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Neuropsychologia 48 (2010) 2147–2157

Contents lists available at ScienceDirect

Neuropsychologia

journa l homepage: www.e lsev ier .com/ locate /neuropsychologia

ocalizing the Frequency × Regularity word readingnteraction in the cerebral cortex

acqueline Cumminea,∗, Gordon E. Sartyb, Ron Borowskyb

Department of Speech Pathology and Audiology, University of Alberta, CanadaDepartment of Psychology, University of Saskatchewan, 9 Campus Drive, Saskatoon, SK S7N 5A5, Canada

r t i c l e i n f o

rticle history:eceived 17 November 2009eceived in revised form 5 March 2010ccepted 1 April 2010vailable online 10 April 2010

eywords:dditive Factors Methodpatial localizationemporal localizationunctional magnetic resonance imaging

a b s t r a c t

The aim of this work is to combine behavioural and functional magnetic resonance imaging (fMRI) datato advance our knowledge of where the Frequency × Regularity interaction on word naming is locatedin the cerebral cortex. Participants named high and low frequency, regular and exception words in abehavioural lab (Experiment 1) and during an fMRI study (Experiment 2). We used the Additive FactorsMethod (AFM) to localize the expected overadditive Frequency × Regularity interaction both temporally,through word naming reaction times (whereby low frequency exceptions produce the longest reactiontimes), and spatially on the cortex, through hemodynamic response measures from fMRI (whereby lowfrequency exceptions produce the highest activation intensities). Activation maps revealed significantactivation for low frequency exception words in the supplementary motor association cortex (SMA). Weinterpret the SMA activation as increased articulatory preparation, given previous demonstrations of

amingupplementary motor association cortex

the SMA’s involvement in motor programming. Hemodynamic time courses were extracted from fourregions of interest: the middle temporal gyri, SMA, insula and the inferior frontal gyri. Importantly,hemodynamic intensities within the SMA displayed an overadditive interaction pattern parallel to thatfound with naming reaction times. Thus, we provide an application of the AFM to fMRI intensity measuresand evidence that the SMA is a potential cortical source of the Frequency × Regularity interaction during abasic naming paradigm. While the AFM has traditionally been used to localize factors in time we provide

usefu

evidence that the AFM is

. Introduction

Donders’ subtractive method provided one of the earliest meth-ds for studying the chronometry of perceptual and cognitiverocesses (Donders, 1969). By subtracting the reaction time to aask (e.g., identification of a flash of light) from reaction time to aask that involves one additional inserted process (e.g., identifica-ion of the colour of a flash of light), the processing time that it takesor the inserted process could be measured. However, the assump-ion that the additional inserted process was the only change toverall processing, known as the assumption of “pure insertion”,as considered to be problematic (Sternberg, 1969). For example,

ifferent decision processes and/or processing streams may also be

nvolved with the additional task requirements. Nonetheless, theubtractive method is still pervasive in the design of neuroimagingxperiments (see Culham, 2006 for a review).

∗ Corresponding author at: Department of Speech Pathology and Audiology, Fac-lty of Rehabilitation Medicine, University of Alberta, 8205 114St, 3-58A Corbettall, Edmonton, AB T6G 2G4, Canada. Tel.: +1 780 492 3965; fax: +1 780 492 9333.

E-mail address: jcummine@ualberta.ca (J. Cummine).

028-3932/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.oi:10.1016/j.neuropsychologia.2010.04.006

l in understanding how variables influence one another in the brain.© 2010 Elsevier Ltd. All rights reserved.

The introduction of Sternberg’s (1969) additive factors method(AFM) provided a new way of looking at reaction times in cognitiveexperiments. This method has been useful in examining stages orunderlying sub-systems of processing and how factors affect a com-mon stage versus separable stages of processing in basic readingtasks (Borowsky & Besner, 1993; Borowsky & Besner, 2006). How-ever, limited research has been conducted which applies the AFMto measures other than reaction time (see Miller & Hackley, 1992;Schweickert, 1989, 1985 for an application of the AFM to lateralizedreadiness potentials and accuracy, respectively). More specifically,while the AFM has advanced our understanding of when experi-mentally manipulated variables (or factors) influence one anothertemporally in terms of stages of processing in time, it remains tobe seen whether such a method can be useful in our understand-ing of where factors influence one another spatially in terms ofthe cortex. Research that supports the utility of analyzing men-tal activity with behavioural methodology comes from studies that

have demonstrated that a relationship exists between behaviouraland neuroanatomical measures. For example, past research hascorrelated onset measures (e.g., stimulus presentation) with BloodOxygenated Level Dependent (BOLD) activity onset (e.g., inflectionpoint from baseline; Menon, Luknowsky, & Gati, 1998; Richter,

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148 J. Cummine et al. / Neurops

ndersen, Georgopoulos, & Kim, 1997) and several studies haveorrelated the behavioural processing time (e.g., reaction time)ith aspects of the BOLD function (Cummine, Borowsky, Vakorin,ird & Sarty, 2008; Liu, Liao, Fang, Chu, & Tan, 2004; Menon et al.,998; Richter et al., 1997). We explore an application of the AFM tooth temporal stages of processing through reaction time and thenderlying cortical regions through functional Magnetic Resonance

maging (fMRI) BOLD parameters.

.1. Basic reading processes: frequency and regularity

Within research on basic reading processes, two factors that areommonly found to interact in basic naming paradigms are Fre-uency and Regularity (Hino & Lupker, 2000; Lupker, Brown, &olombo, 1997; Visser & Besner, 2001). The Frequency × Regularity

nteraction is the finding that low frequency exception wordsEXCs; e.g., sieve) are named significantly slower than low fre-uency regular words (REGs; e.g., swell), whereas higher frequencyXCs and REGs are named relatively faster and do not demonstratehe same magnitude of effect (Besner & Smith, 1992; Lupker etl., 1997; Paap & Noel, 1991). Similarly, this interaction can beescribed such that EXCs are slower than REGs, and the EXC’srequency effect is larger than for REGs (an “overadditive” inter-ction). Dual route theorists explain the Frequency × Regularityffect in terms of the difference in processing speeds between thehole-word processing system and the sub-word processing sys-

em (Besner & Smith, 1992; Paap & Noel, 1991). Specifically, it isrgued that high frequency EXCs can be processed fast enough viahe whole word route so that naming can occur before the sub-wordoute can create a competing and incorrect phonological code. Lowrequency EXCs, on the other hand, take longer to process and thushe sub-word route can produce a competing phonological repre-entation that must ultimately be resolved with the whole wordronunciation prior to speech output.1

There is a dearth of research on the Frequency × Regularitynteraction in terms of an examination of the AFM and the sub-ystems involved in basic reading processes. In general, the AFMuggests that an additive pattern of reaction times is evidenceor the factors affecting different stages of processing (Roberts

Sternberg, 1993; Sternberg, 1969; note that the AFM does notequire discrete and fully separate stages of processing, as it haseen shown to be applicable to different processes that are inascade, Miller, 1988). Specifically, if Frequency and Regularity

1 Other theoretical accounts by single mechanism theorists do not make aistinction between whole word and sub-word routes to account for the Fre-uency × Regularity interaction (Harm & Seidenberg, 2004; Plaut, 1999). Instead,hese theorists treat spelling–sound correspondences as a continuum whereEGs and EXCs represent different points on this continuum. However, theyo suggest that there are distributed representations of orthographic, phono-

ogical and semantic knowledge and that it is the lower weights on theonnections between these units that produce the slower reaction time to lowrequency EXCs in the typical Frequency × Regularity interaction. In other words,here is a “division of labor” between an orthography–phonology path and anrthography–semantics–phonology path whereby low frequency EXCs would relyore heavily on the slower, semantically mediated path (Harm & Seidenberg, 2004).

ome connectionist models employ a sigmoid input–output function where, pre-umably, the input levels of Frequency and Regularity to one of the systems (e.g.,he phonological units) might serve to produce an output which parallels the typi-al Frequency × Regularity interaction found on basic naming reaction times (Plaut,999; Plaut & Booth, 2000, but see Borowsky & Besner, 2006 for discussion of someroblems with this approach). Although the extent to which one subscribes to eitherdual route or connectionist account of basic reading processes influences the way

n which the Frequency × Regularity interaction is interpreted, it is clear that allodels of basic reading processes subscribe to the notion of separable subsystems.s such, the AFM helps explore the separability of subsystems, and its utility is notependent on the model one subscribes to and thus we argue that the AFM maye useful for all theoretical frameworks (see Besner & Borowsky, 2006; Borowsky &esner, 2006; Plaut and Booth, 2006a,b for further discussion of this issue).

ogia 48 (2010) 2147–2157

affect different stages of processing then the effect of one factorwill not depend on the levels of the other factor. For example, ifFrequency had effects at an earlier stage of processing than Reg-ularity, then activation in a subsequent system would be delayedby a constant amount of time, resulting in additivity of Frequencyand Regularity on reaction time. However, in the presence of anoveradditive pattern of reaction times, the factors being investi-gated are interpreted as affecting a common stage of processing.The overadditive Frequency × Regularity interaction on basic read-ing performance would suggest that Frequency and Regularity areaffecting a common stage of processing whereby the effect ofone factor influences the effect of another factor (see Borowsky& Besner, 1993; Borowsky & Besner, 2006; Yap & Balota, 2007 fora description of the effects of context, stimulus quality and wordfrequency). Combining the AFM with the previously outlined dualroute interpretation regarding the influences of Frequency and Reg-ularity, the interaction of these factors is located in a commonsub-system such as the articulatory buffer where the pronunci-ation codes are evaluated just prior to speech (see Fig. 1). Thus,incorporating the AFM with stages-of-processing interpretationsof the Frequency × Regularity interaction provides a more compre-hensive account of the underlying processes at work during basicreading than simple subtractive logic. We now consider the AFMand its applicability to understanding where factors influence eachother on the cortex using measures extracted from fMRI neuro-physiological data.

1.2. Functional imaging investigations of frequency andregularity

Several regions in the brain have been implicated as impor-tant cortical regions during word recognition paradigms. One suchregion includes the supplementary motor association cortex (SMA),which is consistently reported as active during tasks that manip-ulate Frequency and Regularity (Carreiras, Mechelli, Estevez, &Price, 2007; Fiez, Balota, Raichle, & Petersen, 1999; Saur et al.,2008; Vinckier et al., 2007). For example, Fiez et al. (1999) hadparticipants read aloud high and low frequency words and foundthat low frequency words produced activation in this region.Similarly, Carreiras et al. (2007) reported increased SMA activa-tion for basic word recognition during both naming and lexicaldecision tasks. Past imaging research has implicated the SMA asbeing involved in preparatory motor responses (Cavina-Pratesi,Valyear, Culham, Köhler, Obhi, Marzi & Goodale, 2006; Fiez et al.,1999; Möller, Jansma, Rodriguez-Fornells & Munte, 2007; Müller& Knight, 2006; Rauschecker, Pringle & Watkins, 2008; Richter etal., 1997), so this region is one candidate for where Frequency andRegularity might converge within the brain. Specifically, if the Fre-quency × Regularity interaction is produced because of competingphonological codes for low frequency EXCs, it is possible that theSMA is the region where such competition is resolved.

Notably, the Frequency × Regularity interaction is primarily aresult of low frequency EXCs and so regions involved in pro-cessing these stimuli are of primary interest. Past research hasimplicated the temporal lobes as a region responsible for theprocessing of EXCs (Dhanjal, Handunnetthi, Patel & Wise, 2008;Fiebach, Friederici, Muller, & von Cramon, 2002; Frost et al., 2005;Glasser & Rilling, 2008; Hauk, Davis & Pulvermuller, 2008; Indefrey& Levelt, 2004; Joubert et al., 2004; Simos, Breier, Fletcher, Foorman,Castillo & Papanicolaou, 2002; Simos, Pugh, Mencl, Frost, Fletcher,Sarkari & Papanicolaou, 2009; Spitsyna, Warren Scott, Turkheimer,

& Wise, 2006). For example, Frost et al. (2005) reported that activa-tion in the middle temporal gyrus was modulated by frequency(and imageability) and Fiez et al. (1999) reported that the lefthemisphere temporal region was more active when participantsread low frequency words as compared to high frequency words.

J. Cummine et al. / Neuropsychologia 48 (2010) 2147–2157 2149

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ig. 1. A depiction of how the dual-route model and AFM can be combined to provFM: Additive Factors Method; SW: Sub-word; WW: Whole-word.

imilarly, Joubert et al. (2004) found that the middle temporalyrus was highly active when participants silently read stimulihat required more phonological information, further supportinghe notion that this region plays a significant role in the process-ng of low frequency EXCs. As such we should see evidence of this

iddle temporal gyrus activity in our activation maps.Interestingly, Joubert et al. (2004) noted that the increased

emporal activation for low frequency stimuli corresponded toncreased activation in the inferior frontal gyrus, suggesting thenferior frontal gyrus may be sensitive to phonological information.he inferior frontal gyrus is continually reported as an active regionn word recognition research (Carreiras et al., 2007; Fiebach et al.,002; Fiez et al., 1999; Frost et al., 2005; Vinckier et al., 2007) andccordingly, is another important region of interest when lookingor the location of the interaction between Frequency and Regu-arity. For example, Fiebach et al. (2002) reported that the inferiorrontal gyrus was particularly sensitive to frequency during a lexi-al decision task. Carreiras et al. (2007) found frontal activation forords and nonwords during naming and lexical decision. Recentork by Vinckier et al. (2007) reported activation in the inferior

rontal gyri for word stimuli but not for false fonts or infrequentetters. Repeated findings of activation in this region during visual

ord recognition suggest that this region plays an important rolen basic reading and is another potential anatomical location whererequency and Regularity may influence one another in the brain.

Finally, the insular cortex is another region of interest thatas received a considerable amount of attention in basic readingtudies (Borowsky et al., 2006; Carreiras et al., 2007; Fiebach etl., 2002; Fiez et al., 1999; Frost et al., 2005; Posner & Raichle,994), and so it too is a structure where the Frequency × Regularity

nteraction may be located in the brain. The insula is particularlyensitive to low frequency words and several researchers haveuggested that this region plays an important role in the trans-ation of phonological information (Fiebach et al., 2002; Fiez etl., 1999). Consistent with these reports, some of our recent workBorowsky et al., 2006) found that the insular cortex was active

uring tasks that engage both lexical (posterior insula) and sub-

exical (anterior insula) phonological processing. Given accounts ofhe Frequency × Regularity interaction that emphasizes the addi-ional phonological processing that is required for low frequencyXCs, the insula is a potential region for localizing these factors.

ormation about the temporal location of Frequency and Regularity. Abbreviations:

1.3. Summary

Behavioural research on basic naming typically shows an over-additive Frequency × Regularity interaction for reaction time andthe AFM suggests that these two factors are affecting a com-mon stage of processing in time. However, the AFM has yet to beapplied to fMRI data in attempts to localize the Frequency andRegularity factors in the brain. Imaging research on basic wordrecognition utilizing naming tasks indicates that regions wheresuch factors may be coming together include the SMA, middle tem-poral gyrus, inferior frontal gyrus, and insular cortex. We reporthere the first fMRI naming study that explicitly looks for the Fre-quency × Regularity interaction in these four brain regions. We usethe AFM with BOLD measures to localize these two factors in thebrain.

2. Experiment 1

Although the naming task has optimal ecological validity in thatwe normally engage in naming words on a regular basis (Owenet al., 2004), it is not a particularly demanding task and thus maynot elicit a particularly strong hemodynamic response. We wantedto maximize sensitivity by using a blocked design instead of anevent related design (Culham, 2006; Cummine et al., 2008). Thus,an initial behavioural study was run to ensure that presenting thestimuli in a purely blocked fashion (as opposed to the mixed stimu-lus design that is typically employed in behavioural studies) wouldstill produce the expected results.

2.1. Materials and methods

2.1.1. ParticipantsUndergraduate students (N = 63) performed the naming tasks,

for credit in their Introductory Psychology course. Inclusion crite-

ria consisted of normal or corrected normal vision, and English as afirst language. The experiment was performed in compliance withthe relevant laws and institutional guidelines, and was approved bythe University of Saskatchewan Behavioural Sciences Ethics Com-mittee.

2 ychologia 48 (2010) 2147–2157

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.1.2. StimuliThe stimuli consisted of REGs (e.g., swell) and EXCs (e.g., sieve).2

here were 55 letter strings of each type for a total of 110 lettertrings (high frequency items = 54 and low frequency items = 56).timuli were matched for onset phoneme, length and word fre-uency (McDougall, Borowsky, MacKinnon, & Hymel, 2005).

.2. Materials

Stimuli were presented on a computer monitor usingPrime software (Psychology Software Tools, Inc., http://www.stnet.com). Voice onset was coded via a microphone and thexperimenter used a button response to code accuracy on each trial.

.2.1. ProcedureThe letter strings were presented in four pure blocks (high

requency REGs, low frequency REGs, high frequency EXCs, lowrequency EXCs). After giving written consent, participants werenstructed at the beginning of each block to read each letter stringloud as quickly and accurately as possible. Letter strings were pre-ented one at a time at the top of the computer screen. Participantsad 1800 ms to name the letter string. Naming made prior to 250 msr after 1800 ms was coded as an error. The presentation of the let-er string blocks was randomized to minimize any effects due toresentation order.

.3. Results

Only correct responses and trials in which reaction timesere >250 ms were included in the subsequent analyses.

2 (Frequency) × 2 (Regularity) repeated measures ANOVAas conducted. There was a significant effect of Frequency,

(1, 62) = 218.81, p < .001, where high frequency wordsmean = 551.59 ± 9.23 ms) were responded to faster than lowrequency words (mean = 602.53 ± 10.12 ms). There was a signif-cant effect of Regularity, F(1, 62) = 47.80, p < .001, where REGsmean = 559.16 ± 9.50 ms) were responded to faster than EXCsmean = 594.97 ± 10.25 ms). There was a significant overadditiventeraction between Frequency and Regularity, F(1, 62) = 44.74,< .001 (see Fig. 2). The effect of Regularity was significantly greater

or low frequency words with a difference of 57.59 ms betweenEGs and EXCs in comparison to high frequency words which onlyhowed a difference of 14.03 ms. Response times to high frequencyEGs (mean = 544.58 ± 9.53 ms) were significantly faster than high

requency EXCs (mean = 558.61 ± 9.91 ms), t(62) = 2.30, p = .025,nd low frequency REGs (mean = 573.74 ± 10.01 ms) were signifi-antly faster than low frequency EXCs (mean = 631.33 ± 11.13 ms),(62) = 9.40, p < .001 (uncorrected).

The results from this behavioural experiment are in line withrevious naming experiments that present the stimulus typesixed together (e.g., Besner & Smith, 1992; Hino & Lupker, 2000).

pecifically, when the stimuli are presented in a purely blocked for-at, we see a significant Frequency effect, a significant Regularity

ffect and most importantly, a significant overadditive interactionetween Frequency and Regularity. As such, we were satisfied that a

locked fMRI experiment would be an appropriate way to examinehe Frequency × Regularity interaction while allowing for maximalensitivity to evoke a BOLD response.

2 The stimuli were presented as part of a larger experiment which also includedure blocks of PHs (e.g., pynt) and NWs (e.g., bint). Importantly, the stimuli wereresented in concordance with a partial Latin square design to ensure no effects ofresentation order were present. In addition, there were no differences betweenhe stimuli on the following characteristics: imageabililty, bi-gram sum, length,honological neighbourhood or orthographic neighbourhood.

Fig. 2. Significant Frequency (high vs. low) × Regularity (REG vs. EXC) interactionon behavioural reaction time.

3. Experiment 2

3.1. Participants

Ten participants took part in the fMRI experiment. None hadparticipated in Experiment 1. Inclusion criteria and ethical approvalwere identical to the behavioural experiment.

3.2. Stimuli

The stimuli were identical to those used in Experiment 1.

3.3. Procedure and apparatus

After giving written consent, participants were familiarizedwith the MRI before the experiment. Participants were instructedto read each letter string aloud as quickly and accurately as possi-ble. A within-subjects design was used, and participants respondedvocally during a regular, periodic gap in the image acquisition(Borowsky et al., 2006; Borowsky et al., 2005). Specifically, follow-ing the offset of a volume of image acquisition, a letter string waspresented during a temporal gap in which the participant namedthe letter string aloud. Letter string presentation was counterbal-anced in accordance to a partial Latin square design and stimuliwere presented one at a time to the top-center portion of the pro-jection screen.

All imaging was conducted using a 1.5T Siemens Symphony MRI.For each experiment, 55 volumes of 12 slice axial single-shot echo-planar images (EPI) were obtained (TR = 3700 ms, with a 1850 msgap of no image acquisition at the end of each TR, TE = 55 ms,64 × 64 acquisition matrix, 128 × 128 reconstruction matrix). EPIslice thickness was 8 mm, with a 2 mm separation between slices.The first 5 volumes were used to achieve a steady state of imagecontrast and were discarded prior to analysis. The remaining vol-umes of images were organized into 5 blocks of 10 volumes each.

Each block consisted of 5 volumes of response followed by 5 vol-umes of rest. A computer running EPrime software was used totrigger each image acquisition in synchrony with the presentationof visual stimuli. Responses made during the 1850 ms gaps in imageacquisition and were monitored and measured (via a button press

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y the experimenter) over the MRI intercom as there was no noisenterference from the MRI during the gap. The stimuli were pre-ented using a data projector connected to the EPrime computernd a back-projection screen that was visible to the participanthrough a mirror attached to the head coil. In order to capture aull-cortex volume of images for each participant, either the fourthr fifth inferior-most slice was centered on the posterior commis-ure, depending on distance between the posterior commissurend the top of the brain for each participant. T1-weighted high-esolution spin-echo anatomical images (TR = 400 ms, TE = 12 ms,56 × 256 acquisition and reconstruction matrix) were acquired inxial, sagittal, and coronal planes for the purpose of overlaying thectivation maps. The position and thickness of the T1 axial imagesatched the echo-planar images.For optimal sensitivity, the experiment used a blocked design,

s described above, and was analyzed using the BOLDfold tech-ique (Sarty & Borowsky, 2005). This method of analysis requireshat sufficient time elapse between task conditions for the hemo-

ynamic response function (HRF) or BOLD response to fully returno baseline levels. After correcting for linear baseline drift, the

ean BOLD response for each voxel, over repetitions of the tasks empirically determined and a corresponding correlation coef-cient between the mean response and the actual response is

ig. 3. Average hemodynamic response function and graph of the plotted mean peak inteB) Middle Temporal Gyri, (C) Insula, and (D) Inferior Frontal Gyri.

gia 48 (2010) 2147–2157 2151

computed as a measure of BOLD response consistency across blockrepetitions. A correlation, �, of 0.60 or greater was used to defineactive voxels. The corresponding p-value for a correlation of 0.60is p < 0.05 when using a conservative Bonferroni-correction forapproximately 100,000 comparisons.

3.4. Unique versus shared activation maps

Unique and shared activation maps were computed for each par-ticipant as follows (see also Borowsky et al., 2006; Borowsky et al.,2005; Borowsky et al., 2007). Let C denote one of the four stimulustypes: high frequency REGs, low frequency REGs, high frequencyEXCs, low frequency EXCs. For each stimulus-type, C, and for eachsubject, two maps were computed, a threshold map �C(p) of � cor-relation values and a visibility or intensity map VC(p) (maximumaveraged BOLD response), where p is a voxel coordinate. The cor-responding activation map for C was defined as MC(p) = KC,�(p)VC(p)where KC,�(p) = 1 if �C(p) ≥ � and zero otherwise. As mentioned

above, we used a threshold of � = 0.60 for all our computations.This threshold value represents the minimal acceptable correlationbetween the raw BOLD response and its mean repeated across thefive blocks. Let A and B denote a pair of stimulus types (e.g., high fre-quency REGs and EXCs). Intersection maps (Mint) and unique maps

nsities (within the gray window) for: (A) Supplementary Motor Association cortex,

2152 J. Cummine et al. / Neuropsychologia 48 (2010) 2147–2157

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Muni) were computed for paired stimuli A and B for each subjectccording to:

uni(p) = [KA,�(p)VA(p) − KB,�(p)VB(p)][1 − KA,�(p)KB,�(p)] (1)

int(p) = KA,�(p)KB,�(p)(

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The unique map represents a difference (A∪B)\(A∩B) and showsask subtraction for activations that are not common to conditionsand B. In this computation, unique activation to A is represented

s positive and unique activation to B is represented as negative.he intersection map is an averaged map of A and B and representsn intersection A∩B showing activation shared to both conditionsand B. The unique and intersection maps were averaged across

articipants to produce the final maps, as described next.Using AFNI (Cox, 1996), unique and intersection map voxels sep-

rated by 1.1 mm distance were clustered and clusters of volumeess than 100 �L were clipped out. The data were then spatially

lurred using an isotropic Gaussian blur with a full width at halfaximum (FWHM) of 3.91 mm. The averaging of images across

ubjects was done after a standard piecewise affine transforma-ion of the individual blurred maps to a standardized brain atlasTalairach & Tournoux, 1988). Visual inspection of the individual

inued )

participant anatomical images did not reveal any structural abnor-malities that would compromise the averaging of data in Talairachspace. Mean activation maps in Talairach coordinates were deter-mined for each map type along with the corresponding one samplet statistic for each voxel mean. The maps that follow show regions ofactivation that surpass both the correlation threshold (� of 0.60) atthe individual level and a one-tailed t-test against zero at the grouplevel. Regions of activation on the resulting maps were deemed sig-nificant at t(9) = 1.833, p = 0.05. The activation levels are treated asbinary (i.e., significantly active or not) so as to avoid a the problemin traditional subtractive mapping whereby only the most intenseactivation earns a placement on the map even though the otherstimulus type may have also resulted in significant activation.

3.5. Regions of interest

To extract the relevant BOLD time course data, four bilateral

regions of interests (ROIs) were delineated for each participant.ROIs were drawn, using AFNI (Cox, 1996), in one to three slices(depending on the participant and the particular ROI) in the insularcortex, lateral inferior frontal gyri, SMA, and middle temporal gyrusof both the right and left hemisphere. Stimulate (Strupp, 1996)

J. Cummine et al. / Neuropsychologia 48 (2010) 2147–2157 2153

Table 1Mean peak intensities (greyscale units) as a function of Regularity, Frequency and region of interest.

Regular Words Exception Words

Region of interest High frequency Low frequency High frequency Low frequencyMTG 1.132 4.490 3.565 5.415Insula 4.265 5.625 1.200 4.040

15 3.085 5.36505 6.355 7.090

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as used to extract the ROI-averaged hemodynamic response timeourses for each participant, for each condition and in each ROI.nly time courses which were based on >5 active voxels, deemed

ignificant using BOLDfold and a threshold of � of 0.60, werencluded. The time courses depict intensity values, in grayscalenits (gs), across ten volumes. These were averaged across the fivelocks and across all participants to produce the overall averageemodynamic response for each of the four conditions in each ofhe four ROIs.

Importantly, past research indicates that the hemodynamicesponse function saturates after several stimulus presentations athich point any subsequent stimuli will just serve to widen the

unction (Menon & Kim, 1999). As a result, the falling edge of theemodynamic function is quite variable. We also know that theising edge of the hemodynamic response function is correlatedith stimulus onset so we focused on only those intensity valuesear the initial peak of the hemodynamic response function. Withinhe middle temporal gyri, insula and SMA most stimuli had an ini-ial peak by the third stimulus presentation (third TR), whereas theemodynamic response functions for each stimulus within the infe-ior frontal gyri displayed an initial peak closer to the fifth stimulusresentation. So, for the middle temporal gyri, insula and SMA welotted the average intensity at TR two and three (see grey bar onig. 3 and Table 1), for each condition. For the inferior frontal gyrie plotted the average intensity at TR four and five.

In keeping with past research, we then evaluated the extento which the pattern of mean intensities resembled that typi-ally found in behavioural reaction times (Fiez et al., 1999; Frostt al., 2005). A sample size much larger than is characteristicallyound in imaging studies would be required to produce a signif-cant Frequency × Regularity interaction; thus, we are aware that

repeated measures ANOVA on the intensity values would lackufficient power to detect an interaction. However, our primarynterest lies in the pattern of intensities produced and whether theyre similar to the significant Frequency × Regularity interactionroduced in our behavioural study. Ultimately, we know that ouraradigm is successful at producing the interaction of interest oneaction time (e.g., Experiment 1) and this provides a rationale forxploring the pattern of intensities produced under the same taskonditions.

. Results

.1. Behavioural response patterns

In line with Experiment 1, the behavioural response timesollected from the fMRI experiment displayed a similar Fre-uency × Regularity interactive pattern (see Fig. 4). That is,igh frequency words (mean = 904.04 ± 33.97 ms (standardrror)) were responded to faster than low frequency words

mean = 946.83 ± 30.97 ms) and REGs (mean = 912.06 ± 27.05 ms)ere responded to faster than EXCs (mean = 938.82 ± 39.47 ms).esponse times to high frequency REGs (mean = 896.7 ± 28.73 ms)ere faster than high frequency EXCs (mean = 911.39 ± 40.02 ms)

nd low frequency REGs (mean = 927.41 ± 30.23 ms) were faster

Fig. 4. Frequency (high vs. low) × Regularity (REG vs. EXC) interaction pattern onbehavioural response times (e.g., offset) collected from Experiment 2.

than low frequency EXCs (mean = 966.24 ± 40.02 ms). The effect ofRegularity was greater for low frequency words with a difference of38.82 ms between REGs and EXCs in comparison to high Frequencywords which only showed a difference of 14.69 ms.

4.2. High frequency REGs and high frequency EXCs

Shared activation between high frequency REGs and high fre-quency EXCs was found in the posterior LH temporal cortex, SMAand motor cortex (see Fig. 5A). Unique activation to high frequencyEXCs was also found in the middle temporal gyri, SMA, inferiorfrontal gyri and insular cortex (see Fig. 5B). Unique activation tohigh frequency REGs was found in the inferior temporal cortex andSMA.

4.3. Low frequency REGs and low frequency EXCs.

Shared activation between low frequency REGs and low fre-quency EXCs was found in the left hemisphere temporal cortex,motor cortex, insular cortex and SMA (Fig. 6A). Unique activationfor low frequency EXCs was found in the right hemisphere middletemporal gyrus and SMA (see Fig. 6B). Unique activation for lowfrequency REGs was found in the inferior temporal cortex.

4.4. Regions of interest

4.4.1. Supplementary motor association cortex (SMA).Low frequency EXCs displayed an overall higher intensity func-

tion, followed by low frequency REGs. High frequency EXCs andREGs produced similar intensity functions in this region. The inten-sity peak for each stimulus (average of TR two and three; see graywindow in Fig. 3A) were plotted. Overall, EXCs produced higherintensity functions compared to REGs and low frequency stimuli

2154 J. Cummine et al. / Neuropsychologia 48 (2010) 2147–2157

Fig. 5. (A) Shared activation between high frequency REGs and EXCs. Shared activation between REGs and EXCs is found in the lateral occipital gyri, precentral gyri, postcentralgyri, inferior frontal gyri, supplementary motor association cortex and superior temporal gyri. (B) Unique activation to high frequency REGs and high frequency EXCs. Uniqueactivation to REGs was found in the lateral occipital gyri, inferior temporal gyri, precentral gyri, superior frontal gyri, precuneus, and supplementary motor associationc oral gyp ontalp la cort

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ortex. Activation unique to EXCs was found in the right hemisphere inferior tempostcentral gyri, precentral gyri, inferior frontal gyri, middle frontal gyri, superior froint to right middle temporal gyri, supplementary association cortex and left insu

roduced higher intensity functions compared to high frequencytimuli. Notably, the pattern of mean intensities parallels the over-dditive interaction reported on behavioural naming responseimes from Experiment 1 and 2.

.4.2. Middle temporal gyriLow frequency EXCs displayed an overall higher intensity func-

ion than any of the other stimuli, followed by low frequencyEGs, high frequency EXCs and high frequency REGs. The inten-ity peak for each stimulus (average of TR two and three; see grayindow in Fig. 3B) were plotted. Overall, EXCs produced higher

ntensity functions compared to REGs and low frequency stimuliroduced higher intensity functions as compared to high frequencytimuli. The pattern of mean intensities is one of underadditiv-

ty

.4.3. Insular cortexLow frequency REG and EXCs produced similar intensities

ime courses, followed by high frequency REGs. High frequency

ig. 6. (A) Shared activation between low frequency REGs and EXCs. Shared activation betemporal gyri and superior parietal lobule, right hemisphere inferior parietal lobule, precB) Unique activation to low frequency REGs and low frequency EXCs. Unique activation forecentral gyri, middle frontal gyrus, anterior cingulate gyri and cerebellum. Unique acemporal gyrus, bilateral superior parietal lobule, precentral gyri, supplementary motoro right middle temporal gyrus, supplementary motor association cortex and left insular

ri, middle temporal gyri, superior temporal gyri, bilateral superior parietal lobule,gyri, supplementary motor association cortex, cingulate gyri, and midbrain. Arrowsex.

EXCs produced a very small intensity function in this region.The intensity peak for each stimulus (average of TR two andthree; see gray window in Fig. 3C) were plotted. Overall, REGsproduced higher intensity functions compared to EXCs and lowfrequency stimuli produced higher intensity functions comparedto high frequency stimuli. The pattern of mean intensities is one ofunderadditivity

4.4.4. Inferior frontal gyriLow frequency EXCs displayed an overall higher intensity func-

tion, followed by high frequency EXCs, low frequency REGs andthen high frequency REGs. The intensity peak for each stimulus(average of TR four and five; see gray window in Fig. 3D) were plot-

ted. Overall, EXCs produced higher intensity functions compared toREGs and low frequency stimuli produced somewhat higher inten-sity functions compared to high frequency stimuli. Notably, thepattern of mean intensities displays additivity between Frequencyand Regularity.

ween REGs and EXCs was found in the lateral occipital gyri, left hemisphere superiorentral gyri, postcentral gyri, supplementary motor association cortex and cuneus.r REGs was found in the right hemisphere inferior temporal gyrus, left hemisphere

tivation for EXCs was found in the lateral occipital gyri, right hemisphere middleassociation cortex, paracentral lobule, and posterior cingulate gyrus. Arrows pointcortex.

J. Cummine et al. / Neuropsycholo

Fig. 7. A depiction of how the dual-route model and AFM can be combined to provideinformation about the spatial location of Frequency and Regularity. Abbreviations:AFM: Additive Factors Method; MTG: Middle Temporal Gyri; SMA: SupplementaryMotor Association cortex; IFG: Inferior Frontal Gyri; WW: Whole Word; SW: Sub-Word.

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5.2.1. Middle temporal gyriAs we anticipated, our evaluation of the hemodynamic time-

course supports the notion that the middle temporal gyri issensitive to Frequency (Fiez et al., 1999) whereby low frequency

3 Although we have couched out interpretation in terms of the dual-route modelof basic reading processes, we note that some parallel distributed processing modelshave also including a sub-system for articulation (e.g., Seidenberg, Peterson, Plaut, &McDonald, 1996), and thus our results would be applicable to such models as well.

. Discussion

We provide a demonstration of the application of the AFM toMRI BOLD intensity to understand where certain factors that affectord naming, namely Frequency and Regularity, interact in the

rain. We found evidence that the SMA is a region where Frequencynd Regularity, for word naming, show an overadditive interactionn the cortex. Our unique and shared maps illustrate that the SMAs more active for low frequency words than for high frequency

ords, and more active for EXCs than for REGs. Consistent withast research, the SMA appears to be involved in the preparationf a motor response, and such preparation produces higher inten-ity BOLD responses for low frequency EXCs than for any othertimuli.

The AFM provides a useful alternative to the traditional Donders’ubtractive method for studying the chronometry of perceptualnd cognitive processes (Donders, 1969). The subtractive methods still pervasive in the design and analysis of neuroimaging exper-ments although it can be argued that the assumption of purensertion is suspect when subtracting a ‘baseline task’. To mini-

ize the problem of a ‘baseline’ task we plotted the mean intensityt a critical time point and evaluated the pattern of results. UsingFM to assess imaging data provides another method for studyingental chronometry and proved useful in furthering our under-

tanding of where Frequency and Regularity interact in the brainsee Fig. 7). On a similar note, our activation map computation alsovoided the drawbacks of subtractive logic, wherein we calculatednique and shared maps instead of differences maps. Thus, insteadf extracting information about which factor provides the mostntense activation in a particular region, our unique and shared

aps provide information about separable and common regionsctive for stimuli. As such, shared maps can serve to elucidateegions where factors potentially come together and regions thatre involved in a particular cognitive process (e.g., an underlyingub-system).

gia 48 (2010) 2147–2157 2155

5.1. Localizing the Frequency × Regularity interaction: the SMAas the articulatory buffer

To date, there has been minimal research on the Fre-quency × Regularity interaction in terms of an examination of theAFM and the sub-systems involved in basic reading processes. Inkeeping with past research (Fiez et al., 1999; Frost et al., 2005; Hino& Lupker, 2000; Lupker, Brown, & Colombo, 1997; Visser & Besner,2001), we provide evidence for the interaction of Frequency andRegularity on behavioural response time (Experiment 1 and Exper-iment 2) and BOLD intensity (Experiment 2). The AFM suggests thatin the presence of an overadditive pattern of reaction times, the fac-tors being investigated are interpreted as affecting a common stageof processing (Roberts & Sternberg, 1993; Sternberg, 1969). Thus,the overadditive Frequency × Regularity interaction on basic read-ing response times and BOLD intensities indicates that Frequencyand Regularity affect a common stage of processing. Combining theAFM with the previously outlined dual route interpretation regard-ing the influences of Frequency and Regularity, the interaction ofthese factors is located in a common sub-system such as the articu-latory buffer where the pronunciation codes are evaluated just priorto speech.3 Thus, we localized this interaction within a cognitiveframework to an articulatory sub-system and within a functionalframework to the SMA (see Figs. 1 and 7). Overall, applying theAFM to behavioural response times and BOLD intensities providesa more comprehensive account of the underlying processes at workduring basic reading than simple subtractive logic.

Most importantly, the peak intensities of significant activationfor each stimulus type displayed an overadditive interaction pat-tern within the SMA, which parallels the pattern typically reportedfor naming reaction times. Interpreting these findings within theframework of the AFM suggests that the factors of Frequency andRegularity influence one another within the SMA. While there areno known reports of the application of the AFM to interactiveeffects in imaging data during reading aloud, there are a few studiesthat have plotted the pattern of mean intensities from the hemo-dynamic response function (Fiez et al., 1999; Frost et al., 2005).However, there is a crucial methodological difference between theprevious reports and our results that makes comparison among thestudies impractical. Specifically, we focused on only those intensityvalues near the initial peak of the hemodynamic response function,unlike previous studies. This decision was based on past researchthat indicates the hemodynamic response function saturates afterseveral stimulus presentations, leading to a variable falling edgeand that the rising edge of the hemodynamic response functionis correlated with stimulus onset (Menon & Kim, 1999). Focus-ing on values near the initial peak potentially serves to minimizenoise that can be averaged in when including all points on thehemodynamic function. With that said, AFM is a useful alterna-tive to subtractive logic as more researchers begin to explore howwe can extract and interpret information from the hemodynamictimecourse.

5.2. Effects of frequency and regularity on activation in regions ofinterest

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ords produced more intense activation than high frequencyords and, to a lesser degree, Regularity (Frost et al., 2005) whereby

XCs produced more intense activation than REGs. Furthermore,ur activation maps show that low frequency EXCs primarily acti-ate the right middle temporal gyrus. Other research has shownhat the right middle temporal gyrus is particularly active dur-ng prosody tasks (e.g., detecting the rise in pitch at the end of aentence, which indicates a question is being asked; see Glasser

Rilling, 2008). It is possible that similar processing demandsre required for tasks involving the detection and production ofroper prosody and naming low frequency EXCs. More specifically,rosody involves the manipulation and modification of phone-ic pronunciations in order to transmit meaning. Similarly, EXCs

re highly reliant on the particular pronunciation of the ortho-raphic letter pattern if the correct pronunciation and subsequentord meaning is to be understood. That is, a correct whole wordhonological representation must be activated for the correct pro-unciation to be produced, and the subsequent meaning of theord to be transmitted. Our results suggest that low frequency

XCs rely on this process to a greater degree than any of the othertimulus types.

.2.2. InsulaConsistent with past reports, an assessment of the hemody-

amic timecourse in this region suggests the insula is particularlyensitive to Regularity whereby REGs produced more intense acti-ation than EXCs, and Frequency, whereby low frequency wordsroduced more intense activation than high frequency words.otably, this is the only region in which we found REGs to pro-uce more intense activation than EXCs. However, this is in lineith previous notions about the insula’s involvement in processing

amiliar stimuli (Posner & Raichle, 1994). Some of our recent workBorowsky et al., 2006) found that the insular cortex was activeuring tasks that engage both whole- and sub-word phonologicalrocessing. Though there are no differences in frequency betweenhe REGs and EXCs in our study, REG naming involves the activationf familiar whole word and sub-word representations, whereas EXCaming only involves activation of familiar whole word represen-ations. Unlike other brain regions, the insula has been reported asroducing more activation in response to familiar stimuli.

Notably, the pattern of intensities from both the middle tem-oral gyri and the insula displayed interaction patterns that wereot overadditive. While alternative interactions were not the focusf our present experiments, there are several potential expla-ations for these findings that could be clarified through futureork. One possible explanation is that Frequency and Regularity

nfluence two independent processes and/or sub-systems. If pro-essing in these sub-systems takes part in parallel and must beompleted before the next stage can begin, the effects on the mea-urement of interest (e.g., reaction time, BOLD intensity) would ben interaction (Sternberg, 1969). In this case, factors that influencehe sub-systems separately would interact negatively (Sternberg,969). Future work that manipulates several factors known to influ-nce different sub-systems (e.g., stimulus quality, which could bessumed to influence early orthographic encoding, and semanticriming, which could be assumed to influence semantic process-

ng, see Borowsky & Besner, 1993; Borowsky & Besner, 2006) woulderve to clarify the nature of the underadditivity found in the middleemporal gyri and insula.

.2.3. Inferior frontal gyri

In line with previous reports that the inferior frontal gyri play

n important role during visual word recognition, we find acti-ation in this region for all stimuli. However, an evaluation ofhe BOLD timecourses demonstrates that EXCs produce markedlyigher intensities in this region in comparison to REGs for both

ogia 48 (2010) 2147–2157

low and high frequency stimuli. Accordingly, the inferior frontalgyri appear to be particularly sensitive to Regularity. Although thisregion was not the locus of the Frequency × Regularity interaction,the pattern of intensities in the inferior frontal gyri is one of addi-tivity. Using the AFM, this pattern of intensities indicates that theinferior frontal gyri are not a common region influenced by Fre-quency and Regularity but instead represent a region where theeffect of Regularity is likely preceded by some effect of Frequency.As such, the effect of Frequency does not depend on the levels ofRegularity in this region (and vice versus) but only serves to con-tribute a constant component to the intensities. The inferior frontalgyri are continually reported as an active region in word recogni-tion research (Carreiras et al., 2007; Fiebach et al., 2002; Fiez et al.,1999; Frost et al., 2005; Vinckier et al., 2007) and consequently playa significant role in the basic naming task

Our results also reveal a small dissociation between Frequencyand Regularity and their effects on intensity in different regions.More specifically, we found that Frequency displays a large effecton intensity at earlier processing systems within the middle tem-poral gyrus (mean difference between high and low stimuli = 2.604greyscale (gs)), whereas the effect of Regularity in this region issmaller (mean difference between EXCs and REGs = 1.679 gs). Incontrast, the effect of Regularity on intensity is large within laterprocessing regions such as the inferior frontal gyri (mean differencebetween EXCs and REGs = 2.36 gs), whereas the effect of Frequencyin this region is much smaller (mean difference between high andlow frequency stimuli = 0.908 gs). This dissociation can be under-stood if we combine what is assumed within the behavioural-baseddual route model of basic reading (Besner & Smith, 1992) with whatis evidenced from the fMRI based dual route model of basic reading(see Figs. 1 and 7; Borowsky et al., 2006; Fiebach et al., 2002). Thatis, the cognitive dual route model suggests that the whole wordroute is sensitive to Frequency whereas the sub-word route is sen-sitive to Regularity. The fMRI dual route model suggests that wholeword processing involves a ventral route in the brain whereas sub-word processing involves a dorsal route in the brain (Borowskyet al., 2006; Cohen, Dehaene, Vinckier, Jobert, & Montavont, 2008;Fiebach et al., 2002). Taken together, it is not surprising that themiddle temporal gyrus in the ventral stream is sensitive to Fre-quency whereas the inferior frontal gyri in the dorsal stream issensitive to Regularity.

Finally, our findings underscore the importance of evaluatingthe time course peaks differentially as a function of stimulus typeand region. It was evident from our results that the hemodynamicresponse function in the middle temporal gyrus, insula and SMAhad initially peaked by the third stimulus presentation. In contrast,the average initial peak in the inferior frontal gyri was later thanthe other three regions and typically peaked around the fourth orfifth stimulus presentation. If we had focused on the average inten-sity for each stimulus, in each region of interest, we would havebeen left with an intensity value that reflected several possiblestages of processing including initial stimulus processing, mainte-nance of stimulus processing and the subsequent return to baselineof the hemodynamic response function. Thus, we would have lostessential information about how factors influence one another inparticular regions. Evaluating particular points of the hemody-namic response function allowed us to avoid possible confoundingof multiple processing components/stages.

6. Conclusion

We provide an application of the AFM to fMRI intensity mea-sures to explore subsystems within whole word and sub-wordreading processes. We found that the SMA is critically involvedin the Frequency × Regularity interaction in word naming, and is

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region in the brain where Frequency and Regularity influencene another, and could very well be the location of the articulatoryuffer that is referred to in many models of basic reading processes.lthough the AFM has traditionally been used to localize factors in

ime, we provide evidence that the AFM is useful in understandingow variables influence one another in the brain.

eferences

esner, D., & Smith, M. C. (1992). Models of visual word recognition: When obscuringthe stimulus yields a clearer view. Journal of Experimental Psychology: Learning,Memory, and Cognition, 18, 468–482.

orowsky, R., & Besner, D. (1993). Visual word recognition: A multistage activationmodel. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19,813–840.

orowsky, R., & Besner, D. (2006). Parallel distributed processing and lexical-semantic effects in visual word recognition: Are a few stages necessary?Psychological Review, 113, 181–195.

orowsky, R., Cummine, J., Owen, W. J., Friesen, C. K., Shih, F., & Sarty, G. E. (2006).fMRI of ventral and dorsal processing streams in basic reading processes: Insularsensitivity to phonology. Brain Topography, 18, 233–239.

orowsky, R., Esopenko, C., Cummine, J., & Sarty, G. E. (2007). Neural representationsof visual words and objects: A functional MRI study on the modularity of readingand object processing. Brain Topography, 20, 89–96.

orowsky, R., Owen, W. J., Wile, T. A., Friesen, C. K., Martin, J. L., & Sarty, G. E. (2005).Neuroimaging of language processes: fMRI of silent and overt lexical processingand the promise of multiple process imaging in single brain studies. CanadianAssociation Radiology Journal, 56, 204–213.

arreiras, M., Mechelli, A., Estevez, A., & Price, C. J. (2007). Brain activation for lexicaldecision and reading aloud: Two sides of the same coin? Journal of CognitiveNeuroscience, 19, 433–444.

avina-Pratesi, C., Valyear, K. F., Culham, J. C., Köhler, S., Obhi, S. S., Marzi, C. A., etal. (2006). Dissociating arbitrary stimulus–response mapping from movementplanning during preparatory period: Evidence from event-related functionalmagnetic resonance imaging. The Journal of Neuroscience, 26, 2704–2713.

ohen, L., Dehaene, S., Vinckier, F., Jobert, A., & Montavont, A. (2008). Reading normaland degraded words: Contribution of the dorsal and ventral visual pathways.NeuroImage, 40, 353–356.

ox, R. W. (1996). AFNI: Software for analysis and visualization offunctional magnetic resonance neuroimages. Computers and Biomed-ical Research, 29,162–173. [AFNI 3-d anatomical brain available at:http://afni.nimh.nih.gov/old/afni/astrip + orig.HEAD (and BRIK)].

ulham, J. C. (2006). In R. Cabeza, & Kingstone (Eds.), A handbook of functional neu-roimaging of cognition (second ed., pp. 53–82). London, England: The MIT Press.

ummine, J., Borowsky, R., Vakorin, V., Bird, J., & Sarty, G. (2008). The relationshipbetween naming reaction time and functional MRI parameters in Broca’s area.Magnetic Resonance Imaging, 26, 824–834.

hanjal, N. S., Handunnetthi, L., Patel, M. C., & Wise, R. J. S. (2008). Percep-tual systems controlling speech production. The Journal of Neuroscience, 28,9969–9975.

iebach, C. J., Friederici, A. D., Müller, K., & von Cramon, D. Y. (2002). fMRI evidence fordual routes to the mental lexicon in visual word recognition. Journal of CognitiveNeuroscience, 14, 11–23.

iez, J. A., Balota, D. A., Raichle, M. E., & Petersen, S. E. (1999). Effects of lexical-ity, frequency, and spelling-to-sound consistency on the functional anatomy ofreading. Neuron, 24, 205–218.

rost, S. J., Mencl, W. E., Sandak, R., Moore, D. L., Rueckl, J. G., Katz, L., et al. (2005). Afunctional magnetic resonance imagin study of the tradeoff between semanticsand phonology in reading aloud. NeuroReport, 16, 621–624.

lasser, M. F., & Rilling, J. K. (2008). DTI tractography of the human brain’s languagepathways. Cerebral Cortex, 18, 2471–2482.

arm, M. W., & Seidenberg, M. S. (2004). Computing the meanings of words in read-ing: Cooperative division of labor between visual and phonological processes.Psychological Review, 111, 662–720.

auk, O., Davis, M. H., & Pulvermüller, F. (2008). Modulation of brain activity by mul-tiple lexical and word form variables in visual word recognition: A parametricfMRI study. NeuroImage, 42, 1185–1195.

ino, Y., & Lupker, S. J. (2000). Effects of word frequency and spelling-to-soundregularity in naming with and without preceding lexical decision. Journal ofExperimental Psychology: Human Perception and Performance, 26, 166–183.

ndefrey, P., & Levelt, W. J. M. (2004). The spatial and temporal signatures of wordproduction components. Cognition, 92, 101–144.

oubert, S., Beauregard, M., Walter, N., Bourgouin, P., Beaudoin, G., Leroux, J., et al.(2004). Neural correlates of lexical and sublexical processes in reading. Brainand Language, 89, 9–20.

iu, H., Liao, W., Fang, S., Chu, T., & Tan, L. (2004). Correlation between temporalresponse of fMRI and fast reaction time a language task. Magnetic ResonanceImaging, 22, 451–455.

gia 48 (2010) 2147–2157 2157

Lupker, S. J., Brown, P., & Colombo, L. (1997). Strategic control in a naming task:Changing routes or changing deadlines? Journal of Experimental Psychology:Learning, Memory, and Cognition, 23, 570–590.

McDougall, P., Borowsky, R., MacKinnon, G. E., & Hymel, S. (2005). Process dissoci-ation of sight vocabulary and phonetic decoding in reading: A new perspectiveon surface and phonological dyslexias. Brain and Language, 92, 185–203.

Menon, R. S., & Kim, S. G. (1999). Spatial and temporal limits in cognitive neuroimag-ing with fMRI. Trends in Cognitive Sciences, 3, 207–216.

Menon, R., Luknowsky, D., & Gati, J. (1998). Mental chronometry using latency-resolved functional MRI. Proceedings of the National Academy of Sciences, 95,10902–10907.

Miller, J. (1988). Discrete and continuous models of human information pro-cessing: Theoretical distinctions and empirical results. Acta Psychologica, 67,191–257.

Miller, J., & Hackley, S. A. (1992). Electrophysiological evidence for temporal overlapamong contingent mental procesess. Journal of Experimental Psychology: General,121, 195–209.

Möller, J., Jansma, B. M., Rodriguez-Fornells, A., & Münte, T. F. (2007). What the braindoes before the tongue slips. Cerebral Cortex, 17, 1173–1178.

Müller, N. G., & Knight, R. T. (2006). The functional neuroanatomy of working mem-ory: Contributions of human brain lesion studies. Neuroscience, 139, 51–58.

Owen, W. J., Borowsky, R., & Sarty, G. E. (2004). FMRI of two measures of phonologicalprocessing in visual word recognition: Ecological validity matters. Brain & Lang,90, 40–46.

Paap, K. R., & Noel, R. W. (1991). Dual-route models of print to sound: Still a goodhorse race. Psychological Research, 53, 13–24.

Plaut, D. C. (1999). A connectionist approach to word reading and acquired dyslexia:Extention to sequential processing. Cognitive Science, 23, 543–568.

Plaut DC & Booth JR. (2006a). Individual and developmental differences in semanticpriming: Empirical and computational support for a single mechanism accountof lexical processing. Psych Rev, 107, 786-823.

Plaut DC & Booth JR. (2006b). More modeling but still no stages: Reply to Borowskyand Besner. Psych Rev, 113, 196–200.

Posner, M. I., & Raichle, M. E. (1994). Images of mind. New York: Scientific AmericanLibrary.

Rauschecker, A. M., Pringle, A., & Watkins, K. E. (2008). Changes in neural activityassociated with learning to articulate novel auditory pseudowords by covertrepetition. Human Brain Mapping, 29, 1231–1242.

Richter, W., Andersen, P. M., Georgopoulos, A. P., & Kim, S. G. (1997). Sequentialactivity in human motor areas during a delayed cued finger movement taskstudied by time-resolved fMRI. NeuroReport, 8, 1257–1261.

Roberts, S., & Sternberg, S. (1993). The meaning of additive reaction-time effects:Tests of three alternatives. Attention and Performance, 14, 611–653.

Sarty, G. E., & Borowsky, R. (2005). Functional MRI activation maps from empiricallydefined curve fitting. Magnetic Resonance Engineering, 24b, 46–55.

Saur, D., Kreher, B. W., Schnell, S., Kummerer, D., Kellmeyer, P., Vry, M. S., et al. (2008).Ventral and dorsal pathways for language. Proceedings of the National Academyof Science, 105, 18035–18040.

Schweickert, R. (1989). Separable effects of factors on activation functions in discreteand continuous models: d’ and evoked potentials. Psychological Bulletin, 106,318–328.

Schweickert, R. (1985). Separable effects of factors on speed and accuracy: Memoryscanning, lexical decision, and choice tasks. Psychological Bulletin, 97, 530–546.

Seidenberg, M. S., Peterson, A., Plaut, D., & McDonald, M. C. (1996). Pseudo-homophone effects and models of word recognition. Journal of ExperimentalPsychology: Learning, Memory and Cognition, 22, 48–62.

Simos, P. G., Breier, J. I., Fletcher, J. M., Foorman, B. R., Castillo, E. M., & Papanicolaou, A.C. (2002). Brain mechanisms for reading words and pseudowords: An integratedapproach. Cerebral Cortex, 12, 297–305.

Simos, P. G., Pugh, K., Mencl, E., Frost, S., Fletcher, J. M., Sarkari, S., et al. (2009). Tem-poral course of word recognition in skilled readers: A magnetoencephalographystudy. Behavioural Brain Research, 197, 45–54.

Spitsyna, G., Warren, J. E., Scott, S. K., Turkheimer, F. E., & Wise, R. J. S. (2006). Converg-ing language streams in the human temporal lobe. The Journal of Neuroscience,26, 7328–7336.

Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders’method. In W. G. Koster (Ed.), Attention and performance II (pp. 276–315). ActaPsychologica.

Strupp, J. P. (1996). Stimulate: A GUI based fMRI analysis software package. Neu-roImage, 3, S607.

Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain.New York: Thieme Medical Publishers, Inc.

Vinckier, F., Dehaene, S., Jobert, A., Dubus, J. P., Sigman, M., & Cohen, L. (2007).Hierarchical coding of letter strings in the ventral stream: Dissecting the inner

organization of the visual word-form system. Neuron, 55, 143–156.

Visser, T. A. W., & Besner, D. (2001). On the dominance of whole-word knowledgein reading aloud. Psychonomic Bulletin & Review, 8, 560–567.

Yap, M. J., & Balota, D. A. (2007). Additive and interactive effects on respone timedistributions in visual word recognition. Journal of Experimental Psychology:Learning, Memory, and Cognition, 33, 274–296.