Michael
Dambacher2,5
, Fabio Richlan1 and Arthur M. Jacobs
2,6
1Centre for Cognitive Neuroscience, University of Salzburg, Austria
2Institute of Psychology, Freie Universität Berlin, Germany
3Dept. of Neurology and Institute of Psychology II, University of Lübeck, Germany
4Novartis Pharma, Basle, Switzerland
5Dept. of Psychology, University of Konstanz, Germany
6Dahlem Institute for Neuroimaging of Emotion, Berlin, Germany
Keywords: Models of visual word recognition, fMRI, lexical decision, phonological mediation,
lexico-semantic access, spelling-check.
Address correspondence to:
Mario Braun
Centre for Cognitive Neuroscience
Universität Salzburg
Hellbrunner Straße 34
5020 Salzburg Austria
phone: ++43.662.8044.5176
fax: ++43.662.8044.5126
email: [email protected]
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Abstract
We investigated phonological processing in normal readers to answer the question to what
extent phonological recoding is active during silent reading and if or how it guides lexico-semantic
access. We addressed this issue by looking at pseudohomophone and baseword frequency
effects in lexical decisions with event-related functional magnetic resonance imaging (fMRI). The
results revealed greater activation in response to pseudohomophonesthan for well controlled
pseudowords in the left inferior/superior frontal and middle temporal cortex, left insula, and left
superior parietal lobule. Furthermore, we observed a baseword frequency effect for
pseudohomophones (e.g., FEAL) but not for pseudowords (e.g., FEEP). This baseword frequency
effect was qualified by activation differences in bilateral angular and left supramarginal, and
bilateral middle temporal gyri for pseudohomophones with low- compared to high-frequency
basewords. We propose that lexical decisions to pseudohomophones involves phonology-driven
lexico-semantic activation of their basewords and that this is converging neuroimaging evidence
for automatically activated phonological representations during silent reading in experienced
readers.
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Introduction
Almost everyone who learns to read is already able to speak and to understand spoken language.
The first language code with which we are confronted is of phonological nature. In the process of
learning to read, written letters are mapped to their phonological representations by a process of
phonological recoding. In reading aloud phonological recoding is a necessary condition, since the
orthographic code has to be translated into its phonological counterpart to allow for speech
output. Most researchers agree that phonological recoding takes place in both, spoken and
written language processing, but it is still a matter of debate to what extent it is also active in
experienced readers (e.g., Ziegler et al, 2013; Hawelka et al., 2013; Frisson et al., 2014) and
whether phonological recoding is automatically activated and guides access to lexico-semantic
representations. In principle, phonological recoding could be abandoned after stable
representations of visual word forms have developed and a skilled reader could determine the
meaning of words directly from knowledge of its spelling. However, there is behavioral,
electrophysiological, and neuroimaging evidence that the first language code with which we are
confronted remains active in silent reading (e.g., Van Orden, 1987; Ziegler et al., 2000; Ziegler et
al., 2001a; Braun et al., 2009; Briesemeister et al., 2009; Wheat et al., 2010; Pammer et al., 2004;
Perrone-Bertolotti et al., 2012; Jäncke and Shah, 2004; Alexander and Nygaard, 2008; Yao, et al.,
2011).
Direct Access vs. Phonological Mediation
Two main views exist on how we gain access to the meaning of words in silent reading. The direct
access hypothesis states that there is a direct pathway from orthography to meaning (e.g.,
Seidenberg, 1985), and that if phonological recoding is performed in silent reading it must be
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post-lexical (i.e., after meaning is computed). In contrast, the phonological mediation hypothesis
(e.g., Van Orden, 1987) claims that phonological activation is a necessary condition to gain access
to word meaning. Accordingly, phonology is assumed to be activated automatically prior to
lexical access of word meaning and thus taking place early during visual word recognition (for
reviews see Berent and Perfetti, 1995; Frost, 1998).
Pseudohomophone Effect
Evidence for phonological mediation is provided by the pseudohomophone effect (Rubenstein et
al., 1971; McCann et al., 1988; Seidenberg et al., 1996) which describes the fact that a nonword
(e.g., FEAL) which shares phonology but not orthography with a word (e.g., FEEL) leads to slower
RTs compared to another nonword (e.g., FEEP). "FEEP" differs in phonology but its orthography is
equally similar to the real word "FEEL". The pseudohomophone effect is used as a marker of
phonological activation in reading development (e.g., Goswami et al., 2001) and provides major
constraints for computational models of visual word recognition (Jacobs and Grainger, 1994;
Jacobs et al., 1998; Seidenberg et al., 1996; Ziegler and Jacobs, 1995; Ziegler et al., 2001b). The
standard explanation for the pseudohomophone effect is that a given pseudohomophone
contacts the lexical representation of its phonologically identical baseword in a mental lexicon,
which in turn activates semantic information associated with that representation. Thus, the
'phonological lexicon' and co-activated semantics of the baseword signal the presence of a word,
whereas the 'orthographic lexicon' signals its absence. In lexical decision, it is assumed that
resolving this conflict takes time and therefore participants show longer response times for
pseudohomophones compared to pseudowords (Jacobs et al., 1998; Ziegler et al., 2001b).
Furthermore, baseword frequency effects provide evidence for a lexical locus of the
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pseudohomophone effect (see below).
Baseword Frequency
The baseword frequency effect states that pseudohomophones with low frequency basewords
lead to slower response times than those with high baseword frequencies in lexical decision (e.g.,
Ziegler et al., 2001b; Reynolds and Besner, 2005), but also in semantic categorization and
proofreading (Ziegler et al., 1997). A baseword frequency effect for pseudohomophones could be
interpreted as reflecting the activation of frequency sensitive representations of the basewords
through the pseudohomophone’s identical phonology suggesting phonology driven lexical access
(e.g., Forster and Chambers, 1973).
Models of Visual Word Recognition
The first computational models simulating the pseudohomophone effect, were the multiple read-
out model including phonology (MROM-P; Jacobs et al., 1998) and the dual-route cascaded model
(DRC; Coltheart et al., 2001). Both explain the pseudohomophone effect in lexical decision in
terms of higher summed global lexical activity in a phonological lexicon which prolongs a temporal
deadline generating longer response times for pseudohomophones compared to pseudowords.
Both make the same predictions concerning the baseword frequency effect. Pseudohomophones
with low frequency basewords should lead to faster responses because pseudohomophones with
high frequency basewords should elicit higher activation leading to stronger word present signals
resulting in more errors and slower responses in lexical decisions. However, the empirical findings
show reversed effects: responses to pseudohomophones with high frequency basewords are
faster than to those with low frequency basewords (e.g., Ziegler et al., 2001). In the light of this
finding a spelling-check was proposed to account for the baseword frequency effect (e.g., Paap et
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al., 1982). The idea is that pseudohomophones activate high frequency basewords faster, because
these have higher resting levels and are therefore accessed more easily in an orthographic lexicon
allowing for a faster spelling-check. Unfortuneately, the models mostly do not directly speak to
brain regions assumed to process certain kinds of information or to the processing strength of
specific stimuli in specific tasks. Nevertheless, recent neuroimaging research has made progress to
more directly link the output of computational models to brain activation (Braun et al., 2006; Levy
et al., 2009; Taylor et al., 2013; Graves et al., 2014; Hofmann and Jacobs, 2014).
Reading Network
There is neuroimaging evidence for an indirect (sublexical) and a direct (lexico-semantic) route for
lexical access in line with the dual-route account of the DRC. The former is thought to correspond
to a dorsal occipito-parietal to inferior-frontal circuit (Jobard et al., 2003; Price, 2012). The dorsal
circuit is believed to be involved in the conversion of orthography to phonology and to comprise
superior temporal and inferior parietal regions with the supramarginal gyrus (SMG) and the
opercular part of the IFG. The direct route comprises a ventral occipito-temporal circuit involving
the ventral occipito-temporal cortex (vOT), the inferior temporal (ITG; Kronbichler et al., 2004) and
posterior middle temporal gyrus (pMTG; Noonan et al., 2013), as well as the triangular part of the
IFG and is assumed to be involved in lexico-semantic processing. Furthermore, parts of the angular
gyrus (AG) and the SMG seem to be involved in the processing of semantic information (e.g.,
Binder et al., 2003; Binder et al., 2005; Binder and Desai, 2011; Binder et al., 2009). But these
results do not directly speak to the question about the fate of phonology after stable sound-to-
letter correspondences have been established as in experienced readers. Below we report brain
regions involved in phonological and lexico-semantic processing. We expect activity in these
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regions in case pseudohomophones’ phonology activate their basewords and in turn allow for
lexico-semantic access.
Phonological Processing
The specific regions believed to signal phonological processing comprise the left IFG (Fiebach et
al., 2002; Ischebeck et al., 2004; Heim et al., 2009; Heim et al., 2013), bilateral insula (Mechelli et
al., 2007; Borowsky et al., 2006), the STG (Booth et al., 2002; Mesulam, 1990; Rumsey et al.,
1997), the SMG and AG (Joubert et al., 2004; Binder et al., 2003; Paulesu et al., 1993; Kronbichler
et al., 2007; Ischebeck et al., 2004; Church et al., 2008), and the supplementary motor area
(SMA; Carreiras et al., 2006). Activation of the left IFG and the insula has been proposed to serve
grapheme-phoneme conversion (pars opercularis) and/or processing for lexico-semantic access
(i.e., pars triangularis and orbitalis; Fiebach et al., 2002; Poldrack et al., 1999). Such functional
roles are also assigned to parts of the inferior parietal lobule (IPL; e.g., Booth et al., 2002). In
their meta-analysis Jobard et al. (2003) suggested that phonological processing involves left
lateralized brain structures such as superior temporal areas, the SMG and the opercular part of
the left IFG. Jobard et al. (2003) identified three clusters of which two were located in the
anterior and posterior part of the STG and the third in the MTG. Furthermore, Bitan et al. (2005)
reported activation in left MTG for rhyming, but not for spelling judgments (see also Indefrey
and Levelt, 2004; Graves et al., 2010; Graves et al., 2014; Simos et al., 2009; Newman and
Joanisse, 2011 for further evidence of phonological processing in MTG).
Semantics
The meta-analysis of 120 functional neuroimaging studies of Binder et al. (2009) reported
seven main regions for semantic processing including the posterior IPL (AG and SMG), the
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lateral temporal cortex (MTG and ITG), and the left IFG. Furthermore, activation in pars
orbitalis of the left ventral IFG was linked to semantic processing. Devlin et al., (2003) applied
transcranial magnetic stimulation (TMS) to the anterior IFG during semantic and perceptual
decision tasks. TMS slowed participants’ reaction time on the semantic but not on the control
task, supporting a causal role for the anterior IFG in semantic processing (see also Poldrack et
al., 1999; Wagner et al., 2001). A large body of evidence links MTG activation to semantic
processing (e.g., Gold et al., 2005; Noonan et al., 2013; Newman and Joanisse, 2011; Indefrey
and Levelt, 2004; Booth et al., 2007; Price et al., 1997; Vigneau et al., 2006; Richlan et al., 2009;
Whitney et al., 2011). Thus, research mainly suggests a lexico-semantic reading pathway that
integrates the left vOT with the left ventral IFG, and a probably non-semantic, phonological
decoding pathway linking the superior temporal and inferior parietal cortices to the dorsal
precentral gyrus. Until now it remains unclear how these pathways overlap or dissociate in the
reading system (Price, 2012). This is especially true for the MTG where evidence suggests that
activation either signals phonological processing involving the dorsal path (Graves et al., 2010;
Indefrey and Levelt, 2004; Graves et al., 2014; Simos et al., 2009; Newman and Joanisse, 2011)
or semantic processing involving the ventral path (Gold et al., 2005; Noonan et al., 2013;
Newman and Joanisse, 2011; Indefrey and Levelt, 2004; Booth et al., 2007; Price et al., 1997;
Vigneau et al., 2006; Richlan et al., 2009; Whitney et al., 2011).
Present Study
The aim of the present study was to investigate the pseudohomophone and the baseword
frequency effect in lexical decision by means of fMRI. The demonstration of pseudohomophone
and baseword frequency effects in lexical decision, a task which does not necessarily require
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phonological processing (Grainger and Jacobs, 1996; Seidenberg, 1985) would provide evidence
for an automatic activation of phonological representations which constrains access to meaning
(Van Orden, 1987; Edwards et al., 2005). We assume that lexical decisions to pseudohomophones
involve the activation of the baseword’s phonological code which signals the presence of a word
while the orthographic code signals its absence. This should result in greater activation in left
hemispheric regions involved in the computation of these codes such as the insula, pars
opercularis of the IFG, the SMA, the STG or SMG in case of phonological processing, and vOT and
parts of the IFG in case of orthographic (Cohen et al., 2000), and pars orbitalis and triangularis of
the IFG and inferior parietal and middle temporal areas for semantic processing (Gold et al., 2005;
Noonan et al., 2013).
Paralleling the logic used in behavioral studies, brain activation should be modulated by the
pseudohomophone – pseudoword contrast. In addition, if phonological processing constrains
lexico-semantic access at the whole-word level, neural activation in response to
pseudohomophones should be influenced by their baseword frequency. In particular, a baseword
frequency effect would support models of visual word recognition implementing whole-word
phonological as well as frequency sensitive representations. We thus expect that this kind of
processing activates areas believed to be involved in lexico-semantic processing like the AG and
MTG, or the pars triangularis and orbitalis of the left IFG (Bitan et al., 2005; Gold et al., 2005; Lau
et al., 2008; Binder et al., 2009).
While the basis of the baseword frequency effect in lexical decision is still under debate, the most
parsimonious explanation seems the assumption of a spelling-check which is probably more
effortful for low frequency basewords (Ziegler et al., 2001b) and might be due to lower resting
levels of the orthographic codes (Grainger and Jacobs, 1996). A harder spelling-check for low
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frequency items could lead to greater activation in regions concerned with orthographic
processing like the IFG or the vOT (Purcell et al., 2011). We carefully controlled our
pseudohomophones and pseudowords for a number of lexical and sublexical measures known to
influence visual word processing (see Table 1). Pseudohomophones and pseudowords did not
differ in orthographic similarity from each other nor to their identical basewords. Thus, if
pseudohomophones and pseudowords differed in response times and BOLD responses the effect
could not be due to an orthographic similarity confound. Furthermore, if participants in lexical
decision made only use of the direct ortographic-semantic route both, pseudohomophones and
pseudowords should activate the meaning of their basewords to the same extent, leading to
similar brain activation in the above mentioned 'lexico-semantic' networks.
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Experimental Procedures
All procedures were cleared by the local ethical review board.
Participants
Fourteen right-handed students (three men, mean age 23 years, range 20 to 30) participated in
the study. All participants were native German speakers, had normal or corrected to normal
vision, were free of any current or past neuropsychiatric disorders and did not take psychoactive
medication. Data from all participants were used in the analyses. Participants were compensated
for their time with 24 Euro.
Stimuli
The stimulus set contained 480 stimuli (240 words and 240 nonwords). Of the 240 word stimuli
120 served as fillers. Of the 240 non-words half were pseudohomophones and half were
pseudowords. Pseudohomophones and pseudowords were derived from the same base words by
replacement of one letter. Where possible, the letter was changed at the same position and a
vowel was replaced by another vowel and a consonant by another consonant.
Pseudohomophones had the same phonology, but differed in spelling from their basewords.
Pseudowords differed in spelling and also in phonology from their base words. All pseudowords
were pronounceable, for example, ’SAHL’ is a pseudohomophone derived from the baseword
’SAAL’ (hall) and ’SARL’ is the corresponding pseudoword. Of the pseudohomophones and the
pseudowords one third had three, one third had four and one third had five letters. Half of the
pseudohomophones and pseudowords of each length were derived from high frequency
basewords (more than 20 occurrences per million, mean 854.3). The other half of the
pseudohomophones and pseudowords had low frequency basewords (less than 20 occurrences
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per million, mean 6.8). Frequency estimates were taken from the CELEX database (Baayen et al.,
1993). To rule out orthographic similarity as a potential source of the pseudohomophone effect,
pseudohomophones and pseudowords were matched according to the strong criteria put forward
by Martin (1982). Pseudohomophones and pseudowords were controlled for number of letters
(let, F(3,236) = 0, p = 1, bigram frequency (BF, F(3,236) = 1.705, p = .167), number of bigram
neighbors (BN, F(2,236) = 0.198, p = .898), number of orthographic neighbors (N, F(3,236) = .086, p
= .968), number of higher frequency numbers (HFN, F(3,236) = 2.121, p = .098) and summed
frequency of orthographic neighbors (FN, F(3,236) = 0.666, p = .574). Thus, none of these variables
differed for items of low and high frequency within each nonword condition and also not between
conditions which should rule out sublexical and global lexical activity as a potential source of
differences between pseudohomophones and pseudowords. Table 1 shows the controlled
variables for pseudohomophones and pseudowords and the statistics for words. Of the 120 word
stimuli, one third had three, one third had four, and one third had five letters. Half of the word
stimuli of each word length were of high frequency (more than 11 occurrences per million, mean
421.2) and the other half were of low frequency (less than 11 occurrences per million, mean 3.3).
Words of high and low frequency were matched on bigram count (BC, F(1,118) = 0.985, p = .323),
number of letters (let, F(1,118) = 0, p = 1), summed frequency of the neighbors (FN, F(1,118) =
0.714, p = .400) and summed frequency of higher frequency neighbors (FHFN, F(1,118) = 0.649, p =
.422).
<< insert Table 1 and 2 about here >>
Procedure
Before participants were placed in the scanner they were given written instructions and were
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further orally informed about the experimental procedure. They were informed that they would
see letter strings, some of which were German words and some were nonwords. Participants
were instructed to indicate via button press as rapidly as possible, but not to the expense of
accuracy, whether the stimulus was a German word or not. Two magnet-compatible response
boxes (one in each hand) were used. The response hands were counterbalanced across
participants.
Participants performed 15 practice trials to familiarize themselves with the task. The experiment
was divided into three runs. The experimental trials were presented in randomized order for
each participant. Each stimulus was presented for 1000 ms with an inter-stimulus interval of
2000 ms during which a fixation cross was presented – resulting in a total trial duration of 3000
ms. Responses with reaction times below 200 ms and above 2000 ms were excluded (0.39%).
The stimuli were displayed in yellow on a grey background using upper case letters set in Courier
48pt font. Visual images were back-projected onto a screen by an LED-projector and participants
viewed the images through a mirror on the head coil. The whole experiment took about 40
minutes. The experiment was implemented using Presentation experimental software
(Neurobehavioral Systems Inc, Albany, CA).
Image Acquisition
Functional and structural imaging was performed with a General Electric 1.5 Tesla Signa scanner
(General Electric, Fairfield, CT, USA) using a standard head-coil. Conventional high-resolution
structural images (rf-spoiled GRASS sequence, 60 slice sagittal, 2.8 mm thickness) were followed
by three runs with 308 volumes each of functional images sensitive to BOLD contrast acquired
with a T2* weighted gradient echo EPI sequence (TR = 2000ms, TE = 29 ms, flip angle = 80°,
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number of slices = 34, slice thickness = 3.5 mm, 64 × 64 matrix, FOV 224 mm). Low frequency
noise was removed with a high-pass filter (128s).
fMRI Analysis
Data were analyzed using the general linear model in SPM8 (Statistical Parametric Mapping;
Wellcome Department of Cognitive Neurology, London, UK: http://www.fil.ion.ucl.ac.uk/spm).
The first three scans of each run were discarded to avoid magnetic saturation effects. Firstly, data
were pre-processed for each subject: after separate realignment procedures for the three runs, a
co-registration step was performed to adjust the images of each subjects to the first invidiual run.
Parameters for spatial normalization were determined by normalizing the mean realigned image
to a standard EPI template. Subsequently, functional images were resampled to isotropic 3 x 3 x 3
mm voxels and spatially smoothed with an 8 mm full-width at half-maximum isotropic Gaussian
kernel. All experimental conditions were modeled by a canonical hemodynamic response
function and its temporal time derivatives. Errors to pseudohomophones and pseudowords were
low (3%; corresponding to an average of 7 items per subject) and were therefore not modeled
separately. Statistical analysis was performed in a two-stage mixed effects model. On the subject-
specific 1st level model conditions of interest were contrasted against the fixation baseline. These
subject-specific contrast images were used for the 2nd level group analysis. Direct contrasts
between pseudohomophones and pseudowords with high and low baseword frequency were
calculated with 2 x 2 repeated measures ANOVA and paired t-tests. Stereotaxic coordinates for
voxels with maximal z-values within activation clusters are reported in the MNI coordinate
system.
Results
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Behavioral Results
Response times to words were about 150 ms faster than to nonwords. Percentage of errors to
words and nonwords were low and nearly the same (2.89% vs. 3.05%) and therefore not further
analyzed. Items with response times above 2000ms were excluded from the analysis. Words were
responded to fastest followed by responses to pseudowords and pseudohomophones. Response
times were slower to low than to high frequency items in each category. Table 3 shows the mean
response times and error rates for the different stimuli categories. For response times, separate
2x2 ANOVAs with homophony (pseudohomophones vs. pseudowords) and frequency (high vs.
low) as factors were performed for subjects (F1) and items (F2). In the repeated measures
ANOVA by subjects homophony and frequency were treated as within-subject factors. The
analyses revealed effects of homophony (F1(1,13) = 18.85, p < .001; F2(1,236) = 11.55, p < .001)
and frequency (F1(1,13) = 7.89, p = .015; F2(1,236) = 4.69, p = .031) and also a significant
interaction (F1(1,13) = 8.40, p = .012; F2(1,236) = 3.89, p = .049). Response times were slower in
response to pseudohomophones derived from low compared to high frequency basewords (t = -
3.610, df = 13, p = .003). In contrast, response times to pseudowords did not differ with regard to
frequency (t = -0.559, df = 13, p = .586). Additional contrasts for pseudohomophones and
pseudowords separately calculated for items with high and low frequency basewords showed a
significant difference for low (t = 5.187, df = 13, p < .001) and a marginally significant difference
for high frequency items (t = 1.873, df =13, p = .083). Thus, the behavioral analysis revealed a
pseudohomophone effect and a baseword frequency effect for pseudohomophones but not for
pseudowords. Both effects seem to rely stronger on the processing of pseudohomophones with
low frequency basewords.
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<< insert Table 3 about here >>
Imaging Results
First, images for the contrasts of interests were calculated separately for each subject. In the next
step pseudohomophones and pseudowords were separately contrasted with the fixation
baseline. Pseudohomophones elicited greater activation compared to fixation in posterior
occipital, occipito-temporal, parietal, and temporo-parietal regions. There were also extended
activations in left inferior frontal regions including the insula, pars opercularis and triangularis.
Furthermore, pre- and postcentral regions and posterior cingulate and right SPL/AG were more
activated by pseudohomophones compared to fixation baseline. Greater activation for
pseudowords compared to fixation were found in nearly the same regions as for
pseudohomophones. In contrast to pseudohomophones, pseudowords showed less extended
inferior frontal and inferior and superior parietal activation (see Figure 1). Since the aim of the
study was to investigate the neural basis of pseudohomophone and baseword frequency effects
the following analyses directly contrasted the activity in response to pseudohomophones vs.
pseudowords and items with high vs. low baseword frequencies.
<< insert Figure 1 about here>>
Pseudohomophone Effect
The whole-brain analysis revealed that no region showed greater activation for pseudowords than
for pseudohomophones. In contrast, activation to pseudohomophones was greater than for
pseudowords in the left hemisphere comprising the insula and the inferior orbito-frontal cortex
including pars orbitalis and pars triangularis, pre-motor, middle/superior frontal gyrus,
inferior/superior parietal regions and in inferior/middle temporal gyri on the right (see Table 4).
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Activation in the insula und pars orbitalis showed the strongest activation in response to low
frequency pseudohomophones followed by high frequency pseudohomophones. Pseudowords
produced only little activation or deactivation. In the superior frontal gyrus all stimulus conditions
showed greater activation for low frequency items with low frequency pseudohomophones
eliciting the strongest activation. The separately performed 2x2 ANOVAs with the beta estimates
of the peak values with homophony (pseudohomophones, pseudowords) and baseword frequency
(high, low) as within-subject factors for regions showing greater activation for pseudohomophones
than for pseudowords revealed main effects of homophony (insula: F(1,13) = 17.85, p < .001; pars
orbitalis: F(1,13) = 11.70, p = .005; superior frontal gyrus (SFG): F(1,13) = 17.40, p = .001; MTG:
F(1,13) = 16.27, p = .001; SPL: F(1,13) = 6.96, p = .021), no main effect of frequency, and no
interactions. Thus, regions showing stronger activation in response to pseudohomophones did not
show significant effects of baseword frequency (see Figure 2 and Table 4).
<< insert Figure 2 and Table 4 about here >>
Baseword Frequency Effect
To see if there were brain regions showing differential activation for items of high and low
baseword frequency, we performed a whole brain analysis for the contrasts of high vs. low
frequency pseudohomophones and high vs. low frequency pseudowords. There were no regions
showing an effect of baseword frequency for pseudowords. In contrast, the results showed an
effect of baseword frequency for pseudohomophones. Pseudohomophones with high frequency
basewords showed the highest and pseudohomophones with low frequency basewords showed
always the lowest activation. The four clusters showing a difference in activation for
pseudohomophones with high compared to pseudohomophones with low frequency
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basewords comprised the left inferior and superior lateral occipital cortex including the AG, parts
of the left MTG and STG and the posterior SMG. Furthermore, the right AG and SMG showed
greater activation for high frequency pseudohomophones compared to pseudohomophones with
low frequency basewords. Also the left and right MTG and STG showed greater activation for
pseudohomophones with high compared to those with low frequency basewords (see Figure 3).
To further show that the baseword frequency effect was only obtained for pseudohomophones and not
for pseudowords we additionally calculated the whole brain analyses for the contrasts of high vs.
low frequency pseudohomophones by exclusively masking (p < .05, uncorrected) it with the
contrast of high vs. low frequency pseudowords. The results revealed a reliable baseword frequency
effect for pseudohomophones but not for pseudowords (see Table 5).
<<insert Figure 3 and Table 5 about here >>
The separately performed 2x2 ANOVAs with the beta estimates of the peak values with
homophony (pseudohomophones, pseudowords) and baseword frequency (high, low) as within-
subject factors for regions showing greater activation for pseudohomophones with high
compared to pseudohomophones with low frequency basewords revealed main effects of
frequency (left AG: F(1,13) = 19.63, p < .001; left MTG: F(1,13) = 11.01, p < .001; right AG: F(1,13)
= 6.45, p = .025; right MTG: F(1,13) = 14.67, p < .001) and interactions of frequency and
homophony (left AG: F(1,13) = 9.19, p = .001; left MTG: F(1,13) = 7.54, p = .017; right AG: F(1,13)
= 8.41, p = .012; right MTG: F(1,13) = 11.30, p < .001) and no main effect of homophony. To
further investigate the interaction paired t-tests for the comparisons of high and low frequency
pseudohomomphones and high and low frequency pseudowords were calculated. The results
showed that the effect of baseword frequency was significant for pseudohomophones (left AG: t
19
= 6.921, df = 13, p < .001; rAG: t = 4.402, df = 13, p < .001; left MTG: t = 3.613, df = 13, p = .003;
right MTG: t = 4.799, df = 13, p < .001) but not for pseudowords (left AG: t = 1.003, df = 13, p =
.334; right AG: t = -0.402, df = 13, p = .694; right MTG: t = 1.586, df = 13, p = .137; left MTG: t =
1.437, df = 13, p = .174).
Discussion
In the introduction, we outlined the empirical criteria for evidence of phonology triggered
lexico-semantic access in silent reading i.e. the pseudohomophone effect (differences in
response times and brain activation for pseudohomophones and pseudowords derived from
the same basewords), and the baseword frequency effect (differences in response times and
brain activation for pseudohomophones derived from basewords of different lexical
frequency). Our behavioral and neuroimaging results unequivocally demonstrate both effects
and thus provide evidence for phonological mediation in visual word recognition (e.g.,
Coltheart et al., 2001; Frost, 1998; Van Orden, 1987). Responses to pseudohomophones were
42 ms slower than to pseudowords. Furthermore, pseudohomophones showed a baseword
frequency effect with slower responses (50 ms) to pseudohomophones with low than with high
frequency basewords. In contrast, there was no baseword frequency effect for pseudowords.
Our study thus is the first to demonstrate both, a pseudohomophone and a baseword
frequency effect in the scanner.
Imaging: Pseudohomophone Effects
In the hemodynamic responses the pseudohomophone effect was evident in greater activation
for pseudohomophones compared to pseudowords in left hemispheric regions as the insula, pars
triangularis and orbitalis, SMA and superior frontal gyrus (SFG). Furthermore, a right
20
inferior/middle temporal cluster showed greater activation for pseudohomophones. Thus,
pseudohomophones showed activation in inferior and superior frontal regions involved in
phonological processing (e.g., Bokde et al., 2001; Jobard et al., 2003; Mechelli et al., 2007;
Cattinelli et al., 2013; Binder et al., 2009; Newman and Joanisse, 2011; Pillay et al., 2014) but also
in semantic processing (e.g., Fiebach et al., 2002; Ischebeck et al., 2004).
Imaging: Baseword Frequency Effects
Whereas pseudowords did not show baseword frequency effects for the imaging data, the
comparison of pseudohomophones with high and low frequency basewords revealed activation
differences in bilateral IPL including the AG, posterior SMG and in bilateral STG and MTG with
greater activation for pseudohomophones with low frequency basewords. Previous research
suggests that activation in MTG is involved in semantic processing (e.g., Gold et al., 2005; Noonan
et al., 2013; Newman and Joanisse, 2011; Indefrey and Levelt, 2004; Booth et al., 2007; Price et al.,
1997; Vigneau et al., 2006; Richlan et al., 2009; Whitney et al., 2011) but there is also evidence
suggesting a role in phonological processing (e.g., Indefrey and Levelt, 2004; Bitan et al., 2005;
Simos et al., 2009; Newman and Joanisse, 2011; Graves et al., 2014), or orthographic-phonological
mapping (e.g., Graves et al., 2010). Many studies report that left MTG is consistently more active
during the processing of words than during the processing of pseudowords (e.g., Binder et al.,
2003; Fiebach et al., 2002).
Interpretation of Effects
In the light of these findings we suggest that the greater activation for pseudohomophones in
comparison to pseudowords reflects stronger phonological and semantic activation of the
basewords. Since pseudohomophones and pseudowords are visually equally similar to their
21
basewords orthography cannot explain the obtained activation differences. Thus, information
signaling the presence of a word is low in case of a pseudoword (some orthographic similarity) and
high in case of a pseudohomophone (same phonology and semantics and some orthographic
similarity) and must rely on phonological features. Greater activation for identifying
pseudohomophones reflects greater access to and mapping with stored representations.
Activation in pars orbitalis and triangularis probably signals processing for meaning, and activation
in SMA (Purcell et al., 2011) and the insula (Borowsky et al., 2006) could signal processes of
orthographic-phonological mapping. Activation differences for pseudohomophones with high
compared to those with low frequency in the IPL, STG and MTG could signal faster and easier
access to stored lexico-semantic representations for pseudohomophones with high frequency
basewords, probably due to higher resting levels. In contrast, pseudowords did not activate their
high and low frequency basewords differentially, probably because they fail to activate whole-
word phonology and semantic representations.
Spelling-Check
The aim of our study was to investigate the neural bases of pseudohomophone and baseword
frequency effects in lexical decision. Current models of visual word recognition either do not
predict both effects or predict the wrong direction (longer RTs in response to pseudohomophones
with high compared to low frequency basewords). To account for this finding it was suggested that
making lexical decisions to pseudohomophones involves orthographic processing in the form of a
spelling-check (Paap et al., 1982; Ziegler et al., 2001). Such a spelling-check would, when
successfully performed, allow for a correct nonword decision for pseudohomophones and
pseudowords. Based on the current results we suggest that the pseudohomophone and the
baseword frequency effect originate from different mechanisms and to rely on different brain
22
regions. The pseudohomophone effect is probably driven by a spelling-check involving inferior and
superior frontal and superior parietal brain regions. Activation in the SPL is shown to be associated
with mental imagery (Ganis et al., 2004), visual attention (Wojciulik and Kanwisher, 1999; Simon et
al., 2004; Gottlieb, 2007), and the spelling of words (Bitan et al., 2005, 2007; Purcell et al., 2011).
Such a spelling-check probably operates in a serial fashion (e.g., Cohen et al., 2008; Gaillard et al.,
2006). In contrast, the baseword frequency effect is signaled by activation differences in inferior
parietal and middle temporal regions, probably driven by the need for greater allocation of
attention and the suppression of ongoing processing (Binder et al., 2003) necessary to process the
phonological (Indefrey and Levelt, 2004; Bitan et al., 2005; Simos et al., 2009; Newman and
Joanisse, 2011; Graves et al., 2014) and semantic (Gold et al., 2005; Noonan et al., 2013; Newman
and Joanisse, 2011; Indefrey and Levelt, 2004; Booth et al., 2007; Price et al., 1997; Vigneau et al.,
2006; Richlan et al., 2009; Whitney et al., 2011; Mechelli et al., 2007) properties of
pseudohomophones with high and low frequency basewords. This interpretation is in line with
results showing that greater attention to task demands and higher short-term memory load is
associated with reduced BOLD signals in right AG (McKiernan et al., 2003) and right temporal
parietal junction (Todd et al., 2005).
Model - Pathways
As outlined in the introduction, current models of visual word recognition cannot fully account
for the pseudohomophone and baseword frequency effects. Models with implemented
interactive activation processes (e.g., DRC and MROM-P), assume that higher activation in a
phonological lexicon leads to longer processing times for pseudohomophones with high
compared to those with low frequency basewords. This is because high frequency
pseudohomophones are believed to elicit stronger 'word present' signals and thus should show
23
greater activation in brain regions known to process phonological and/or lexcico-semantic
information. However, the behavioral and imaging results of the present study are against this
interpretation. Pseudohomophones with high compared to those with low frequency basewords
were rejected faster and showed less deactivation in bilateral MTG, AG and SMG.
Since pseudohomophones and pseudowords did not differ in orthographic similarity to each
other and also not to their same basewords we would have expected a baseword frequency
effect also for pseudowords if orthographic processing was the basis of the baseword frequency
effect (Newman and Joanisse, 2011). The fact that there was a baseword frequency effect only
for pseudohomophones argues against the activation of meaning via a direct orthographic route
as proposed by the triangle model.
Furthermore, the fact that pseudohomophones with high and low baseword frequencies activate
the dorsal visual pathway with AG, SMG, and STG (Sandak et al., 2004; McCandliss et al., 2003) but
also the MTG of the ventral visual pathway (Schurz et al., 2010; Jobard et al., 2003; Sandak et al.,
2004) is evidence against the proposal that pseudohomophones, like other nonwords are
exclusively processed by an indirect route and by converting sublexical phonology to sublexical
orthography as proposed by dual-route models of visual word recognition.
The current results thus speak for a close interaction of dorsal and ventral pathways in visual word
recognition in the case of pseudohomophones and therefore for models assuming parallel and
serial processing pathways as the DRC and the CDP+. For example, the CDP+ implements a parallel
lexical pathway and a serial grapheme–phoneme conversion pathway. When reading nonwords
the sublexical pathway converts a string of letters into graphemes by serial left-to-right processing,
which is probably associated with activation in dorsal-parietal regions and also used by a potential
24
spelling-check. But this serial mechanism does probably not account for the obtained baseword
frequency effect for pseudohomophones.
Recent research has shown that there are probably multiple routes used in skilled reading
depending on the task and the stimuli (Jobard et al., 2011; Richardson et al., 2011; Seghier et al.,
2012; Cummine et al., 2013; Graves et al., 2014). Richardson et al. (2011) identified three potential
processing streams from occipital to temporal cortical areas: a ventral lexico-semantic route, a
dorsal phonological route, and an intermediate route encompassing partially both ventral and
dorsal routes. The results of the DCM analysis were interpreted to show that connections between
the posterior inferior occipital gyrus (pIO) and the posterior superior temporal sulcus (pSTS) were
involved in linking orthography to phonology. Connections between anterior and posterior STS
were suggested to link phonology to semantics, and the ventral path from pIO over vOT to
anterior STS is suggested to link orthography to semantics. Activation in pSTS was interpreted to
signal phonological processing which could be influenced by early inferior occipital and late visual
processing (vOT). This was hypothesized to be consistent with the idea that orthography and
phonology are linked over different levels of processing, i.e. different grain sizes, as proposed by
Ziegler et al. (2001a) and Ziegler and Goswami (2005, 2006). Furthermore, information between
these areas are probably transmitted by the inferior longitudinal fasciculus which extends to the
ventral and dorsal path and hypothesized that the posterior MTG is probably involved as an
intermediate region which may relay information transferred from pIO to pSTS.
Extending the hypothesis of Richardson et al. (2011) we propose that the MTG may not only relay
information from early visual to phonological areas but also to be involved in the integration of
orthographic, phonological and semantic information. We suggest that the MTG could be a region
25
where multiple sources of information converge and are integrated into a coherent percept of the
input in support of access to stored lexico-semantic representations.
Conclusion
In sum, the present study used pseudohomophones and pseudowords in lexical decision to
investigate phonological processing in visual word recognition. Both, behavioral and neuroimaging
results revealed a pseudohomophone effect as well as a baseword frequency effect for
pseudohomophones but not for pseudowords. We interpret these results as evidence that reading
pseudohomophones in contrast to pseudowords activate whole-word representations of their
phonologically identical basewords triggering access at the lexico-semantic level. This conclusion is
supported by the obtained baseword frequency effect showing activation differences for
pseudohomophones with high and low frequency basewords. Both effects therefore support
models of visual word recognition, which implement frequency sensitive representations and
assume phonologically mediated access to semantic representations in visual word recognition.
We suggest that reading pseudohomophones comprises the same stages of processing as with
words: a prelexical analysis of the visual word form and early lexical processing in ventral occipito-
temporal areas; access to orthographic, phonological, and semantic features in MTG, STG, AG and
SMG; and a manipulation of these features in the IFG involving processes of orthographic-
phonological mapping, lexico-semantic search, retrieval and selection. Processing specific to
pseudohomophones and pseudowords presumably involves a spelling-check which is harder for
pseudohomophones and likely to be associated with activation in the SFG and the SPL.
27
References
Alexander JD, Nygaard LC (2008), Reading voices and hearing text: talker-specific auditory imagery in
reading. J Exp Psychol Human 34:446–446.
Baayen RH, Piepenbrock R, van Rijn H (1993), The CELEX lexical database.
Berent I, Perfetti CA (1995), A rose is a REEZ: The two-cycles model of phonology assembly in reading
English. Psychol Rev 102:146.
Binder JR, Desai RH, Graves WW, Conant LL (2009), Where is the semantic system? A critical review
and meta-analysis of 120 functional neuroimaging studies. Cereb Cortex 19:2767–96.
Binder JR, McKiernan KA, Parsons ME, Westbury CF, Possing ET, Kaufman JN, Buchanan L (2003),
Neural Correlates of Lexical Access during Visual Word Recognition. J Cognitive Neurosci
15:372–393.
Binder JR, Westbury CF, McKiernan KA, Possing ET, Medler DA (2005), Distinct brain systems for
processing concrete and abstract concepts. J Cogn Neurosci 17:905–917.
Bitan T, Booth JR, Choy J, Burman DD, Gitelman DR, Mesulam M-M (2005), Shifts of effective
connectivity within a language network during rhyming and spelling. J Neurosci 25:5397–5403.
Bitan T, Cheon J, Lu D, Burman DD, Gitelman DR, Mesulam M-M, Booth JR (2007), Developmental
changes in activation and effective connectivity in phonological processing. Neuroimage
38:564–575.
Bokde AL, Tagamets MA, Friedman RB, Horwitz B (2001), Functional interactions of the inferior
frontal cortex during the processing of words and word-like stimuli. Neuron 30:00:00 609–17.
Booth JR, Bebko G, Burman DD, Bitan T (2007), Children with reading disorder show modality
independent brain abnormalities during semantic tasks. Neuropsychologia 45:775–83.
Booth JR, Burman DD, Meyer JR, Gitelman DR, Parrish TB, Mesulam MM (2002), Functional Anatomy
28
of Intra- and Cross-Modal Lexical Tasks. Neuroimage 16:7–22.
Borowsky R, Cummine J, Owen WJ, Friesen CK, Shih F, Sarty GE (2006), FMRI of ventral and dorsal
processing streams in basic reading processes: insular sensitivity to phonology. Brain Topogr
18:233–239.
Braun M, Hutzler F, Ziegler JC, Dambacher M, Jacobs AM (2009), Pseudohomophone effects provide
evidence of early lexico-phonological processing in visual word recognition. Hum Brain Mapp
30:1977–89.
Braun M, Jacobs AM, Hahne A, Ricker B, Hofmann M, Hutzler F (2006), Model-generated lexical
activity predicts graded ERP amplitudes in lexical decision. Brain Res 1073-1074:431–439.
Briesemeister BB, Hofmann, MJ, Tamm, S, Kuchinke, L, Braun, M, Jacobs, AM (2009), The
pseudohomophone effect: evidence for an orthography-phonology-conflict. Neurosci Lett
455:124–8.
Carreiras M, Mechelli A, Price CJ (2006), Effect of word and syllable frequency on activation during
lexical decision and reading aloud. Hum Brain Mapp 27:963–972.
Cattinelli I, Borghese, NA, Gallucci, M, Paulesu, E (2013), Reading the reading brain: a new meta-
analysis of functional imaging data on reading. J Neurolinguist 26:214–238.
Church JA, Coalson RS, Lugar HM, Petersen SE, Schlaggar BL (2008), A Developmental fMRI Study of
Reading and Repetition Reveals Changes in Phonological and Visual Mechanisms Over Age.
Cereb Cortex 18:2054–2065.
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.
Cohen L, Martinaud O, Lemer C, Lehericy S, Samson Y, Obadia M (2003), Visual word recognition in
the left and right hemispheres: Anatomical and functional correlates of peripheral alexias.
Cereb Cortex 13:1313–1333.
29
Coltheart M, Rastle K, Perry C, Langdon R, Ziegler JC (2001), DRC: A dual route cascaded model of
visual word recognition and reading aloud. Psychol Rev 108:204–256.
Cummine J, Gould L, Zhou C, Hrybouski S, Siddiqi Z, Chouinard B, Borowsky R (2013), Manipulating
instructions strategically affects reliance on the ventral-lexical reading stream: converging
evidence from neuroimaging and reaction time. Brain Lang 125:203–214.
Devlin JT, Matthews PM, Rushworth MF (2003), Semantic processing in the left inferior prefrontal
cortex: a combined functional magnetic resonance imaging and transcranial magnetic
stimulation study. J Cognitive Neurosci 15:71–84.
Edwards JD, Pexman PM, Goodyear BG, Chambers CG (2005), An fMRI investigation of strategies for
word recognition. Cognitive Brain Res 24:648–662.
Fiebach C, Friederici A, Müller K, von Cramon D (2002), fMRI evidence for dual routes to the mental
lexicon in visual word recognition. J Cognitive Neurosci 14:11–23.
Forster KI, Chambers SM (1973), Lexical access and naming time. Journal of Verbal Learning and
Verbal Behavior, 12:627–635.
Frisson S, Koole H, Hughes L, Olson A, Wheeldon L (2014), Competition between orthographically
and phonologically similar words during sentence reading: Evidence from eye movements. J
Mem Lang 73:148–173.
Frost R (1998), Towards a strong phonological theory of visual word recognition: true issues and
false trails. Psychol Bull 123:71–99.
Gaillard R, Naccache L, Pinel P, Clémenceau S, Volle E, Hasboun D (2006), Direct intracranial, FMRI,
and lesion evidence for the causal role of left inferotemporal cortex in reading. Neuron
50:191–204.
Ganis G, Thompson, WL, Kosslyn, SM (2004), Brain areas underlying visual mental imagery and visual
perception: an fMRI study. Cognitive Brain Res 20:226–241.
30
Gold BT, Balota DA, Kirchhoff BA, Buckner, RL (2005), Common and dissociable activation patterns
associated with controlled semantic and phonological processing: evidence from FMRI
adaptation. Cereb Cortex 15 1438–50
Goswami U, Ziegler JC, Dalton L, Schneider W (2001), Pseudohomophone effects and phonological
recoding procedures in reading development in English and German. J Mem Lang 45:648-664.
Gottlieb J (2007), From thought to action: the parietal cortex as a bridge between perception, action,
and cognition. Neuron 53:9–16.
Grainger J, Jacobs AM (1996), Orthographic processing in visual word recognition: A multiple read-
out model. Psychol Rev 103:518–565.
Graves WW, Binder JR, Desai RH, Humphries C, Stengel BC, Seidenberg MS (2014), Anatomy is
strategy: Skilled reading differences associated with structural connectivity differences in the
reading network. Brain Lang 133C: 1–13.
Graves WW, Desai R, Humphries C, Seidenberg MS, Binder JR (2010), Neural Systems for Reading
Aloud: A Multiparametric Approach. Cereb Cortex 20:1799–1815.
Hawelka S, Schuster S, Gagl B, Hutzler F (2013), Beyond single syllables: The effect of first syllable
frequency and orthographic similarity on eye movements during silent reading. Lang Cognitive
Proc 28:1134–1153.
Heim S, Eickhoff SB, Ischebeck AK, Friederici AD, Stephan KE, Amunts K (2009), Effective connectivity
of the left BA 44, BA 45, and inferior temporal gyrus during lexical and phonological decisions
identified with DCM. Hum Brain Mapp 30:392-402.
Heim S, Wehnelt A, Grande M, Huber W, Amunts K (2013), Effects of lexicality and word frequency
on brain activation in dyslexic readers. Brain Lang 125:194–202.
Hofmann MJ, Jacobs AM (2014), Interactive activation and competition models and semantic
context: From behavioral to brain data. Neurosci Biobehav R 46: 85-104.
31
Indefrey P, Levelt WJ (2004), The spatial and temporal signatures of word production components
Cognition 92:101-144.
Ischebeck A, Indefrey P, Usui N, Nose I, Hellwig F, Taira M (2004), Reading in a regular orthography:
an FMRI study investigating the role of visual familiarityJ Cogn Neurosci 16 727-41.
Jacobs AM, Grainger J (1994), Models of visual word Recognition - Sampling the state of the Art. J
Exp Psychol Human 20:1311–1334.
Jacobs AM, Rey A, Ziegler JC, Grainger J (1998), MROM-P: An interactive activation, multiple read-
out model of orthographicand phonological processes in visual word recognition. In: Local
Connectionists Approaches to Human Cognition (Grainger J, Jacobs AM, eds), pp 147–187.
Mahwah, New Jersey: Lawrence Erlbaum Associates.
Jäncke L, Shah NJ (2004), “Hearing” syllables by “seeing” visual stimuli. Eur J Neurosci 19:2603-2608.
Jobard G, Crivello F, Tzourio-Mazoyer N (2003), Evaluation of the dual route theory of reading: a
metanalysis of 35 neuroimaging studies. Neuroimage 20:693–712.
Jobard G, Vigneau M, Simon G, Tzourio-Mazoyer N (2011), The weight of skill: interindividual
variability of reading related brain activation patterns in fluent readers. J Neurolinguist
24:113–132.
Joubert SA, Beauregard M, Walter N, Bourgouin P, Beaudoin G, Leroux J-M, Lecours AR (2004),
Neural correlates of lexical and sublexical processes in reading. Brain Lang 89:9–20.
Kronbichler M, Bergmann JRV, Hutzler F, Staffen W, Mair A, Ladurner G (2007), Taxi vsTaksi: On
Orthographic Word Recognition in the Left Ventral Occipitotemporal Cortex. J Cognitive
Neurosci 19:1584-1594.
Kronbichler M, Hutzler F, Wimmer H, Mair A, Staffen W, Ladurner G (2004), The visual word form
area and the frequency with which words are encountered: evidence from a parametric fMRI
study. NeuroImage 21:946–53.
32
Lau EF, Phillips C, Poeppel D (2008), A cortical network for semantics: (de)constructing the N400. Nat
Rev Neurosci 9:920-933.
Levy J, Pernet C, Treserras S, Boulanouar K, Aubry F, Démonet J-F, Celsis P (2009), Testing for the
dual-route cascade reading model in the brain: an fMRI effective connectivity account of an
efficient reading style. PLoS One 4:e6675-e6675.
McCandliss B, Cohen L, Dehaene S (2003), The visual word form area: expertise for reading in the
fusiform gyrus. Trends Cogn Sci 7:293–299.
McCann RS, Besner D, Davelaar E (1988), Word recognition and identification: Do word-frequency
effects reflect lexical access? J Exp Psychol Human 14:693-706.
McKiernan KA, Kaufman JN, Kucera-Thompson J, Binder JR (2003), A parametric manipulation of
factors affecting task-induced deactivation in functional neuroimaging. J Cogn Neurosci
15:394–408.
Mechelli A, Josephs O, Ralph L, Matthew A, McClelland JL, Price CJ (2007), Dissociating stimulus-
driven semantic and phonological effect during reading and naming. Hum Brain Mapp 28:205–
217.
Mesulam MM (1990), Large-scale neurocognitive networks and distributed processing for attention,
language, and memory. Ann Neurol 28:597–613.
Newman RL, Joanisse MF (2011), Modulation of brain regions involved in word recognition by
homophonous stimuli: an fMRI study. Brain Res 1367:250–64.
Noonan KA, Jefferies E, Visser M, Lambon Ralph MA (2013), Going beyond inferior prefrontal
involvement in semantic control: evidence for the additional contribution of dorsal angular
gyrus and posterior middle temporal cortex. J Cognitive Neurosci 25:1824–1850.
Paap K, Newsome SL, McDonald JE, Schvaneveldt RW (1982), An activation-verification model for
letter and word recognition: The wordsuperiority effect. Psychol Rev 89:573–589.
33
Pammer K, Hansen PC, Kringelbach ML, Holliday I, Barnes G, Hillebrand A, Cornelissen PL (2004),
Visual word recognition: the first half second. Neuroimage 22:1819–1825.
Perrone-Bertolotti M, Kujala J, Vidal JR, Hamame CM, Ossandon T, Bertrand O, Lachaux J-P (2012),
How silent is silent reading? Intracerebral evidence for top-down activation of temporal voice
areas during reading. J Neurosci 32:17554–62
Pillay SB, Stengel BC, Humphries C, Book DS, Binder JR (2014), Cerebral localization of impaired
phonological retrieval during rhyme judgment. Ann Neurol 76:738-746.
Poldrack RA, Wagner AD, Prull MW, Desmond JE, Glover GH, Gabrieli JD (1999), Functional
specialization for semantic and phonological processing in the left inferior prefrontal cortex.
Neuroimage 10:15–35.
Price CJ (2012), A review and synthesis of the first 20 years of PET and fMRI studies of heard speech,
spoken language and reading. Neuroimage 62:816–47
Price CJ, Moore C, Humphreys G, Wise R (1997), Segregating semantic from phonological processes
during reading. J Cognitiv Neurosci 9:727–733.
Purcell JJ, Turkeltaub PE, Eden GF, Rapp B (2011), Examining the central and peripheral processes of
written word production through meta-analysis. Front Psychol 2:1-16.
Reynolds M, Besner D (2005), Basic processes in reading: A critical review of pseudohomophone
effects in reading aloud and a new computational account. Psychon B Rev 12:622–646.
Richardson FM, Seghier ML, Leff AP, Thomas MSC, Price CJ (2011), Multiple routes from occipital to
temporal cortices during reading. J Neurosci 31:8239–47
Richlan F, Kronbichler M, Wimmer H (2009), Functional abnormalities in the dyslexic brain: a
quantitative meta-analysis of neuroimaging studies. Hum Brain Mapp30:3299–308.
Rubenstein H, Lewis SS, Rubenstein M (1971), Homographic entries in the internal lexicon: Effects of
systemacity and relative frequency of meaningsJournal of Verbal Learning and Verbal Behavior
34
10 57–62.
Rumsey J, Horwitz B, Donohue B, Nace K, Maisog J, Andreason P (1997), Phonological and
orthographic components of word recognitionA PET-rCBF studyBrain 120 739–759.
Sandak R, Mencl WE, Frost SJ, Pugh KR (2004), The neurobiological basis of skilled and impaired
reading: Recent findings and new directionsScientific Studies of Reading 8 273–292.
Schurz M, Sturm D, Richlan F, Kronbichler M, Ladurner G, Wimmer H (2010), A dual-route
perspective on brain activation in response to visual words: evidence for a length by lexicality
interaction in the visual word form area (VWFA). NeuroImage 49 2649–61.
Seghier ML, Neufeld NH, Zeidman P, Leff AP, Mechelli A, Nagendran A, Price, CJ (2012), Reading
without the left ventral occipito-temporal cortex. Neuropsychologia 50:3621–3635.
Seidenberg MS (1985), The time cours of phonological code activation in two writing systems.
Cognition 19:1–30.
Seidenberg MS, Petersen A, McDonald MC, Plaut DC (1996), Pseudohomophone effects and models
of word recognition. J Exp Psychol Learn 22:48–62.
Simon O, Kherif F, Flandin G, Poline J-B, Rivière D, Mangin J-F, Dehaene S (2004), Automatized
clustering and functional geometry of human parietofrontal networks for language, space, and
number. Neuroimage 23:1192–1202.
Simos PG, Pugh K, Mencl E, Frost S, Fletcher JM, Sarkari S, Papanicolaou AC (2009), Temporal course
of word recognition in skilled readers: a magnetoencephalography study. Behav Brain Res 197
45–54.
Taylor JSH, Rastle K, Davis MH (2013), Can cognitive models explain brain activation during word and
pseudoword reading? A meta-analysis of 36 neuroimaging studies. Psychol Bull 139:766–91.
Todd JJ, Fougnie D, Marois R (2005), Visual short-term memory load suppresses temporo-parietal
junction activity and induces inattentional blindness. Psychol Sci 16:965–972.
35
Van Orden GC (1987), A ROWS is a ROSE: Spelling, sound, and reading. Mem Cognition 15: 181–198.
Vigneau M, Beaucousin V, Hervé PY, Duffau H, Crivello F, Houdé O, Tzourio-Mazoyer N (2006), Meta-
analyzing left hemisphere language areas: phonology, semantics, and sentence processing.
NeuroImage 30:1414–32.
Wagner AD, Paré-Blagoev EJ, Clark J, Poldrack RA (2001), Recovering meaning: left prefrontal cortex
guides controlled semantic retrieval. Neuron 31:329–338.
Wheat KL, Cornelissen PL, Frost SJ, Hansen PC (2010), During visual word recognition, phonology is
accessed within 100 ms and may be mediated by a speech production code: evidence from
magnetoencephalography. J Neurosci 30:5229–5233.
Whitney C, Kirk M, O’Sullivan J, Lambon Ralph MA, Jefferies E (2011), The neural organization of
semantic control: TMS evidence for a distributed network in left inferior frontal and posterior
middle temporal gyrus. Cereb Cortex 21:1066-1075.
Wojciulik E, Kanwisher N (1999), The generality of parietal involvement in visual attention. Neuron
23:747–764.
Yao B, Belin P, Scheepers C (2011), Silent reading of direct versus indirect speech activates voice-
selective areas in the auditory cortex. J Cogn Neurosci 23:3146–52.
Ziegler JC, Bertrand D, Lété B, Grainger J (2013), Orthographic and Phonological Contributions to
Reading Development: Tracking Developmental Trajectories Using Masked Priming. Dev
Psychol 50:1026–1026.
Ziegler JC, Besson M, Jacobs AM, Nazir T, Carr TH (1997), Word pseudoword, and nonword
processing: A multitask comparison using eventrelated potentials. J Cognitive Neurosci 9:758–
775.
Ziegler JC, Goswami U (2006), Becoming literate in different languages: similar problems, different
solutions. Developmental Sci 9:429–436.
36
Ziegler JC, Jacobs AM (1995), Phonological information provides early sources of constraint in the
processing of letter stringsJournal of Mem Lang 34:567–593.
Ziegler JC, Jacobs AM, Klueppel D (2001b), Pseudohomophone Effects in Lexical Decision: Still a
Challenge for Current Word Recognition Models. J Exp Psychol Human 27:547–599.
Ziegler JC, Perry C, Jacobs AM, Braun M (2001a), Identical words are not processed the same in
different languages: The grainsize hypothesis. Psychol Sci 12:379–384.
Ziegler JC, Tan LH, Perry C, Montant M (2000), Phonology matters: The phonological frequency effect
in written Chinese. Psychol Sci 11:234–238.
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Tables
Table 1. Descriptive statistics for pseudohomophones, pseudowords and words
Frequency
low high mean
pseudohomophones
bwF 6.8(6.6) 854.3(3390) 430.5
BF 2623(5038) 3713(4419) 3157.5
BN 17 (11) 16 (9) 16.5
FN 3242(16083) 1231(3609) 2236.5
HFN 3.6(2.3) 3.4(2.5) 3.5
Letters 4(0.84) 4(0.82) 4
N 3.6(2.3) 3.4(2.5) 3.5
pseudowords
bwF 6.8(6.6) 854.3(3390) 430.5
BF 5150(15883) 8051(21930) 6600.5
BN 16(11) 15(10) 15.5
FN 3416(15727) 5324(22117) 4370
HFN 2.8(2.2) 2.8(1.9)
Letters 4(0.84) 4(0.82) 4
N 3.6(2.3) 3.4(2.5) 3.5
words
F 3.3(2.7) 421.2(295) 212.3
BC 42(49) 52(55) 47.5
BF 4025(5492) 13672(23806) 8848.5
BN 15(10) 20(10) 17.5
N 2.9(2.5) 4.3(2.8) 3.6
FHFN 2778(15869) 5653(22539) 4215.5
FN 2722(14595) 5496(20745) 4109
HFN 1.8(1.9) 0.5(0.7) 1.18
Letters 4(0.82) 4(0.82) 4
Syl 1.5(0.5) 1.3(0.5) 1.4 Note. BC = summed bigram count, BF = summed bigram frequency; BN = number of bigram neighbors, F = word
frequency/million, FHFN= summed frequency of higher frequency orthographic neighbors, FN = summed frequency of
orthographic neighbors; HFN = number of higher frequency orthographic neighbors; Letter= number of letters; N =
number of neighbors; Syl = number of syllables. Numbers in brackets represent standard deviations..
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Table 2. Sample stimuli
pseudohomophones pseudowords basewords
Length LF HF LF HF LF HF
3-letters ZEE ALD ZER ALZ ZEH ALT
4-letters EKKE FOLK EFKE BOLK ECKE VOLK
5-letters ZISD RAICH ZWISP REUCH ZWIST REICH
Note. LF = low baseword frequency, HF = high baseword frequency
Table 3. Mean response times (with standard error) and error rates for pseudohomophones,
pseudowords and words
Response Time (ms) Error rates (%)
LF HF LF HF
PSH 959(15) 909(12) 1.68 0.67
PSW 893(10) 891(12) 0.40 0.30
WOR 792(16) 748(11) 1.23 1.66
Note. PSH = pseudohomophones, PSW = pseudowords and WOR = words. LF = low (base)word frequency, HF = high
(base)word frequency.
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Table 4. Brain regions showing greater activation for pseudohomophones compared to
pseudowords (voxel-level uncorrected p < .001, FDR cluster-level corrected p < 0.05).
Brain Region Brodmann Area hem x y z mm³ Zmax
pseudohomophones > pseudowords
Insular Cortex, Frontal
Orbital Cortex
47,13 L -27 20 -5 44 4.54
Frontal Pole, pars orbitalis 47 L -42 44 -5 31 4.39
Supplementary Motor Area,
Paracingulate Gyrus,
Superior Frontal Gyrus
8 L -6 23 46 73 4.01
Middle Temporal Gyrus,
temporooccipital part,
Inferior Temporal Gyrus,
temporooccipital part
20 R 60 -43 -11 28 3.95
Lateral Occipital Cortex,
superior division, SPL
7 L -30 -70 58 43 3.87
Note. x, y, z = coordinates according to MNI stereotactic space.
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Table 5. Brain regions showing greater activation for pseudohomophones with high compared to
pseudohomophones with low baseword frequency (voxel-level uncorrected p < .005, FDR
cluster-level corrected p < .05) exclusively masked by the contrast of pseudowords with high >
pseudowords with low baseword frequency (p > .05, uncorrected).
Brain Region Brodmann Area hem x y z mm³ Zmax
pseudohomophones (high) > pseudohomophones (low)
Lateral Occipital Cortex,
inferior/superior division,
Angular Gyrus
39 L -54 -61 28 363 4.56
Angular Gyrus 39/40 R 60 -52 37 212 4.26
Middle Temporal Gyrus,
posterior division, Superior
Temporal Gyrus, posterior
division, Supramarginal
Gyrus, posterior division,
Middle Temporal Gyrus,
temporo-occipital part
21 66 -34 1 199 4.08
Middle Temporal Gyrus,
posterior division, Middle
Temporal Gyrus, temporo-
occipital part, posterior
Superior Temporal Gyrus
21 L -63 -40 -2 89 3.80
Note. x, y, z = coordinates according to MNI stereotactic space
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Figure Captions
Figure 1: Activation for pseudohomophones and pseudowords compared to fixation baseline
(voxel level uncorrected p < .001; extent threshold k = 5 voxels). Note. Pseudohomophones in red,
pseudowords in blue, overlapping regions in pink. SFG = superior frontal gyrus, MTG = middle
temporal gyrus, SPL = superior parietal lobule.
Figure 2: (A) Axial slices showing regions stronger activated by pseudohomophones (PSH)
compared to pseudowords (PSW). (B) Signal change for high and low frequency
pseudohomophones and pseudowords for regions higher activated for pseudohomophones. Error
bars show standard error of the mean. Note: SFG = superior frontal gyrus, MTG = middle temporal
gyrus, SPL = superior parietal lobule.
Figure 3: (A) Axial slices showing regions differentially activated by pseudohomophones (PSH) with
high and low baseword frequencies. (B) Signal change for high and low frequency
pseudohomophones and pseudowords (PSW) for regions higher activated for
pseudohomophones. Error bars show standard error of the mean. Note: AG = angular gyrus, MTG
= middle temporal gyrus, STG = superior temporal gyrus.