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Erschienen in: Neuroscience ; 295 (2015). - S. 151-163
Neuroscience 295 (2015) 151–163
THE NEURAL BASES OF THE PSEUDOHOMOPHONE EFFECT:
PHONOLOGICAL CONSTRAINTS ON LEXICO-SEMANTIC ACCESS
IN READING
Key words: models of visual word recognition, fMRI, lexical
decision, phonological mediation, lexico-semantic access,
spelling-check.
M. BRAUN, a* F. HUTZLER, a T. F. MU¨NTE, c M. ROTTE, d
M. DAMBACHER, b,e F. RICHLAN a AND A. M. JACOBS b,f
a
Centre for Cognitive Neuroscience, University of Salzburg, Austria
b
Institute of Psychology, Freie Universita¨t Berlin, Germany
c
Dept. of Neurology and Institute of Psychology II, University
of Lu¨beck, Germany
d
Novartis Pharma, Basle, Switzerland
e
Dept. of Psychology, University of Konstanz, Germany
f
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.,
Hawelka et al., 2013; Ziegler 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, 2001a;
Ja¨ncke and Shah, 2004; Pammer et al., 2004; Alexander
and Nygaard, 2008; Braun et al., 2009; Briesemeister
et al., 2009; Wheat et al., 2010; Yao et al., 2011;
Perrone-Bertolotti et al., 2012).
Dahlem Institute for Neuroimaging of Emotion, Berlin, Germany
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 pseudohomophones than 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. Ó 2015 The Authors. Published by
Elsevier Ltd. on behalf of IBRO. This is an open access article
under the CC BY-NC-ND license (http://creativecommons.org/
licenses/by-nc-nd/4.0/).
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 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
*Corresponding author. Address: Centre for Cognitive Neuroscience,
Universita¨t Salzburg, Hellbrunner Straße 34, 5020 Salzburg, Austria.
Tel: +43-662-8044-5176; fax: +43-662-8044-5126.
E-mail address: [email protected] (M. Braun).
Abbreviations: AG, angular gyrus; ANOVA, analysis of variance; DRC,
dual-route cascaded model; fMRI, functional magnetic resonance
imaging; IPL, inferior parietal lobule; MROM-P, multiple read-out
model including phonology; pIO, posterior inferior occipital gyrus;
pSTS, posterior superior temporal sulcus; SFG, superior frontal gyrus;
SMA, supplementary motor area; SMG, supramarginal gyrus; TMS,
transcranial magnetic stimulation; vOT, ventral occipito-temporal
cortex.
http://dx.doi.org/10.1016/j.neuroscience.2015.03.035
0306-4522/Ó 2015 The Authors. Published by Elsevier Ltd. on behalf of IBRO.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
151
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URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-0-286341
152
M. Braun et al. / Neuroscience 295 (2015) 151–163
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; Ziegler and Jacobs, 1995; Seidenberg et al., 1996;
Jacobs et al., 1998; 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
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., 2001a,b).
In the light of this finding a spelling-check was proposed
to account for the baseword frequency effect (e.g., Paap
et 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. Unfortunately, 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, 2005, 2009; Binder
and Desai, 2011). 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 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, 2013), bilateral
insula (Borowsky et al., 2006; Mechelli et al., 2007), the
STG (Mesulam, 1990; Rumsey et al., 1997; Booth et al.,
2002), the SMG and AG (Paulesu et al., 1993; Binder
et al., 2003; Ischebeck et al., 2004; Joubert et al., 2004;
Kronbichler et al., 2007; Church et al., 2008), and the
M. Braun et al. / Neuroscience 295 (2015) 151–163
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; Poldrack et al., 1999;
Fiebach et al., 2002). 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 parts 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; Simos et al., 2009; Graves et al., 2010,
2014; 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 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., Price et al., 1997; Indefrey
and Levelt, 2004; Gold et al., 2005; Vigneau et al., 2006;
Booth et al., 2007; Richlan et al., 2009; Newman and
Joanisse, 2011; Whitney et al., 2011; Noonan et al.,
2013). 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 (Indefrey and Levelt, 2004; Simos et al.,
2009; Graves et al., 2010, 2014; Newman and Joanisse,
2011) or semantic processing involving the ventral path
(Price et al., 1997; Indefrey and Levelt, 2004; Gold et al.,
2005; Vigneau et al., 2006; Booth et al., 2007; Richlan
et al., 2009; Newman and Joanisse, 2011; Whitney
et al., 2011; Noonan et al., 2013).
Present study
The aim of the present study was to investigate
the pseudohomophone and the baseword frequency
effect in lexical decision by means of functional magnetic
resonance imaging (fMRI). The demonstration of
153
pseudohomophone and baseword frequency effects in
lexical decision, a task which does not necessarily
require phonological processing (Seidenberg, 1985;
Grainger and Jacobs, 1996) 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 lowfrequency 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-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 orthographic–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.
EXPERIMENTAL PROCEDURES
All procedures were cleared by the local ethics review
board.
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M. Braun et al. / Neuroscience 295 (2015) 151–163
Table 1. Sample stimuli
Length
3-Letters
4-Letters
5-Letters
Pseudohomophones
Pseudowords
Basewords
LF
HF
LF
HF
LF
HF
ZEE
EKKE
ZISD
ALD
FOLK
RAICH
ZER
EFKE
ZWISP
ALZ
BOLK
REUCH
ZEH
ECKE
ZWIST
ALT
VOLK
REICH
Note: LF = low baseword frequency; HF = high baseword frequency.
Participants
Fourteen right-handed students (three men, mean age
23 years, range 20–30) 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 lowfrequency basewords (less than 20 occurrences per
million, mean 6.8). Frequency estimates were taken from
the CELEX database (Baayen et al., 1993) (see Table 1).
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 a 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 highfrequency 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 2
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).
Procedure
Before participants were placed in the scanner they were
given written instructions and were 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 gray background using upper
case letters set in Courier 48-pt 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 min.
The experiment was implemented using Presentation
experimental software (Neurobehavioral Systems Inc,
Albany, CA, USA).
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.
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M. Braun et al. / Neuroscience 295 (2015) 151–163
Table 2. Descriptive statistics for pseudohomophones, pseudowords
and words
Frequency
High
Mean
Pseudohomophones
bwF
6.8(6.6)
BF
2623(5038)
BN
17(11)
FN
3242(16083)
HFN
3.6(2.3)
Letters
4(0.84)
N
3.6(2.3)
Low
854.3(3390)
3713(4419)
16(9)
1231(3609)
3.4(2.5)
4(0.82)
3.4(2.5)
430.5
3157.5
16.5
2236.5
3.5
4
3.5
Pseudowords
bwF
BF
BN
FN
HFN
Letters
N
6.8(6.6)
5150(15883)
16(11)
3416(15727)
2.8(2.2)
4(0.84)
3.6(2.3)
854.3(3390)
8051(21930)
15(10)
5324(22117)
2.8(1.9)
4(0.82)
3.4(2.5)
430.5
6600.5
15.5
4370
Words
F
BC
BF
BN
N
FHFN
FN
HFN
Letters
Syl
3.3(2.7)
42(49)
4025(5492)
15(10)
2.9(2.5)
2778(15869)
2722(14595)
1.8(1.9)
4(0.82)
1.5(0.5)
421.2(295)
52(55)
13672(23806)
20(10)
4.3(2.8)
5653(22539)
5496(20745)
0.5(0.7)
4(0.82)
1.3(0.5)
212.3
47.5
8848.5
17.5
3.6
4215.5
4109
1.18
4
1.4
4
3.5
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.
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 = 2000 ms, TE = 29 ms, flip angle = 80°, 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 coregistration step was performed to adjust the images of
each subject to the first individual 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 3 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 seven items per subject)
and were therefore not modeled separately. Statistical
analysis was performed in a two-stage mixed effects
model. On the subject-specific first-level model
conditions of interest were contrasted against the fixation
baseline. These subject-specific contrast images were
used for the second-level group analysis. Direct contrasts
between pseudohomophones and pseudowords with
high and low baseword frequency were calculated with a
2 2 repeated measures analysis of variance (ANOVA)
and paired t-tests. Stereotaxic coordinates for voxels with
maximal z-values within activation clusters are reported
in the MNI coordinate system.
RESULTS
Behavioral results
Response times to words were about 150 ms faster than
to nonwords. Percentage of errors to words and
nonwords was low and nearly the same (2.89% vs.
3.05%) and therefore not further analyzed. Items with
response times above 2000 ms 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 highfrequency items in each category. Table 3 shows the
mean response times and error rates for the different
stimuli categories. For response times, separate 2 2
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 lowcompared to high-frequency basewords (t = 3.610,
df = 13, p = .003). In contrast, response times to
pseudowords did not differ with regard to frequency
Table 3. Mean response times (with standard error) and error rates for
pseudohomophones, pseudowords and words
PSH
PSW
WOR
Response time (ms)
Error rates (%)
LF
HF
LF
HF
959(15)
893(10)
792(16)
909(12)
891(12)
748(11)
1.68
0.40
1.23
0.67
0.30
1.66
Note: PSH = pseudohomophones, PSW = pseudowords and WOR = words.
LF = low (base)word frequency; HF = high (base)word frequency.
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M. Braun et al. / Neuroscience 295 (2015) 151–163
(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 lowfrequency basewords.
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
was 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 Fig. 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.
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 (SFG), inferior/superior parietal regions and
in inferior/middle temporal gyri on the right (see Table 4).
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 SFG all stimulus
conditions showed greater activation for low-frequency
items with low-frequency pseudohomophones eliciting the
strongest activation. The separately performed 2 2
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; 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
Fig. 2 and Table 4).
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 highfrequency basewords showed the highest and
pseudohomophones with low-frequency basewords
Fig. 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.
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M. Braun et al. / Neuroscience 295 (2015) 151–163
Table 4. Brain regions showing greater activation for pseudohomophones compared to pseudowords (voxel-level uncorrected p < .001, FDR clusterlevel corrected p < 0.05)
Brain region
Pseudohomophones > Pseudowords
Insular cortex, frontal orbital cortex
Frontal pole, pars orbitalis
Supplementary motor area, paracingulate gyrus, superior frontal gyrus
Middle temporal and inferior temporal gyrus temporooccipital
parts
Lateral occipital cortex, superior division, SPL
x
y
mm3
Zmax
5
5
46
11
44
31
73
28
4.54
4.39
4.01
3.95
58
43
3.87
z
Brodmann area
hem
47,13
47
8
20
L
L
L
R
27
42
6
60
20
44
23
43
7
L
30
70
Note: x, y, z = coordinates according to MNI stereotactic space.
Fig. 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.
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M. Braun et al. / Neuroscience 295 (2015) 151–163
showed always the lowest activation. The four clusters
showing a difference in activation for pseudohomophones
with high compared to pseudohomophones with
low-frequency 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
Fig. 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. lowfrequency pseudowords. The results revealed a reliable
baseword frequency effect for pseudohomophones but
not for pseudowords (see Table 5).
The separately performed 2 2 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 lowfrequency 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 ttests for the comparisons of high- and low-frequency
pseudohomophones and high- and low-frequency
pseudowords were calculated. The results showed that
the effect of baseword frequency was significant for
pseudohomophones (left AG: t = 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., Van Orden, 1987; Frost, 1998;
Coltheart et al., 2001). 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 SFG. Furthermore, a right 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; Binder et al.,
2009; Newman and Joanisse, 2011; Cattinelli et al.,
2013; 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., Price et al., 1997;
Indefrey and Levelt, 2004; Gold et al., 2005; Vigneau
et al., 2006; Booth et al., 2007; Richlan et al., 2009;
Newman and Joanisse, 2011; Whitney et al., 2011;
Noonan et al., 2013) 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., Fiebach et al.,
2002; Binder et al., 2003).
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 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
M. Braun et al. / Neuroscience 295 (2015) 151–163
159
Fig. 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.
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 highand 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
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M. Braun et al. / Neuroscience 295 (2015) 151–163
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
Pseudohomophones (high) > Pseudohomophones (low)
Lateral occipital cortex, inferior/superior division, angular gyrus
Angular gyrus, supramarginal gyrus
Middle-superior temporal gyrus and supramarginal gyrus posterior divisions, middle
temporal gyrus temporooccipital part
Posterior middle temporal gyrus, middle temporal gyrus temporooccipital part, posterior
superior temporal gyrus
x
z
mm3
Zmax
61
52
34
28
37
1
363
212
199
4.56
4.26
4.08
40
2
89
3.80
y
Brodmann
area
hem
39
39/40
21
L
R
R
54
60
66
21
L
63
Note: x, y, z = coordinates according to MNI stereotactic space.
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.,
2001a,b). 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 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., Gaillard et al., 2006; Cohen et al., 2008). 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 (Price et al., 1997;
Indefrey and Levelt, 2004; Gold et al., 2005; Vigneau
et al., 2006; Booth et al., 2007; Mechelli et al., 2007;
Richlan et al., 2009; Newman and Joanisse, 2011;
Whitney et al., 2011; Noonan et al., 2013) 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 greater activation in
brain regions known to process phonological and/or lexicosemantic 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 (McCandliss
et al., 2003; Sandak et al., 2004) but also the MTG of
the ventral visual pathway (Jobard et al., 2003; Sandak
et al., 2004; Schurz et al., 2010) 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 spelling-check. But this serial mechanism
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M. Braun et al. / Neuroscience 295 (2015) 151–163
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 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 orthographicphonological 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.
Acknowledgments—Thomas F. Mu¨nte was supported by grants
of the DFG and BMBF.
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(Accepted 17 March 2015)
(Available online 21 March 2015)