Independent Distractor Frequency and Age-of-Acquisition - Am MIT spezialisierte KI-Forschung

Independent Distractor Frequency and Age-of-Acquisition
Effects in Picture–Word Interference: fMRI Evidence
for Post-lexical and Lexical Accounts according
to Distractor Type

Greig I. de Zubicaray1, Michele Miozzo2, Kori Johnson1,
Niels O. Schiller3, and Katie L. McMahon1

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Abstrakt

■ In two fMRI experiments, participants named pictures with
superimposed distractors that were high or low in frequency or
varied in terms of age of acquisition. Pictures superimposed
with low-frequency words were named more slowly than those
superimposed with high-frequency words, and late-acquired
words interfered with picture naming to a greater extent than
early-acquired words. The distractor frequency effect (Experi-
ment 1) was associated with increased activity in left premotor
and posterior superior temporal cortices, consistent with the

operation of an articulatory response buffer and verbal self-
monitoring system. Umgekehrt, the distractor age-of-acquisition
Wirkung (Experiment 2) was associated with increased activity in
the left middle and posterior middle temporal cortex, konsis-
tent with the operation of lexical level processes such as lemma
and phonological word form retrieval. The spatially dissociated
patterns of activity across the two experiments indicate that dis-
tractor effects in picture–word interference may occur at lexical
or postlexical levels of processing in speech production. ■

EINFÜHRUNG

Forty years of psycholinguistic research have demon-
strated that saying a word, the most fundamental task in
Apropos, requires selecting from among a set of activated
word candidates (see Goldrick, 2007; Dell & Sullivan,
2004). Daher, if a speaker wants to say dog, other words
are activated in addition to the target word dog. To the
extent that multiple lexical candidates are activated, theo-
ries of word production need to identify the nature of
these candidates as well as the degree to which they inter-
fere with target word production.

To meet this challenge, researchers have developed
paradigms that permit introducing word competitors while
varying their characteristics (z.B., semantic, syntactic, oder
phonological). A widely used paradigm has been picture–
word interference (PWI; Rosinski, Golinkoff, & Kukish,
1975), in which participants named a picture of a target
object in the context of a superimposed distractor word
that they are instructed to ignore. An important empirical
observation in PWI is semantic interference (SI), the rela-
tive slowing of naming latencies to target pictures in the
context of categorically related compared with unrelated
distractors, Zum Beispiel, picture–word pairs like dog–fox.

1University of Queensland, 2University of Cambridge, 3Leiden
Universität

Informative results about the locus of distractor interfer-
ence were obtained by manipulating distractor frequency.
Results from several experiments converged in demon-
strating greater interference for low-frequency (LF) dis-
tractors than high-frequency (HF) distractors (Catling,
Dent, Johnston, & Balding, 2010; Dhooge & Hartsuiker,
2010; Miozzo & Caramazza, 2003). The distractor fre-
quency effect was obtained even with semantically un-
related picture–distractor pairs (z.B., superimposing the
HF distractor book and the LF distractor stool to the picture
Hund). Außerdem, when effects of distractor frequency
and SI were induced in the same experiments (Miozzo &
Caramazza, 2003), they did not interact and exhibited dif-
ferent time courses, indicating the two do not share the
same processing locus.

A general framework to explain the greater interference
of LF distractors presupposes limited processing capacity,
whereby word distractors are processed slightly ahead
of target pictures, causing a delay in target naming pro-
portional to the time needed to process the distractors
(Miozzo & Caramazza, 2003). Because processing time is
longer for LF than HF words, LF distractors would generate
greater interference, as indeed observed in the distractor
frequency effect. Jedoch, the locus of the processing
delay resulting in the distractor frequency effect has proven
more problematic to define. Two accounts have been
examined, hereafter referred to as “input” and “output”

Zeitschrift für kognitive Neurowissenschaften 24:2, S. 482–495

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accounts, jeweils (Mahon, Costa, Peterson, Vargas, &
Caramazza, 2007; Miozzo & Caramazza, 2003).

The “input” account locates the distractor frequency
effect at the level of the (spoken or orthographic) recog-
nition mechanisms leading to accessing word meaning.
Because LF words have lower resting activation levels than
HF words as is typically assumed in recognition models
(z.B., McClelland & Rumelhart, 1981; Morton, 1969; Aber
see Norris, 2006, for a critical discussion of this assump-
tion), then LF words should be recognized more slowly
and interfere comparatively more. The assumption that
HF words might be processed more quickly also receives
support from network modeling studies. Zum Beispiel, In
Steyvers and Tenenbaumʼs (2005) model of developing
semantic networks, word frequency influences the prob-
ability of connecting new nodes to existing nodes, resulting
in HF words having more central, highly connected nodes
that are more likely to be accessed first.

Im Gegensatz, the “output” account locates the distractor
frequency effect at the level of postlexical mechanisms lead-
ing to the assembly and execution of articulatory programs
(z.B., Mahon et al., 2007; Miozzo & Caramazza, 2003). Word
distractors are assumed to have a privileged relationship
with the articulators and enter an output buffer as phono-
logically well-formed responses. Außerdem, the speed at
which a response enters the output buffer is assumed to
be related to frequency and to influence the speed at which
the distractor can be excluded according to task-relevant
Kriterien. By entering the buffer faster, HF distractors are
excluded earlier, thus incurring shorter delays in picture
naming relative to LF distractors.

Discriminating between these alternative accounts has
implications for explanations of PWI and word production
theories alike. Zum Beispiel, if the “output” account were
correct, distractors would potentially interact with mul-
tiple levels of processing of word production, einschließlich
the level where phonological/articulatory information is
computed. Zum Beispiel, the SI effect could result from
categorically related distractors entering the buffer faster
because of semantic priming, with a decision mechanism
taking longer to exclude the distractor as it also satis-
fies some response relevant criteria (Mahon et al., 2007;
Miozzo & Caramazza, 2003). Somit, according to this ac-
zählen, distractors potentially influence prelexical semantic
processes as well as postlexical ones. Andererseits,
the distractor frequency effect appears problematic for
the lexical selection by competition (LSC) account that
proposes the time taken to select the target word depends
on the activation levels of the competing lexical nodes.
Entsprechend, the higher the activation level of compet-
ing words, the longer name selection takes (z.B., Levelt,
Roelofs, & Meyer, 1999; Starreveld & La Heij, 1996; Harley,
1993; Roelofs, 1992; La Heij, 1988). Because HF words
should be more strongly activated compared with LF
Wörter, this view incorrectly anticipates larger interference
for the former than the latter, unless additional assump-
tions are made (z.B., distractor blocking; Roelofs, 2005).

There are results seemingly favoring the “output” ac-
zählen. Zum Beispiel, the distractor frequency effect varied
as a function of distractor phonology, suggesting that this
is an effect occurring at the level where phonological/
articulatory information are processed in naming (Miozzo
& Caramazza, 2003). A further result was accrued by
Dhooge and Hartsuiker (2010; Experiment 2) by masking
written word distractors. They reasoned that if the out-
put account were correct, masking the distractor should
preclude the formation of a response in the articulatory
output buffer, thus eliminating the distractor frequency
Wirkung. Masking did eliminate the effect. Darüber hinaus, a di-
rect testing of the “input” account conducted by Miozzo
and Caramazza (2003; Experimente 2 Und 3) did not yield
results expected within this account. Althought distractor
interference should reduce when word recognition is facili-
tated (z.B., by repetition), it should increase if word recog-
nition is made more difficult (z.B., by cAsE aLtErNaTiOn).
Neither prediction was confirmed.

Each of the tests employed to examine the “input” and
“output” hypotheses relies on a complex series of assump-
tionen. Let us exemplify this point referring to case alter-
nation. This manipulation represents a valid test of the
“input” account if one assumes that (A) case alternation
slows ignored/unattended distractor processing and mod-
ulates the interference effect and (B) frequency effects in
word recognition and picture naming have the same locus
and should therefore interact. With respect to the first
assumption, case-alternated primes have no differential
impact on masked repetition priming (Forster, 1998) oder
on conventional priming at short SOAs (Lee, Honig, &
Lee, 2002), indicating a case alternation effect may only
appear for attended stimuli. With respect to the second
assumption, there is evidence suggesting that frequency
effects in word comprehension (reading) and spoken
word production are different, with frequency effects in
the former modality being relatively independent of se-
mantic processing while depending on the presence or
absence of semantic constraint in the latter (Gollan et al.,
2011). In brief, the complexity of assumptions underlying
the tests of “input” and “output” accounts would make
it desirable to acquire additional evidence to adjudicate
between the alternative proposals. We addressed this
issue from a novel perspective in the present investigation
by characterizing the neural correlates of the distractor
frequency effect.

A number of studies have adopted neuroimaging tech-
niques to provide converging evidence for the level(S) bei
which distractor effects occur in speech production (z.B.,
Righi, Blumstein, Mertus, & Worden, 2010; Bles & Jansma,
2008; Heim, Friederici, Schiller, Rüschemeyer, & Amunts,
2008; de Zubicaray, McMahon, Eastburn, & Pringle, 2006;
de Zubicaray, Wilson, McMahon, & Muthiah, 2001). Das
is because of the increasing realization that brain imag-
ing data represents an additional dependent variable of
relevance to spreading activation models (z.B., Goldrick,
2007; Dell & Sullivan, 2004). There is now a large literature

de Zubicaray et al.

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relating brain activation data to stages of processing in
models of spoken word production (z.B., Acheson, Hamidi,
Binder, & Postle, 2011; Peeva et al., 2010; Schuhmann,
Schiller, Goebel, & Sack, 2009; Alario, Chainay, Lehericy, &
Cohen, 2006; Indefrey & Levelt, 2004). These studies have
identified roles for the midsection of the left middle tem-
poral gyrus in lexical semantic processing and the poste-
rior section of the middle and superior temporal gyri
( Wernickeʼs area) in phonological word form retrieval,
jeweils, within a predominantly left hemisphere cere-
bral network. During naming of depicted objects, die Zeit
course of activation in these two regions typically occurs
zwischen 150 Und 300 msec following initial visual object
recognition, with postlexical processes of syllabification
and phonetic encoding followed by articulation occurring
zwischen 300 Und 600 msec attributed to the posterior left
inferior pFC (Brocaʼs area) and premotor cortical areas,
jeweils (z.B., Acheson et al., 2011; Schuhmann et al.,
2009; Indefrey & Levelt, 2004). Folglich, these stud-
ies provide candidate brain regions for testing the “input”
and “output” accounts. Crucially, the two accounts make
contrasting predictions concerning the brain regions as-
sociated with the distractor frequency effect. Although the
“output” account anticipates activation related to distractor
frequency in brain regions supporting phonological and
articulatory processing, no such activation is anticipated by
the “input” account.

A second goal of the present investigation relates to the
nature of the distractor frequency effect. There was a con-
founding, in PWI studies, of distractor frequency with age
of acquisition (AoA; see Brysbaert & Neu, 2009; Brysbaert
& Ghyselinck, 2006). Frequency and AoA are highly in-
tercorrelated, although have also been demonstrated to
exert independent effects in reading, Apropos, and speech
comprehension (Brysbaert & Cortese, 2011; Cortese &
Khanna, 2007; Brysbaert & Ghyselinck, 2006). Zusätzlich,
a number of studies have provided evidence indicating
that frequency and AoA effects might have different loci
across a range of tasks (z.B., Catling & Johnston, 2009;
Dent, Johnston, & Humphreys, 2008). Further arguments
for the distinctiveness of AoA come from accounts attribut-
ing a processing advantage for early AoA words to network
plasticity—As the network develops, plasticity is reduced
resulting in less accessible representations for later ac-
quired words (z.B., Menenti & Burani, 2007; Lambon
Ralph & Ehsan, 2006; Ellis & Lambon Ralph, 2000). Consis-
tent with the growing body of evidence suggesting the
autonomy of AoA from frequency, Catling et al. (2010;
Experiment 2) were able to demonstrate a frequency-
independent distractor AoA effect in PWI, with late-acquired
words producing greater interference.

Parallel to differential effects demonstrated behaviorally,
partially distinct patterns of brain activation would possibly
emerge when investigating the fMRI correlates of fre-
quency and AoA. Behavioral data could thus help us to
identify possible candidate brain regions sensitive to AoA.
Belke, Brysbaert, Meyer, and Ghyselinck (2005) found an

interaction between the SI effect and AoA in blocked
cyclic naming, in which pictures are presented in categori-
cally homogeneous versus mixed contexts. They inter-
preted this result as indicating that the AoA effect occurs
at a lexical semantic (Lemma) level of processing (see also
Brysbaert & Ghyselinck, 2006). Of note, no interaction
between SI and frequency was found in the same task
(Santesteban, Costa, Pontin, & Navarrete, 2006), zu mit-
trasting pattern further suggesting the distinctiveness of
frequency and AoA effects. A number of studies have iden-
tified roles for the middle and posterior portions of the
left middle temporal gyrus (pMTG) in lexical semantic
Verarbeitung (z.B., Acheson et al., 2011; Peeva et al., 2010;
Indefrey & Levelt, 2004). These regions have demonstrated
increased activity in categorically related compared with
unrelated distractor conditions in fMRI studies of picture
Wir (z.B., de Zubicaray et al., 2001, 2006). In line with
the hypothesis that AoA is linked to lexical semantic pro-
Abschließen (Belke et al., 2005), it seems reasonable to anti-
cipate responsiveness of these regions to AoA.

The effect of distractor frequency and AoA in PWI were
tested in Experiments 1 Und 2, jeweils. We employed
sparse temporal sampling designs within two functional
MRI experiments, permitting overt naming responses to
be accurately recorded during scanning (z.B., Heim et al.,
2008; de Zubicaray et al., 2001, 2006).

EXPERIMENT 1

The goal of the first experiment was to determine the
locus of the distractor frequency effect, contrasting predic-
tions from input and output accounts. If the latter account
is correct, then we would expect to observe increased
activity for LF words in brain regions associated with artic-
ulation and control mechanisms. Reliable cerebral cor-
relates of postlexical stages of processing (syllabification,
phonetic encoding, and motor articulation) encompass
both left inferior frontal gyrus (IFG) and premotor cortices
(z.B., Peeva et al., 2010; Eickhoff, Heim, Zilles, & Amunts,
2009; Schuhmann et al., 2009; Tremblay & Gracco, 2009;
Alario et al., 2006; Indefrey & Levelt, 2004). Within the
premotor cortex, Alario et al. (2006) identified a rostro-
caudal gradient in the SMA corresponding to postlexical
Auswahl, phonetic encoding, and articulatory processes
during spoken word production (see also Peeva et al.,
2010). Tremblay and Gracco (2009) likewise recently
identified pre-SMA as playing a central role in response
selection of spoken words. Jedoch, as Dhooge and
Hartsuiker (2010) notiert, the output account does not
specify the nature of the control mechanism hypothesized
to operate on the articulatory buffer. They tentatively
proposed that the verbal self-monitoring system might be
responsible for this function, documenting a range of
experimental findings in support. In their meta-analysis,
Indefrey and Levelt (2004) ascribed the monitoring of
both internal and external speech to bilateral posterior

484

Zeitschrift für kognitive Neurowissenschaften

Volumen 24, Nummer 2

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superior temporal gyri (pSTG), a finding supported by
more recent studies (see Price, 2010; z.B., Zheng, Munhall,
& Johnsrude, 2010). Daher, it seems reasonable to assume
that the premotor cortex (especially SMA), IFG, and pSTG
would show increased activity if the output account were
correct.

As noted in Introduction, the input account predicts
increased activity in the middle and posterior portions of
the left middle temporal gyrus and left posterior middle
and superior temporal gyri (pMTG/pSTG) as they have
been implicated in lexical processing generally (Lemma
selection and phonological word form retrieval, bzw-
aktiv; z.B., Acheson et al., 2011; Peeva et al., 2010; Indefrey
& Levelt, 2004) and during SI in PWI specifically (z.B.,
de Zubicaray et al., 2001, 2006).

in the center of each picture. Stimuli were back-projected
using a BenQ SL705X projector onto a screen that partici-
pants viewed through a mirror mounted on the head coil,
and subtended approximately 10° of visual angle when each
participant was positioned for imaging. A 30 db attenuating
headset was used to reduce gradient noise. Naming re-
sponses were recorded on digital audio files using a custom
positioned fiber-optic dual-channel noise-cancelling micro-
phone attached to the head coil (FOMRI-III, Optoacoustics
Ltd., Or-Yehuda, Israel; www.optoacoustics.com). Naming lat-
encies were determined automatically with a voice key soft-
ware custom written in Microsoft VisualBasic and verified
manually using Audacity software (audacity.sourceforge.
net) in case nonvocal noise triggered the voice key.

Methoden

Teilnehmer

Seventeen healthy volunteers (10 Frauen) with a mean
age of 22 Jahre (SD = 3.5 Jahre) performed the experi-
ment. All were undergraduate students of the University
of Queensland. All were right-handed and native English
speakers, with no history of neurological or psychiatric dis-
Befehl, substance dependence, or known hearing deficits.
All had normal or corrected-to-normal vision and gave in-
formed consent in accordance with the protocol approved
by the Medical Research Ethics Committee of the Univer-
sity of Queensland. They were reimbursed AUD$30 for
participating.

Materials

The materials were identical to those used by Catling et al.
(2010; Experiment 1). Forty-eight black-and-white line
drawings were selected from Snodgrass and Vanderwart
(1980). These were split evenly into early and late-acquired
picture sets that were matched on a range of linguistic vari-
ables following Barry, Hirsh, Johnston, and Williams (2001).
HF and LF distractors were matched on a range of lin-
guistic variables including AoA (for information about the
matching variables, see the appendix in Catling et al.,
2010). Each target picture was paired with an HF and an
LF word that did not share a semantic or phonological
relationship with it. Target pictures were also presented
without distractor words in a neutral condition to examine
a potentially independent effect of target picture AoA and
to determine the direction of distractor related activity in
the fMRI experiment.

A laptop PC running Microsoft VisualBasic and ExacTicks
(Ryle Design, Mt. Pleasant, Michigan) software was used to
show the picture and word stimuli and record vocal re-
sponses on digital audio files (sampling rate, 11 kHz). Line
drawings were presented in black on a luminous white
background, and the visual distractor words were shown
in black lowercase Times New Roman 18-point bold font

Verfahren

A PWI paradigm was employed. Participants were first
familiarized with the set of experimental pictures with the
appropriate label printed below. The size of the pictures,
including background, was approximately 10 cm wide by
10 cm high. Over two consecutive practice blocks they
were instructed to name the pictures as fast and as accu-
rately as possible. Erroneous naming responses were cor-
rected. In a final block, they viewed the pictures without
labels and were instructed to name the pictures per the
instructions above.

Two experimental blocks each comprising 72 Versuche
presented in pseudorandom order were then conducted
(48 target pictures presented in three conditions: neutral/
no distractor, HF distractor, and LF distractor). A short
break was permitted between the two blocks while a
structural image was acquired (see fMRI Acquisition be-
niedrig). Trial presentation was pseudorandomized across
participants using Mix software (van Casteren & Davis,
2006), such that two presentations of the same picture
were always interceded by at least five different pictures,
and trials from a given condition were presented no more
than twice in succession. Participants were instructed
to name the pictures as quickly and accurately as possible
while ignoring the distractor word. They were also in-
structed not to speak or move during image acquisition
Und, in the event of a naming error, not to correct their
response. Trial presentation involved the following se-
quence: A fixation point (+) was shown for 500 ms,
followed by the presentation of the superimposed target
and distractor for 750 ms. Intertrial interval was 15 Sek.

fMRI Acquisition

Scanning was performed using a Bruker Medspec (Erlangen,
Deutschland) 4T system equipped with a transverse electromag-
netic head coil for radiofrequency transmission and recep-
tion ( Vaughan et al., 2002). A point-spread function
mapping sequence was first acquired to correct geometric
distortions in the functional images (Zaitsev, Hennig, &

de Zubicaray et al.

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Speck, 2003). Functional images depicting BOLD contrast
were then acquired with a gradient-echo EPI sequence
optimized for both image quality and noise reduction
(64 × 64 Matrix, 36 axial slices, 3.5 mm in plane resolution,
Scheibendicke = 3.5 mm, effective repetition time [TR] =
15 Sek; Echozeit = 30 ms; flip angle = 90°; McMahon,
Pringle, Eastburn, & Maillet, 2004). Two blocks of 73 Bild
volumes were acquired using a sparse temporal sampling
sequence to capture the estimated peak BOLD signal re-
sponse to task-related neural activity (Eden, Joseph, Braun,
Braun, & Zeffiro, 1999; Elliott, Bowtell, & Morris, 1999).
For each trial, no field gradients were applied for a 4-sec
period of relative silence, allowing for stimulus presenta-
tion and the participantʼs overt verbal response. A single
image volume was then acquired within 3 Sek, approxi-
mately coincident with the trialʼs estimated peak BOLD
response. No field gradients were applied for an additional
8-sec period to allow the BOLD response to the gradient
noise to return to baseline (for a diagram of the imaging
protocol, siehe Abbildung 1 in de Zubicaray et al., 2001). Kopf
movement was limited by foam padding within the head
coil. A 3-D T1-weighted structural image was acquired be-
tween the two functional imaging runs using a magnetiza-
tion prepared rapid acquisition gradient-echo sequence
(MP-RAGE; 2563 Matrix; 0.9 mm3 voxels). Total imaging
time was approximately 50 min.

fMRI Data Preprocessing and Analysis

The fMRI data were preprocessed and analyzed using
statistical parametric mapping software (SPM8; Wellcome
Department of Imaging Neuroscience, Queen Square,
London, VEREINIGTES KÖNIGREICH.). The first volume in each fMRI block was
discarded, and the remaining images were realigned to
the first image of the first block using the INRIAlign tool-
box (Freire, Roche, & Mangin, 2002). A mean image was
generated from the realigned series and coregistered
to the T1-weighted image. The T1-weighted image was
next segmented using the “New Segment” procedure. Der
“DARTEL” toolbox (Aschenbrenner, 2007) was then employed
to create a custom group template from the segmented
gray and white matter images, and individual flow fields
were used to normalize the realigned fMRI volumes to
the Montreal Neurological Institute (MNI) atlas T1 tem-
plate. The images were resampled to 3 mm3 voxels and
smoothed with a 9-mm FWHM isotropic Gaussian kernel.
Global signal effects were then estimated and removed
using a voxel level linear model (Macey, Macey, Kumar, &
Harper, 2004).

We conducted a two-stage, mixed effects model statis-
tical analysis. Event types corresponding to distractor and
Fehler (see Behavioral Results below) conditions were
modeled as effects of interest with delta functions repre-
senting each onset and convolved with a basis function
consisting of a single finite impulse response with a win-
dow length corresponding to the TR. As the sparse image
sequence does not acquire BOLD time course informa-

tion, trials were not convolved with a conventional hemo-
dynamic response function (see Gracco, Tremblay, & Pike,
2005; Eden et al., 1999; Elliott et al., 1999). Hoch- and low-
pass filtering were not applied, because of the long TRs
involved and the use of detrending (Macey et al., 2004).
Linear contrasts were applied to each participantʼs param-
eter estimates at the fixed effects level, then entered in
a group level repeated measures ANOVA, in which co-
variance components were estimated using a restricted
maximum likelihood procedure to correct for nonspheric-
ität (Friston et al., 2002). Regions with significant main
effects and/or interactions were investigated with planned
t contrasts.

A priori ROIs (Figur 1) were defined using labeled
maximum likelihood gray matter maps from 3-D probabi-
listic atlases (Eickhoff et al., 2005; Hammers et al., 2003).
These were left mid-MTG and posterior temporal cortex
(Hammers et al., 2003) and left BA 44/45 (Brocaʼs area)
and premotor cortex (BA 6; Eickhoff et al., 2005). Letzteres
cytoarchitectonically defined region encompassed the
stereotactic MNI coordinates reported by both Tremblay
and Gracco (2009) and Alario et al. (2006) for their SMA
regions involved in postlexical selection, as well as the ven-
trolateral premotor area implicated in articulation (Peeva
et al., 2010; Indefrey & Levelt, 2004). A height threshold
of p < .005 was adopted in conjunction with a clus- ter threshold of p < .05 estimated for the whole brain (54 contiguous voxels) and for each ROI volume using a Monte Carlo estimation procedure with 10,000 simula- tions (alphasim, implemented in Analysis of Functional NeuroImages toolkit, AFNI; National Institute of Mental Health, Bethesda, MD). The height threshold of p < .005 threshold was adopted instead of the more commonly used p < .001 to aid identification of possible overlap in activated regions across experiments. D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 4 / 2 2 4 4 / 8 2 2 / 1 4 9 8 4 2 3 / 2 1 6 7 5 7 o 7 c 4 n 5 _ 8 a / _ j 0 o 0 c 1 n 4 1 _ a p _ d 0 0 b 1 y 4 g 1 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j t f / . u s e r o n 1 7 M a y 2 0 2 1 Figure 1. Cerebral ROIs derived from maximum probability maps (Eickhoff et al., 2005; Hammers et al. 2003) used in Experiments 1 and 2, rendered on a left hemisphere surface of an individual brain. Middle temporal cortex (yellow), posterior temporal cortex (red), premotor cortex (green), and Brocaʼs area (blue). 486 Journal of Cognitive Neuroscience Volume 24, Number 2 Results Behavioral Data Trials scored as errors included incorrect or omitted naming responses and dysfluencies (e.g., stuttering). These were excluded from analysis (5.2%). Additional trials were excluded in which naming onset RTs were <300 or>3000 ms (1.2%). Mean naming RTs as a function
of distractor condition are given in Table 1. Repeated mea-
sures ANOVAs were conducted for RTs with distractor
frequency and target picture AoA conditions with F1 treat-
ing participants as a random factor and F2 treating items
as a random factor. Because of the low error rates, diese
were not subjected to analysis.

There were significant main effects of target picture
AoA [F1(1, 16) = 6.92, MSE = 2283.15, P < .05, η2 = 0.30, and F2(1, 23) = 10.80, MSE = 5612.87, p < .005, η2 = 0.32] and distractor frequency [F1(2, 32) = 28.87, MSE = 2727.18, p < .001, η2 = 0.64, and F2(2, 46) = 37.04, MSE = 3215.37, p < .001, η2 = 0.62] and no sig- nificant interaction (both Fs < 3). Paired samples t tests showed a significant effect of distractor frequency [t1(16) = 2.8, p < .05 and t2(23) = 2.4, p < .05]. In summary, the re- sults replicated those of Catling et al. (2010; Experiment 1) with comparable RTs. Pictures with late-acquired names were named more slowly than those with early-acquired names, and the distractor frequency effect was confirmed; pictures with LF distractors were named more slowly than those with HF words. fMRI Data Data from a single participant were excluded from the fMRI analyses because of excessive head movement during image acquisition, defined as exceeding one voxel (3 mm) within an imaging run. Group-averaged motion and rota- tion parameters from the remaining 16 participants were less than 1 mm and 1°, respectively, consistent with data re- ported for sparse fMRI acquisitions in the literature (e.g., Gracco et al., 2005). A repeated measures ANOVA failed to reveal any brain region demonstrating a significant main effect of Target picture AoA or significant interaction be- tween Target picture AoA and Distractor frequency, either at the whole-brain level or in any of the a priori defined ROIs, although it did reveal a significant main effect of Distractor frequency. Therefore, the data were collapsed across Target picture AoA for subsequent analyses. A repeated measures ANOVA on the collapsed data (HF, LF and, neutral conditions) revealed clusters showing a signifi- cant main effect. These were subjected to post hoc planned t contrasts. A planned contrast of LF > HF distractors revealed sig-
nificant activity at the whole-brain level in three large clus-
ters encompassing medial and lateral premotor, primary
sensorimotor and caudal anterior cingulate cortices bilater-
ally, left pSTG/pMTG and right supramarginal gyrus/pSTG.
ROI analyses revealed significant activity in the left me-
dial and lateral premotor cortex and the left pSTG/pMTG
(Tisch 2 and Figure 2). No other ROIs showed significant
Aktivität. Zusätzlich, no significant activity was observed for
the reverse contrast (HF > LF distractors) at the whole-
brain level or in the ROIs nor did contrasts of LF and HF
distractor conditions versus the neutral condition reveal
any significant activity.

Diskussion

The distractor frequency effect was replicated (Catling et al.,
2010, Experiment 1; Dhooge & Hartsuiker, 2010, Experi-
ment 1), confirming that the effect is robust. The fMRI
results may be interpreted as being consistent with an out-
put account (Mahon et al., 2007). Significantly increased
activity was observed in the medial and lateral premotor
cortex for LF versus HF distractor conditions, with a peak
corresponding to the boundary between the pre-SMA and
SMA. These regions have been associated with postlexical
selection and articulation by various researchers (Peeva
et al., 2010; Tremblay & Gracco, 2009; Alario et al., 2006;
Indefrey & Levelt, 2004). The absence of activity in the
IFG may be consistent with the notion that responses that
are already syllabified and phonetically encoded enter the
articulatory buffer (Mahon et al., 2007), as activity in this re-
gion precedes that in premotor and motor cortices during
speech production (Eickhoff et al., 2009). The increase in
activity in the left pSTG supports Dhooge and Hartsuikerʼs
(2010) proposal that the verbal self-monitoring system
may be responsible for checking the contents of the out-
put buffer and initiating the removal of inappropriate
responses. The absence of activity in the left mid-MTG indi-
cates that, contrary to the predictions of the input account,

Tisch 1. Naming Latencies for Experiment 1 (Distractor Frequency Effect)

Distractor Condition

Bilder

Low Frequency

High Frequency

Neutral (No Word Distractors)

Early

Late

Mean

1202 (356)

1215 (336)

1209 (345)

1175 (333)

1181 (357)

1178 (343)

Standard deviations are in parentheses.

1087 (323)

1142 (336)

1114 (328)

de Zubicaray et al.

487

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ich
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Tisch 2. Cerebral Regions Showing Significant Activity in the Two Experiments

Peak MNI (x y z)

Z Score

Cluster Size ( Voxels)

LF > HF Words (Experiment 1)

Bilateral premotor, primary sensorimotor and

caudal anterior cingulate corticesa

Left pSTG/pMTGa

Right supramarginal gyrus/pSTGa

Left medial preSMA/SMAb

Left lateral premotor cortexb

Left posterior temporal cortexb

Late AoA > Early AoA Words (Experiment 2)

Left mid-MTG and pSTGa

Left inferior parietal lobule/angular gyrusa

Bilateral anterior cingulatea

Left posterior temporal cortexb

Left middle temporal cortexb

Late AoA Words > Neutral (Experiment 2)

Right mid-STG

Inferior parietal lobule and pSTG

Left mid-MTG and pMTG/STG

Left posterior temporal cortexb

3 12 51

−48 −42 3

57 −42 24

−3 9 51

−45 −12 51

48 −42 3

−51 −24 3

−42 −57 21

3 33 30

−42 −33 12

−42 −57 18

−54 −27 −6

42 −9 −3

60 −18 18

−51 −30 12

−51 −33 12

Neutral > Late AoA Words (Experiment 2)

Left parahippocampal gyrus

−12 −39 −6

Height threshold p < .005 and p < .05, cluster corrected. aWhole-brain corrected. bROI corrected. 4.64 3.13 4.0 4.6 3.12 3.13 4.4 3.61 3.68 3.29 3.27 3.27 4.0 3.42 3.6 3.51 3.6 1909 88 182 317 28 55 107 150 74 33 20 16 56 61 67 52 86 the activation levels of nontarget lexical (lemma) nodes are unlikely to be responsible for the effect (e.g., Acheson et al., 2011; Indefrey & Levelt, 2004; see Miozzo & Caramazza, 2003). EXPERIMENT 2 The second experiment aimed to investigate whether the effects of distractor frequency and AoA involve common or distinct brain regions. According to the output account, the activation levels of nontarget lexical nodes do not affect selection (Mahon et al., 2007; also Miozzo & Caramazza, 2003). As neither early nor late-acquired words unrelated to the target picture are relevant to the response required by the task, this means the speed at which they enter the buffer will be primarily responsible for the effect. As early- acquired words are accessed first, they enter the buffer first and are first to be excluded (e.g., Lambon Ralph & Ehsan, 2006; Steyvers & Tenenbaum, 2005). Hence, it seems rea- sonable to presume that the output account predicts in- creased activity for the late AoA distractor condition in the same articulatory motor and verbal self-monitoring regions (following Dhooge & Hartsuiker, 2010) predicted for the distractor frequency effect in Experiment 1, namely, premotor cortex (especially SMA), IFG, and pSTG. In addi- tion, given the prior evidence afforded by Experiment 1, we would expect similar patterns of activity to be involved. The candidate regions for the input account are essentially the same as those proposed for the distractor frequency effect in Experiment 1, namely, left middle and posterior temporal cortex, if the effect has a lexical semantic locus as proposed by Belke et al. (2005). 488 Journal of Cognitive Neuroscience Volume 24, Number 2 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 4 / 2 2 4 4 / 8 2 2 / 1 4 9 8 4 2 3 / 2 1 6 7 5 7 o 7 c 4 n 5 _ 8 a / _ j 0 o 0 c 1 n 4 1 _ a p _ d 0 0 b 1 y 4 g 1 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j t . / f u s e r o n 1 7 M a y 2 0 2 1 Figure 2. Cerebral regions showing significantly increased activity in Experiment 1 for the independent distractor frequency effect (LF > HF
Wörter) in the whole-brain
analysis superimposed on
the surface rendering of
an individual brain (height
thresholded at p < .005 and cluster thresholded at p < .05): (A) left hemisphere lateral view, (B) left hemisphere medial view, (C) right hemisphere lateral view, and (D) right hemisphere medial view. Methods Participants Results Behavioral Data Seventeen healthy volunteers (11 women) with a mean age of 22 years (SD = 4.75 years) performed the ex- periment. None had participated in Experiment 1 and met the criteria described in the Participants section of Experiment 1. Materials Materials were identical to those in Catling et al. (2010; Experiment 2). The same pictures as Experiment 1 were employed, although early and late AoA distractors (again matched on a range of linguistic variables including fre- quency estimates from various corpora; see Catling et al., 2010; Appendix) were used. Each target picture was paired with an early AoA and late AoA word that did not share a semantic or phonological relationship with it. Target pic- tures were again presented without distractor words in a neutral condition to examine any effect of target picture AoA and to determine the direction of distractor related activity in the fMRI experiment. Procedure The procedure was identical to that in Experiment 1. fMRI Acquisition, Data Preprocessing, and Analysis Scoring criteria were identical to Experiment 1. Error trials were excluded from analysis (1.8%), as were RT outliers (0.1%). Mean naming RTs as a function of distractor con- dition are given in Table 3. Repeated measures ANOVAs were conducted for RTs with Distractor AoA and Target picture AoA conditions with F1 treating participants as a random factor and F2 treating items as a random factor. Because of the low error rates, these were not subjected to analysis. These revealed significant main effects of Target pic- ture AoA [F1(1, 16) = 11.22, MSE = 1937.54, p < .005, η2 = 0.41, and F2(1, 23) = 9.65, MSE = 9837.25, p < .01, η2 = 0.29] and Distractor AoA [F1(2, 32) = 58.64, MSE = 1964.64, p < .001, η2 = 0.86, and F2(2, 46) = 47.09, MSE = 2853.46, p < .001, η2 = .67] and no significant interaction (both Fs < 1). Paired samples t tests showed a Table 3. Naming Latencies for Experiment 2 (Distractor AoA Effect) Distractor Condition Pictures Early AoA Late AoA Neutral (No Word Distractors) Early Late Mean 1166 (261) 1225 (279) 1093 (3248) 1196 (313) 1239 (238) 1138 (268) 1180 (251) 1231 (258) 1116 (256) These were identical to those in Experiment 1. Standard deviations in parentheses. de Zubicaray et al. 489 D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 4 / 2 2 4 4 / 8 2 2 / 1 4 9 8 4 2 3 / 2 1 6 7 5 7 o 7 c 4 n 5 _ 8 a / _ j 0 o 0 c 1 n 4 1 _ a p _ d 0 0 b 1 y 4 g 1 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j f t / . u s e r o n 1 7 M a y 2 0 2 1 significant effect of Distractor AoA [t1(16) = 5.0, p < .001 and t2(23) = 3.0, p < .01]. In summary, the results repli- cated those of Catling et al. (2010; Experiment 2) with comparable RTs: Pictures with late-acquired names were named more slowly than those with early-acquired names, and the independent distractor AoA effect was confirmed: Pictures with late-acquired distractors were named more slowly than those with early-acquired words. fMRI Data Data from two participants were excluded from the fMRI analyses because of excessive head movement based on identical criteria to Experiment 1. Group-averaged motion and rotation parameters from the remaining 15 partici- pants were again less than 1 mm and 1°, respectively. A repeated measures ANOVA failed to reveal any brain region demonstrating a significant main effect of Target picture AoA or significant interaction between Target picture AoA and Distractor AoA either at the whole-brain level or in any of the a priori defined ROIs, although it did reveal a significant main effect of Distractor AoA. Therefore, the data were collapsed across target picture AoA for sub- sequent analyses. A repeated measures ANOVA on the col- lapsed data revealed clusters showing a significant main effect of Distractor (early, late, and neutral). These were subjected to post hoc planned t contrasts. A planned contrast of Late > Early AoA distractors re-
vealed significant activity at the whole-brain level in three
clusters encompassing the left mid- MTG and pSTG, links
inferior parietal lobule/angular gyrus and bilateral ante-
rior cingulate (Figur 3). ROI analyses revealed significant
activity in two regions of left pSTG/MTG and left mid-

MTG (Tisch 2). No other ROIs showed significant activ-
ität. No significant activity was observed for the reverse
Kontrast (Early > Late AoA distractors) at the whole-brain
level or in the ROIs.

Zusätzlich, significant activity was observed at the whole-
brain level for the contrast of the Late AoA distractor >
Neutral conditions in the right mid-STG, inferior parietal
lobule and pSTG and left mid-MTG and pMTG/STG (Feige-
ure 2). ROI analyses also revealed significant activity in the
left pMTG/pSTG. No significant activity was detected in
the remaining ROIs. The reverse contrast (Neutral > Late
AoA) revealed a single cluster in the left parahippocampal
gyrus significant at the whole-brain level (Tisch 2). No sig-
nificant activity was observed in any of the ROI analyses
nor was any significant activity observed for the early AoA
distractor versus neutral contrasts either at the whole-brain
level or in ROI analyses.

Diskussion

The independent distractor AoA effect reported by Catling
et al. (2010; Experiment 2) was replicated. Significantly
increased activity was observed in the left mid-MTG and
pMTG/STG regions consistent with a role for these regions
in lexical selection and phonological word form retrieval
(z.B., Acheson et al., 2011; Peeva et al., 2010; Indefrey &
Levelt, 2004). The activity in the left pSTG might also be
considered consistent with the operation of the verbal
self-monitoring system, as Indefrey and Levelt (2004) Profi-
posed the region serves as a common store of lexical word
form representations for both production and perception.
We therefore calculated the overlap in activated voxels
between the distractor frequency (Experiment 1) Und

Figur 3. Cerebral regions
showing significantly increased
activity in Experiment 2 für
the independent distractor
AoA effect (Late > Early words)
in the whole-brain analysis
superimposed on the surface
rendering of an individual brain
(height thresholded at p < .005 and cluster thresholded at p < .05): (A) left hemisphere lateral view, (B) left hemisphere medial view, (C) right hemisphere medial view, and (D) voxels overlapping between the distractor frequency (Experiment 1) and AoA effects (circled). 490 Journal of Cognitive Neuroscience Volume 24, Number 2 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 4 / 2 2 4 4 / 8 2 2 / 1 4 9 8 4 2 3 / 2 1 6 7 5 7 o 7 c 4 n 5 _ 8 a / _ j 0 o 0 c 1 n 4 1 _ a p _ d 0 0 b 1 y 4 g 1 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j f t . / u s e r o n 1 7 M a y 2 0 2 1 AoA effects via inclusively masking the former with the latter result. This revealed only 18 of 107 (or 17%) voxels overlapped solely in the posterior portion of the left pSTG, indicating that while the two effects share activity in pos- terior pSTG that might be sensitive to the self monitoring demands of the task, the distractor AoA effect is associ- ated with additional activity in both middle and posterior MTG consistent with the operation of lexical level pro- cesses (Figure 3). More importantly, the absence of sig- nificant activity in the premotor cortex or IFG precludes an interpretation of a postlexical mechanism operating at the level of an articulatory response buffer (Mahon et al., 2007). This contrasts with the extensive activity observed in the premotor cortex in Experiment 1, indicating that the independent distractor frequency and AoA effects involve different mechanisms. GENERAL DISCUSSION This article is the first to report fMRI data for the distractor frequency and AoA effects in PWI. The results indicate quite clearly that the two effects do not involve identical processing mechanisms based on the observed spatial dis- sociations in cortical activity across the two experiments. Although hypotheses from two rival accounts of distrac- tor interference in PWI were supported in each experi- ment, neither account is capable of providing a complete explanation. Rather, our data indicate that word distrac- tors may affect each level of processing proposed by the two accounts. The Distractor Frequency Effect Miozzo and Caramazza (2003) questioned the conventional LSC account of word production based on a series of in- vestigations of the distractor frequency effect. Subsequent studies confirmed their basic findings by controlling for the potentially confounding factor of AoA (Catling et al., 2010, Experiment 1; Dhooge & Hartsuiker, 2010, Experi- ment 1). The behavioral data from Experiment 1 replicated Catling et al.ʼs results, confirming the robustness of the ef- fect. In Experiment 1, the independent distractor frequency effect in PWI was associated with significant increases in activity in a series of cortical regions associated previously with articulatory motor and verbal self-monitoring mecha- nisms in spoken word production, including the medial (SMA/pre-SMA) and lateral premotor cortex and left pSTG (e.g., Peeva et al., 2010; Tremblay & Gracco, 2009; Alario et al., 2006; Indefrey & Levelt, 2004). The premotor cortex activity, in particular, may be considered consistent with the output hypothesis, as it attributes the locus of the effect to a postlexical articulatory response buffer (Mahon et al., 2007). The activation observed in primary motor cortex is also consistent with Peeva et al.ʼs (2010) recent pro- posal that projections from medial and lateral premotor cortices to primary motor cortex transform sublexical rep- resentations into a set of motor commands to the speech articulators. It is difficult to reconcile the premotor cortex result with an input account. Indefrey and Leveltʼs (2004) meta- analysis of the time course of spoken word production indicated premotor cortex activity occurs subsequent to lexical (lemma and word form) processing in middle and posterior temporal cortical areas. Nor do the results appear to support Roelofsʼ (2005) modified LSC mech- anism implemented in WEAVER++. In Roelofsʼ (2005) WEAVER++ account, a selective attention mechanism re- actively blocks distractor processing, favoring the produc- tion of the target name. The speed of blocking depends on the speed with which the distractor information be- comes available. As HF words are accessed first (e.g., Steyvers & Tenenbaum, 2005), they are first to be blocked. Roelofs, Piai, and Schriefers (2011) were able to demon- strate that WEAVER++ accounts for the distractor fre- quency effect and the results of Dhooge and Hartsuikerʼs (2010) masking and SOA manipulations. However, the WEAVER++ model still entails the assumption that the activation levels of the lexical nodes of LF words are higher once processing of HF words is reactively blocked, leading to a prediction of increased activity in middle and poste- rior MTG/STG. Roelofs (2008) associated the attentional control mechanism in WEAVER++ with the anterior cingu- late and IFG. The former region showed increased activity as part of a larger cluster involving premotor and sensori- motor regions, although this activity was located in the cingulate motor area, indicating a more likely motor role. The left IFG/Brocaʼs area did not show significant activity, either at the whole-brain level or in an ROI analysis. As Dhooge and Hartsuiker (2010) noted, the output ac- count does not specify the nature of the control mecha- nisms operating on the articulatory response buffer. They proposed that this role is performed by the verbal self- monitoring system. The finding of increased activity in the pSTG is consistent with this proposal, given evidence linking verbal self-monitoring to this region (Price, 2010; Zheng et al., 2010; Indefrey & Levelt, 2004). Precisely how the self-monitoring system might exclude responses from the articulatory buffer remains to be specified (see Roelofs et al., 2011). Another issue requiring specification relates to the role in PWI played by SMA/pre-SMA, regions sensitive to distractor frequency. The Distractor AoA Effect Experiment 2 replicated the independent distractor AoA effect reported by Catling et al. (2010; Experiment 2), indi- cating it is a robust effect. The direction of the AoA effect (larger interference/activation for late vs. early words) is in the opposite direction to that predicted by an LSC ac- count operating under a typical assumption of lower rest- ing activation for late-acquired words (e.g., Meschyan & Hernandez, 2002). However, network modeling studies indicate that early-acquired words are accessed relatively de Zubicaray et al. 491 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 4 / 2 2 4 4 / 8 2 2 / 1 4 9 8 4 2 3 / 2 1 6 7 5 7 o 7 c 4 n 5 _ 8 a / _ j 0 o 0 c 1 n 4 1 _ a p _ d 0 0 b 1 y 4 g 1 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j . t f / u s e r o n 1 7 M a y 2 0 2 1 faster because they have more central, highly connected nodes (Steyvers & Tenenbaum, 2005), possibly as a result of network structuring through development (e.g., Menenti & Burani, 2007; Lambon Ralph & Ehsan, 2006; Ellis & Lambon Ralph, 2000). The AoA effect was associated in our experiment with increases in activity in left mid-MTG/STG and pMTG/STG, in addition to the anterior cingulate. The former regions are related to lexical level processes, in- cluding lemma and phonological word form retrieval (e.g., Acheson et al., 2011; Peeva et al., 2010; Indefrey & Levelt, 2004), supporting an input account in which repre- sentations of early-acquired words are accessed first (e.g., Lambon Ralph & Ehsan, 2006; Steyvers & Tenenbaum, 2005). Of note, these regions have demonstrated increased activity in categorically related compared with unrelated distractor conditions in fMRI studies of SI effects (e.g., de Zubicaray et al., 2001, 2006). Thus, these findings converge with the interaction between AoA and SI observed by Belke et al. (2005), a result interpreted by them as indi- cating independent AoA effects occur at a lexical (lemma) level of processing. The ACC activity for the independent distractor AoA effect is consistent with that observed previously for the SI effect in PWI (e.g., de Zubicaray et al., 2001, 2006). This activity was rostral to the cingulate motor area activity observed for the distractor frequency effect. A prevailing account of ACC activity during interference tasks is that it reflects a general competition or conflict monitoring mechanism (e.g., Botvinick, Cohen, & Carter, 2004). On the basis of an assumption of competition among rival lexical nodes for production, a “competition detector” could monitor the total amount of activation of items at a given level of processing and respond with an error signal if a particular threshold is exceeded (Hartsuiker, 2006). This type of monitor was proposed by de Zubicaray and colleagues (2001, 2006) based upon results from their fMRI studies of SI effects, and appended to Harleyʼs (1993) connectionist computational model in a successful simula- tion of the SI effect (Hockey, Wiles, & de Zubicaray, 2005). Other neuroimaging studies have reached similar conclu- sions concerning the monitoring role of ACC in speech production (e.g., Riès, Janssen, Dufau, Alario, & Burle, 2011; Christoffels, Formisano, & Schiller, 2007). Alterna- tively, the activity might also be considered consistent with Roelofsʼ (2008) LSC account in which ACC performs a role in attentional control during speech production. The ad- ditional activity observed in the left inferior parietal lobule appears consistent with some role in distractor process- ing, given the extensive research linking this region with attention and reading (see Shaywitz & Shaywitz, 2008) and its increased response in the distractor versus neutral condition. Implications for Input and Output Accounts The contrasting patterns of activity observed for the inde- pendent distractor frequency and AoA effects indicate that neither input nor output accounts provide complete ex- planations of these PWI effects. A number of recent studies have challenged the reliability and generality of results cited in support of the latter account (e.g., Janssen, Schirm, Mahon, & Caramazza, 2008; cf. Mädebach, Oppermann, Hantsch, Curda, & Jescheniak, 2011; Piai, Roelofs, & Schriefers, 2011; also Mahon et al., 2007; cf. Lee & de Zubicaray, 2010). However, an output account clearly provides a better explanation of the premotor activity as- sociated with the independent distractor frequency effect than an input account, while the latter provides a better account of the middle to posterior MTG/STG activity ob- served in conjunction with the distractor AoA and SI ef- fects. A way to reconcile our data to these accounts is to propose that word distractors may affect the processes implicated by each account. The results of Experiment 1 converge with other find- ings from neuroimaging studies indicating that distractor effects may occur at multiple levels of information pro- cessing in word production. The locus of the SI effect in the postcue naming task has been variously attributed to prelexical, lexical, and postlexical mechanisms (Mahon et al., 2007; Dean, Bub, & Masson, 2001; Humphreys, Lloyd-Jones, & Fias, 1995). In that task, participants view two differently colored superimposed pictures, naming the target according to a subsequently presented color cue. When the distractor and target pictures are categori- cally related, naming latencies are slower than when they are unrelated. Dean et al. (2001) proposed that this in- terference occurs when demands on the integration of perceptual attributes (color) and conceptual representa- tions are high because of feature overlap among category exemplars. Recent fMRI evidence supports this proposal (Hocking, McMahon, & de Zubicaray, 2010). AoA Effects in Target Picture Names Although main effects of target picture AoA were observed in the naming latencies in both experiments, target pic- ture AoA did not interact with the independent distractor frequency or AoA effects, replicating the results of Catling et al. (2010). Nor did target picture AoA produce signifi- cant changes in fMRI activity. The absence of an interaction is consistent with research indicating different processing mechanisms for AoA effects according to task (Catling & Johnston, 2009; Brysbaert & Ghyselinck, 2006). However, in an fMRI study of covert picture naming, Ellis, Burani, Izura, Bromiley, and Venneri (2006) reported increased activation for pictures with late-acquired names in the left middle occipital and fusiform gyri, a result they in- terpreted as reflecting mapping of visual onto semantic representations. To investigate target picture AoA effects more directly in our data, we performed a post hoc analysis involving a direct t contrast of early and late target pictures in the neutral (i.e., no distractor) condition, combining data from participants across both experiments (n = 31). This 492 Journal of Cognitive Neuroscience Volume 24, Number 2 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 4 / 2 2 4 4 / 8 2 2 / 1 4 9 8 4 2 3 / 2 1 6 7 5 7 o 7 c 4 n 5 _ 8 a / _ j 0 o 0 c 1 n 4 1 _ a p _ d 0 0 b 1 y 4 g 1 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j f / . t u s e r o n 1 7 M a y 2 0 2 1 analysis likewise failed to reveal any significant activity. As Ellis et al. (2006) employed a blocked experimental design, it is possible that their findings represent the strategic operation of a cognitive search mechanism like that envisaged by Steyvers and Tenenbaum (2005). Alter- natively, an absence of differential activity might also be considered consistent with the architecture of neural net- work models (e.g., Lambon Ralph & Ehsan, 2006; Zevin & Seidenberg, 2002). According to these models, AoA effects in picture naming are because of the arbitrary map- pings between objects and their names (i.e., there is no systematic relationship between semantic input and pho- nological output representations). Although these effects emerge in networks with distributed random representa- tions, they do not emerge in networks with localist ones (Zevin & Seidenberg, 2002). Hence, one would not neces- sarily expect to observe localized activity attributable to a specific level of processing in an fMRI experiment. This dif- fers from explanations that attribute AoA effects with word distractors to a lexical semantic level of processing (e.g., Belke et al., 2005; see also Brysbaert & Ghyselinck, 2006). Summary and Conclusions The two fMRI experiments reported here each provides some support for input and output accounts of the dis- tractor frequency and AoA effects, respectively. However, given the different patterns of activity observed, neither account is able to provide a complete explanation. The fMRI data show the former effect engages regions involved in postlexical processes, including articulation and verbal self-monitoring, while the latter effect engages regions linked with lexical level processes. These interpretations should, nevertheless, be considered within the limitations of inference afforded by neuroimaging, as fMRI data can- not provide direct evidence of relationships between pro- cesses and processors in speech production. Acknowledgments We thank Jonathan Catling for providing the distractor stimuli for Experiments 1 and 2. This research was supported by Australian Research Council (ARC) Discovery Project grant DP1092619. Greig de Zubicaray was supported by ARC Future Fellowship FT0991634. Niels O. Schiller was supported by the Netherlands Institute for Advanced Study in the Humanities and Social Sci- ences as a Fellow-in-Residence 2010/2011. Michele Miozzo was supported by NIH grant DC006242. Reprint requests should be sent to Greig I. de Zubicaray, School of Psychology, University of Queensland, Brisbane, QLD 4072, Australia, or via e-mail: greig.dezubicaray@uq.edu.au. REFERENCES Acheson, D. J., Hamidi, M., Binder, J. R., & Postle, B. R. (2011). A common neural substrate for language production and verbal working memory. Journal of Cognitive Neuroscience, 23, 1358–1367. Alario, F. X., Chainay, H., Lehericy, S., & Cohen, L. (2006). The role of the supplementary motor area (SMA) in word production. 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