NeuroImage 59 (2012) 655–662

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Different types of target probability have different prefrontal consequences

Nicholas Hon ⁎, Jeffrey Ong, Rebecca Tan, Tang Hsiang YangDepartment of Psychology, National University of Singapore, 9 Arts Link, S117570, Singapore

⁎ Corresponding author. Fax: +65 67731843.E-mail address: psyhonn@nus.edu.sg (N. Hon).

1053-8119/$ – see front matter © 2011 Elsevier Inc. Aldoi:10.1016/j.neuroimage.2011.06.093

a b s t r a c t

a r t i c l e i n f o

Article history:Received 16 March 2011Revised 15 June 2011Accepted 30 June 2011Available online 23 July 2011

Keywords:Target probabilityDLPFCVLPFCfMRI

One of the factors known to affect target detection is target probability. It is clear, though, that targetprobability can be manipulated in different ways. Here, in order to more accurately characterize the effects oftarget probability on frontal engagement, we examined the effects of two commonly-used but different targetprobability manipulations on neural activity. We found that manipulations that affected global stimulus classprobability had a pronounced effect on ventrolateral prefrontal cortex and the insula, an effect which wasabsent with manipulations that only affected the likelihood of specific target stimuli occurring. This lattermanipulation only modulated activity in dorsolateral prefrontal cortex and the precentral sulcus. Our datasuggest two key points. First, different types of target probability have different neural consequences, andmaytherefore be very different in nature. Second, the data indicate that ventral and dorsal portions of prefrontalcortex respond to different types of task-relevant information.

1 Also known as g

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© 2011 Elsevier Inc. All rights reserved.

Introduction

The ability to accurately detect the occurrence of meaningful stimuli(or “targets”) is fundamental to everyday life. Unsurprisingly, then,target detection and the factors influencing it have been fastidiouslystudied for well over a half-century. In the brain, target detection isaccompaniedby robust activity in anumber of frontal areas including thedorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex(VLPFC), precentral sulcus (PreCS) and the insula (Bledowski et al.,2004b; Hampshire et al., 2007; Kirino et al., 2000; Shulman et al., 2001),and in some cases, orbitofrontal cortex (Casey et al., 2001). This patternof activity is observed regardless of the type of target used or theresponse required by the targets (Downar et al., 2001; Hon et al., 2009;Linden et al., 1999), suggesting that it is stimulus- and response-independent. A popular model suggests that, when a target is detected,an updating of mental (and neural) models of the current, relevantcontext occurs (Donchin and Coles, 1988). Frontal activity observed inresponse to targets is proposed to reflect this updating.

One of the factors proposed to influence the level of detection-related frontal activity is target probability: In general, the lower theprobability of occurrence of a target event, the greater the level offrontal activation. Electrophysiological studies have long shownneural sensitivity to target probability. Target stimuli evoke a strongpositive deflection in the ERPwaveform, a deflection referred to as theP3 (Soltani and Knight, 2000). An inverse relationship between P3amplitude and target probability has been routinely reported: P3amplitude increases as a target becomes more infrequent (Duncan-

Johnson and Donchin, 1977; Soltani and Knight, 2000; Tueting et al.,1970). Brain imaging studies, using methods like fMRI, aimed at moreaccurately identifying the generators of the P3 have revealed a networkof frontal (andparietal) areas similar if not identical to theonedescribedabove (Bledowski et al. 2004a,b; Kirino et al., 2000; McCarthy et al.,1997). Mirroring electrophysiological studies, magnitude of BOLD-dependent frontal activation increases as target probability decreases(Casey et al., 2001; Huettel et al., 2002). Findings such as these suggestthat target probability may play a role in determining the magnitude offrontal engagement in target detection.

It is clear, though, that target probability can be made to vary in anumber of different ways. This leaves open the question of howcomparable different target probability manipulations are. In manystudies, it is target-to-distractor probability1 which is manipulated.Target-to-distractor probability refers to the distribution of targets anddistractors within a given block of trials. For instance, in a given block, iftargets occur on 20% of the total number of trials, then distractorswouldoccuron the remaining80%. The effect of target-to-distractorprobabilitymanipulations is assessed by comparing targets from blocks withdifferent target-to-distractor ratios. Notice that, with such manipula-tions, the probability of specific target stimuli and the probability oftarget events as a whole are largely confounded. As such, in those cases,it is unclear whether it is the probability of occurrence of specific targetstimuli or of target events as a whole which drives frontal activity.

Of course, it is possible for specific target stimuli to occur withdifferent probabilities without the probability of target events as awhole being affected. One common way is for different constituentmembers of the same target set to occur with different probabilities

lobal stimulus class probability.

Fig. 1. General schematic of detection paradigm used in both experiments. Each blockutilized different target sets. Target sets always comprised two target letters. The sameresponse was made to all targets in an experiment.

656 N. Hon et al. / NeuroImage 59 (2012) 655–662

(target-to-target probability), with some members appearing lessfrequently than others. For example, suppose that, in any given block,targets always account for 50% of all trials, and that the target setcomprises the letters “A” and “B”. Notice that any change to theinternal distribution of “A” and “B”, so long as targets as a wholecontinue to account for 50% of all trials, would affect the probability ofspecific target stimuli occurring without causing any change to theoverall likelihood of target events. In contrast to target-to-distractorprobability, any behavioral or neural differences when target-to-target probability is manipulated would be related only to theprobability of specific target stimuli occurring.

The goal of this study was to more accurately characterize theeffects of target probability on frontal activation. To do this, weexamined the frontal effects of the two commonly-used targetprobability manipulations discussed above.2 In Experiment 1, wemanipulated target-to-distractor probability. In Experiment 2,we trackedneural activity when target-to-target probability was manipulated. Iftarget probability per se influences detection-related frontal activity,then both the target-to-distractor and target-to-target probabilitymanipulations should affect similar frontal areas.

Methods

Design and procedure

In both experiments, participants watched sequences of seriallypresented letter stimuli with the goal of detecting target letters (Fig. 1).Each letter stimulus was shown for 1000 ms, followed by a blank framepresented for 800 ms. Each letter was presented in the center of thescreen and subtended approximately 3.15°×3.72° of visual angle.

In Experiment 1, in which we manipulated target-to-distractorprobability, 17 participants performed two blocks of letter targetdetection. In one block, targets accounted for 25% of the total numberof trials (T25 block); in the other, targets accounted for 50% of all trials(T50 block). Within each block, the order of presentation of thedifferent trial types (targets and distractors) was randomized for eachsubject. The order of presentation of the T25 and T50 blocks wascounterbalanced across subjects.

In each block, participants were instructed to look out for either oftwo letter targets; that is, the target set for each block always comprisedtwo letters (e.g., A and B). In both blocks, the total number of targetswassplit evenly between the twomembersof the target set. Therefore, in theT50 block, each target letter accounted for 25% of all trials; whereas, inthe T25 block, each target letter accounted for 12.5% of all trials. Thetarget letters used were always different across blocks. Targets werenever re-used and never formed part of the distractor set. The totalnumber of targets was the same for each block (66 targets in eachblock); therefore, to achieve the different target-to-distractor probabil-ities, the number of distractors was different in the T25 and T50 blocks.The total number of trials in the T25 block was 264 (66 targets and 198distractors) and the total number of trials in the T50 block was 132(66 targets and 66 distractors). Subjects made the same response(right-hand index finger button press) to all the targets, regardless ofblock.

Because there were a different number of distractors in the twoblocks, it was important to ensure that differential experience withspecific distractor stimuli did not affect our results. To ensure this, thesame distractor set was used for both blocks, with the set comprisingall other letters of the alphabet excluding the four letters designatedas targets for the two blocks. Therefore, the distractor set for bothblocks always comprised the same 22 letters. Because the samedistractors were used for both blocks, each of the 22 distractor letterswas presented 12 times throughout the whole experiment: Three

2 There are, of course, other methods of manipulating target probability; forexample, by varying target-to-target intervals (Gonsalvez et al., 1999).

times each in the T50 block and nine times in the T25 block. As notedpreviously, block order was counterbalanced. Therefore, when a givendistractor letter was presented for the first time in, for instance, theT50 block, it could have been the very first occurrence of this distractorin the experiment (if the T50 block was presented first) or the tenth (ifthe T25 block was presented first). Given the known long-lastingeffects of stimulus experience on behavior and brain activity (vanTurennout et al., 2000), this ensured that, across all subjects, experiencewith specific distractors did not disproportionately affect one block overthe other.

In Experiment 2, 19 different participants also performed two blocksof letter target detection. As in Experiment 1, two letters of the alphabetwere assigned as targets for each block; that is, eachblock had a target setcomprising two pre-specified letters. The target sets were alwaysdifferent across blocks. The distractor set comprised letters not used astargets. Targets were never re-used and never formed part of thedistractor set. Subjects made button-press responses with their rightindex-fingers when they detected the targets. As before, the sameresponsewasmade toall targets, regardless of block. Inbothblocksof thisexperiment, targets as a whole accounted for 50% of the total number oftrials. In thecritical blockof this experiment, onememberof the target setoccurred less frequently than the other (Uneven Targets block).Specifically, in the Uneven Targets block, one member of the target setaccounted for 12.5% of the total number of trials (Infrequent target), andthe other accounted for 37.5% of all trials (Frequent target). In the otherblock,which served as a control and to ensure that thedesign structure ofthis experiment matched the two-block structure of Experiment 1, thetwo members of the target set occurred with equal probability (EvenTargets block); that is, the two target letters eachaccounted for 25%of thetotal number of trials. In both blocks, therewere a total of 240 trials, withtargets as a whole accounting for 120 of these. In the Uneven Targetsblock, the Infrequent target appearedon30 trials, and the Frequent targetappeared on 90 trials. In the Even Targets block, each target letterappearedon60 trials. The order of presentation of thedifferent trial typeswithin any given block was randomized for each subject, and the blockswere presented to the subjects in a counterbalanced fashion.

Image acquisition

Subjects were scanned on a 3.0 T Siemens TIM Trio MRI scannerwith a standard head coil. Functional volumes were acquired with anechoplanar imaging sequence (repetition time, 1500 ms; echo time,30 ms; field of view, 192×192 mm; flip angle, 90°). Each volume

3 We also assessed the effect of block order on our results. To do this, we entered theDLPFC andVLPFCparameter estimates from the (TargetsT25 Block−DistractorsT25 Block) and(TargetsT50 Block−DistractorsT50 Block) contrasts into a mixed-ANOVA taking probability(T50 andT25) as awithin variable andblockorder (T25 thenT50vsT50 thenT25) as a betweenvariable. There were neither main effects of block order nor any block order X probabilityinteractions in any of the ROIs (all psN .05).

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comprised of 28 slices, aligned with the anterior commissure–posterior commissure line. Slice thickness was 4 mm, with interslicedistance of 0.4 mm, and in-plane resolution of 3.0×3.0 mm. InExperiment 1, a total of 505 (175 for the T50 block and 330 for theT25 block) functional volumes were acquired for each participant. InExperiment 2, a total of 630 (315 each for the Uneven and Even Targetsblocks) functional volumes were acquired for each participant in theexperiment. Anatomical images were acquired using a T1-weightedMPRAGE sequence (flip angle=7°, TE=1.64 ms, TR=2530 ms) togenerate 240 1 mm slices.

Analysis of fMRI data

The fMRI data were preprocessed and analyzed using SPM5(Wellcome Department of Imaging Neuroscience, London, UK). Novolumes (other than those acquiredwithdummy scans)were discardedduring the analysis. Prior to statistical analysis, images were motion-corrected and normalized to MNI (Montreal Neurological Institute)space. The normalized images were spatially smoothed with an 8 mmfull-width half-maximum Gaussian kernel.

For the whole brain analysis, there were 2 levels of statisticalanalysis. First, event-related contrasts of interest for each participantwere computed using the general linear model, fitting the activity ofeach voxel with a combination of functions obtained by convolvingthe synthetic hemodynamic response function with the time series ofevents. To that end, for both experiments, each trial, comprising the1000 ms letter stimulus on-period and the following 800 ms blankframe, was considered a single event of 1800 ms duration. Trial order,in both experiments, was randomized, allowing for detection ofdifferences between conditions even without explicit temporaljittering (Friston et al., 1999; Josephs and Henson, 1999). This allowedus to adopt designs that were consistent with those used inestablished behavioral studies of the phenomena in question.

In Experiment 1, we explicitly modeled three trial types for eachblock. Recall that, in both blocks, target sets always comprised twoletters. We modeled each of the target letters and the distractorsseparately. At the individual subject-level analysis stage, for eachblock, we linearly combined the two independently-estimated targetsto form a single target condition. In Experiment 2, there were threetrial types in each block. In the Uneven Targets block, the three trialtypes were Frequent Target (member of the target set that occurred37.5% of the trials), Infrequent Target (member of the target set thatoccurred 12.5% of the trials) and Distractors. In the Even Targets block,there were also three trial types: Target 1, Target 2 and Distractors,where the two equiprobable targets were arbitrarily assigned aseither Target 1 or Target 2. The different trials types for the Even andUneven Targets blocks were explicitly modeled. Low-frequency noisewas removed with a high-pass filter with a cutoff period of 128 s.Second, for each contrast, individual participants data were combinedand subjected to a random effects analysis.

For our subsidiary ROI analysis and the analysis of the neural targetprobability effects in DLPFC and VLPFC, we defined spherical ROIs(regions of interest; radius=10mm) around the following VLPFC andDLPFC coordinates: ±50, 12, 8 and ±34, 36, 22 respectively. Thesecoordinateswere derived by averaging theDLPFC andVLPFC peaks froman independently performed targetdetectionstudy. ForExperiment1,weextracted, for each subject, parameter estimates (PE) from the followingtwocontrasts: TargetsT25 Block−DistractorsT25 Block (henceforthPET25) andTargetsT50 Block−DistractorsT50 Block (henceforth PET50). For Experiment 2,we extracted contrast values from the following two contrasts: Infrequenttargets−DistractorsUnevenTargetsBlock (henceforthPEInfrequent) andFrequenttargets−DistractorsUnevenTargetsBlock (henceforth PEFrequent). Preliminaryanalyses revealed no interaction between conditions of interest andhemisphere; therefore, for themain analysis, summary values, created byaveraging activity from left and right ROIs, were used. For each ROI,neural target probability indices for both experiments were comput-

ed. For Experiment 1, this index was computed in the followingmanner: PET25−PET50. For Experiment 2, it was computed thus:PEInfrequent−PEFrequent.

Results

Experiment 1: behavioral data

In Experiment 1, we examined the effects of target-to-distractorprobability on neural activity. To begin with, though, we report thebehavioral consequences of this manipulation. As predicted by a largeliterature, targets in the T25 block (M=488 ms, SD=47 ms)produced reliably slower responses than targets in the T50 block(M=469 ms, SD=57 ms; t(16)=2.53, pb .05). Detection accuracywas uniformly high and there was no difference in accuracy betweenthe two blocks (T25: 99.5%; T50: 99.9%).

Experiment 1: fMRI data

To assess general target detection-related activity, we contrastedall targets with all distractors. We found, consistent with theliterature, that simple target detection produced activation in alarge number of brain areas (Fig. 2, left panel and Table 1).Pronounced bilateral activity was observed in a number of lateralfrontal areas including DLPFC, VLPFC, PreCS and the insula. In theparietal lobe, activation was observed in the intraparietal sulcus (IPS).Also obvious was prominent activation of the left motor cortex,consistent with the requirement of a right-handed manual responsein relation to target detection.

To directly assess the effects of target-to-distractor probability onneural activity, we performed the following contrast: [(TargetsT25 Block−DistractorsT25 Block)−(TargetsT50 Block−DistractorsT50 Block)]. This con-trast essentially pitted the activity elicited by less frequent targets withthat elicited by more frequent targets. This contrast revealed that activityin the vast majority of detection-related areas was modulated by targetprobability: Less frequent targets (i.e. those from the T25 block) producedstronger activation than targets that appearedmore frequently (Fig. 3, leftpanel and Table 2). In particular, robust activity in parietal and prefrontalsites was revealed by this contrast. Frontal activation was observed inVLPFC, DLPFC, PreCS and the insula.3 Additionally, we found that theneural target probability effects in DLPFC and VLPFC were significantlypositively correlated (r=.59, p=0.01; Fig. 4 left panel), suggesting thattarget-to-distractor probability had similar effects on dorsal and ventralportions of prefrontal cortex.

Our main contrast, [(TargetsT25 Block−DistractorsT25 Block)−(TargetsT50 Block−DistractorsT50 Block)], factored in the activity associ-atedwithdistractors. Thiswasnecessary because the key to-be-comparedstimuli, the targets, came from twodifferent blocks. As such to account forpossible time-dependent changes in theMRsignal,weneeded to factor-inblock-specific “controls” (in this case, thedistractors fromthe twoblocks).This leaves open the question of whether distractor processing signifi-cantly influenced our results. Although possible, we feel this is unlikely.Numerous studies have shown, with a variety of methods ranging frominvasive cell-recordings to fMRI, that prefrontal areas are not sensitive todistractor stimuli (Everling et al., 2002; Freedman et al., 2001; ThompsonandDuncan, 2009). As such, althoughwe recognize thepossibility,we feelit unlikely that distractor processingplayed a significant role in generatingthe results above.

Fig. 2. Results from all targets— all distractors contrast from (left) Experiment 1 and (right) Experiment 2. For depiction purposes, the statistical threshold usedwas pegged at the T valuecorresponding to pb .05 (FDR) for the Experiment 1 contrast.

658 N. Hon et al. / NeuroImage 59 (2012) 655–662

Experiment 2: behavioral data

In this second experiment, we examined the effects of target-to-target probability on neural activity. First, though, we discuss thebehavioral effects. In theUneven Targets block,we found that InfrequentTargets (M=465 ms, SD=50 ms) produced slower reaction times thanFrequent ones (M=441 ms, SD=48 ms; t (18)=4.11, pb .005). In theEven Targets block,which functioned as a control, we found that the twotargets took the same time to detect,which is unsurprisinggiven that thetwo targets in this block occurred with the same probability. There wasno difference in detection accuracy between the Infrequent (99.3%) andFrequent Targets (99.8%) of the Uneven Targets block, nor betweenTarget 1 (99.5%) and Target 2 (98.9%) of the Even Targets block.

Experiment 2: fMRI data

As in Experiment 1, all targets contrasted against all distractorsproduced strong activity in the familiar set of frontal and parietalareas, as well as in left motor cortex (Fig. 2, right panel and Table 1).

In this experiment, the aim was to assess the effects of target-to-target probability. To begin with, though, we compared the targetsfrom the Even Targets block as a simplemanipulation check. Essentially,we contrasted Target 1 and Target 2. Recall that these two targetsoccurred with the same probability. Predictably, comparing these two

Table 1Target-related neural activity. The peaks listed here come from independent contrastspitting all targets against all distractors from the two experiments. All reported peakspassed a whole-brain false detection rate threshold of pb .05 (corrected). Coordinatesare given in MNI space. Numbers in parentheses refer to Brodmann Areas. VLPFC:ventrolateral prefrontal cortex; DLPFC: dorsolateral prefrontal cortex; PreCS: pre-central sulcus; IPS: intraparietal sulcus; STS: superior temporal sulcus.

Side Experiment 1 Experiment 2

Coords (x, y, z) T value Coords (x, y, z) T value

MotorMotor L −54, −22, 28 8.43 −60, −26, 30 4.90FrontalPreCS (6) L −44, −6, 62 3.91 – –

R 46, 16, 46 7.01 36, 16, 52 4.50DLPFC (9/46) L −44, 46, 28 3.35 – –

R 44, 40, 32 3.96 50, 46, 26 3.74VLPFC (44/45) L −62, 12, 20 4.00 – –

R 52, 14, 16 6.70 56, 14, 10 4.46Insula L −34, 20, −4 5.95 −42, 16, −2 4.51

R 42, 20, −2 6.49 60, 18, 0 4.03ParietalIPS (7/40) L −54, −44, 56 4.42 −48, −52, 46 3.47

R 46, −48, 52 5.49 44, −52, 48 5.57TemporalSTS (22/39) L −46, −50, 8 3.74 – –

R 54, −48, 8 5.94 62, −36, 14 3.67VisualEarly visual (18) L −36, −94, −14 3.46 – –

R 48, −80, −12 3.61 44, −80, −14 4.19

equiprobable members of the same target set produced no resultantactivation.

To assess the effects of target-to-target probability on neuralactivity, we compared Infrequent and Frequent Targets from theUneven Targets block. With this contrast, we observed activation ofearly and late visual areas. Additionally, activation of frontal andparietal areas (Fig. 3, right panel and Table 2) was evident. Frontalactivation was observed in DLPFC and PreCS. Notably absent wasactivation in VLPFC and the insula, regions that were robustlyinfluenced by target-to-distractor probability in Experiment 1.

Region of interest (ROI) analyses provide additional confirmation ofthis finding. In this experiment, Infrequent targets did not produceVLPFC activation that was statistically different from that evoked byFrequent targets (pN .20, paired samples t-test; Fig. 5 right panel). Incontrast, in Experiment 1, targets from the T25 block produced reliablygreater activity in VLPFC than targets from the T50 block (pb .005,paired samples t-test, Fig. 5 left panel). Additionally, the finding thatthe probability manipulation of this experiment modulated onlyDLPFC and not VLPFC suggests that target-to-target probability affectsventral and dorsal portions of prefrontal cortex differently. Consistentwith this idea, we found that, in this experiment, the DLPFC and VLPFCtarget probability indices were not significantly correlated (r=.14,pN .50; Fig. 4 right panel).

Prefrontal areas commonly affected by the two target probabilitymanipulations

Finally, we examined which prefrontal areas would be commonlyactivated by the two target probability manipulations. To do this, weassessed, using a conjunction-null model, the conjunction of the[(TargetsT25 Block−DistractorsT25 Block)− (TargetsT50 Block−DistractorsT50 Block)] contrast of Experiment 1 and the [Infrequent−Frequent] contrast of this experiment. Only right DLPFC and PreCSwere reliably activated with this analysis (Fig. 6).

Discussion

The purpose of this study was to more accurately characterize theeffects of target probability on frontal activity. To do this, weexamined the neural effects of two different target probabilitymanipulations. In Experiment 1, we considered the effects of target-to-distractor probability by manipulating the overall distribution oftargets relative to distractors. In Experiment 2, on the other hand, weexamined the effects of target-to-target probability. Specifically, inthat experiment, we examined the difference in neural activityelicited by less and more frequent members of the same target set.Both target-to-target probability and target-to-distractor probabilitywere found to modulate activity in PreCS and DLPFC. However, anotable difference was that VLPFC and the insula were not reliablyaffected by target-to-target probability, only by target-to-distractorprobability. As such, our data indicate that manipulations of different

Fig. 3. Neural target probability effects. Left: Results from the [(TargetsT25 Block−DistractorsT25 Block)− (TargetsT50 Block−DistractorsT50 Block)] contrast from Experiment 1.Right: Results from the Infrequent−Frequent Targets contrast from Experiment 2. All depicted activity passed awhole-brain corrected threshold (pb .05, FDR) and exceeded acluster size of 20 voxels.

659N. Hon et al. / NeuroImage 59 (2012) 655–662

types of target probability produce different patterns of prefrontal(PFC) activity.

An influential class of models suggests that prefrontal areas play acritical role in behavioral and cognitive control by representing task-relevant information (Desimone and Duncan, 1995; E.K. Miller andCohen, 2001). Control is said to be exerted as these task represen-tations bias or guide activity in related brain systems such that overallprocessing and behavior converges on what is relevant (Kastner et al.,1999; Kastner and Ungerleider, 2000). Target events are proposed toproduce an updating of this higher-order task representation(Donchin and Coles, 1988). Information about targets, including theprobability with which they occur, must be integral to these represen-tations. On this view, low probability targets, being more unexpected,would require a more pronounced adjustment of task representationsthanhigher probability ones. Accordingly, lower probability targets affectfrontal activity to a greater extent than higher probability counterparts.

Frontal task representations must accommodate a wide array ofinformation pertaining to the task, ranging from the identity of thetarget stimuli to the responses required by detection of those stimuli(Sakai, 2008). Our data contribute to this idea by suggesting thatdifferent aspects of the task schema are coded in different parts of PFC,

Table 2Peaks from contrasts pitting less and more frequent targets from the two experiments.The peaks from Experiment 1 come from a [(TargetsT25 Block−DistractorsT25 Block)−(TargetsT50 Block−DistractorsT50 Block)] contrast. The peaks from Experiment 2 comefrom the Infrequent−Frequent Targets contrast. All reported peaks passed a whole-brain false detection rate threshold of pb .05 (corrected). Coordinates are given in MNIspace. Numbers in parentheses refer to Brodmann Areas. VLPFC: ventrolateralprefrontal cortex; DLPFC: dorsolateral prefrontal cortex; PreCS: precentral sulcus;IPS: intraparietal sulcus; STS: superior temporal sulcus.

Side Experiment 1 Experiment 2

Coords(x, y, z) T value Coords(x, y, z) T value

FrontalPreCS (6) L −30, −6, 62 3.23 −38, 8, 58 5.19

R 42, 8, 50 4.28 30, 14, 60 3.99DLPFC (9/46) L −40, 36, 22 4.86 – –

R 36, 42, 32 4.85 32, 38, 36 5.11VLPFC (44/45) L −54, 6, 10 3.91 – –

R 56, 16, 4 4.08 – –

Insula L −28, 16, 4 6.32 – –

R 40, 18, −2 7.12 – –

ParietalIPS (7/40) L −26, −62, 46 4.51 −32, −64, 50 6.53

R 38, −58, 44 5.19 36, −56, 50 4.17TemporalSTS (22/39) R 58, −34, 0 4.28 50, −40, 24 6.73VisualEarly visual (18/19) L −42, −80, −12 6.23 −52, −74, −4 5.18

R 46, −68, −16 3.54 46, −78, −4 5.26Late visual (37) L −50, −58, −14 3.57 −46, −60, −8 5.39

R – – 46, −50, −18 4.16

even when these relate to the same target stimulus. Specifically, ourdata suggest the following account: DLPFC has a particular sensitivityto stimulus-level information, including information about theprobability of a specific target stimulus occurring. VLPFC, on theother hand, is sensitive to information relevant to the action (orresponse) plans associated with the targets, including informationabout the probability or frequency with which these plans are calledinto play. Here, we use the term “action plans” loosely, to refer to anybehavioral or mental response that is required by a task, be they overt(e.g., button press) or covert (e.g., silent counting). We discuss thisproposal below.

In Experiment 2, we manipulated the likelihood of occurrence ofdifferentmembers of the same target set. Recall that both the frequentand infrequent members of the target set were presented within thesame block, and shared the same response. As such, our target-to-target probability manipulation would have only influenced thelikelihood of specific target stimuli appearing. When we contrastedthe different probability targets in that experiment, we observed onlyDLPFC activation, suggesting that DLPFC is particularly sensitive tostimulus-level information. Recent studies have also suggested a linkbetween DLPFC and stimulus-level information. For example, in astudy utilizing the Stroop task, low probability incongruent stimuliwere found to produce greater DLPFC activity than higher probabilityones, even when the global congruence–incongruence ratio wascontrolled for (Blais and Bunge, 2010).

In Experiment 1, on the other hand, our target-to-distractormanipulation would have affected two things in tandem. First, itwould have also affected the likelihood of occurrence of specific targetstimuli: A given target letter would have appeared less frequently,relative to distractors, in the T25 block than in the T50 block. Accordingly,we also observed DLPFC activation in that experiment. Second, it wouldhave affected the likelihood of occurrence of targets as a whole: Targetevents occurred less frequently (relative to the distractors) in the T25block than in the T50 block. One interpretation of ourfindings, therefore,is that VLPFC has a particular sensitivity to information about thecurrently relevant stimulus class, including how often the relevantstimulus class occurs. However, it is worth noting that, because ourtargets required responses, a consequence of manipulating the overalllikelihood of occurrence of target events is that frequency of respondingwould have also been affected: Responses would have been made lessfrequently (and be less expected) in the T25 block than in the T50 block.We suggest that the modulation of VLPFC activity observed inExperiment 1 is related to this difference in the rate (and predictability)of responding across the two blocks. Specifically, we propose that theVLPFC activation observed in that experiment reflects the updating ofaction plans by information about the frequency with which a task-relevant response is required, with less frequently executed responsesproducing greater adjustments to existing plans.

Our findings are consistent with recent suggestions that ventralprefrontal areas contribute to controlled behavior by housing

Fig. 4. Regression plots showing the relationship between the VLPFC and DLFPC neural target probability indices in Experiment 1 (left) and Experiment 2 (right).

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representations of task-related action plans. Recent studies havefound that VLPFC activity is enhanced when updating of action plansis performed (Chikazoe et al., 2009; Mars et al., 2007; Verbruggen etal., 2010). Two recent studies are of particular interest to this report.In one study, it was observed that only infrequent stimuli thatrequired a change of action plan produced activation of VLPFC.Infrequent (but task relevant) stimuli that did not require such achange did not modulate VLPFC activity (Chikazoe et al., 2009).Similarly, a recent transcranial magnetic stimulation (TMS) studyfound that TMS applied to VLPFC disrupted the ability to update actionplans, while leaving updating by task-relevant stimuli undisturbed(Verbruggen et al., 2010). This is consistent with the idea we presenthere — that there is a distinction between the frontal coding of task-relevant stimuli and the action plans that those stimuli trigger. Whileprevious studies of action plan updating have emphasized changes toexisting plans or the supplanting of one plan with another, here, wesuggest that incoming information about the frequency with whichtask-relevant responses are made can also produce updating.

At this juncture, it is worth discussing our results in relation toprevious imaging work on the target probability effect. Our findingsare largely consistent with the results reported by Huettel (Huetteland McCarthy, 2004). In that experiment, different targets wereassociated with different handed responses. Typically, (congruent)targets were presented in the hemi-field consistent with the hand ofresponse (e.g., a target requiring a right-handed response waspresented on the right side of the screen). Infrequently, (incongruent)targets were presented in the hemi-field opposite to the hand ofresponse (e.g., a target requiring a right-handed response waspresented on the left side of the screen). Comparing the infrequentincongruent trials with the frequent congruent trials produced strongdorsal and ventral prefrontal activity. This is consistent with ourfindings. Notice that, in that experiment, when infrequent targets

Fig. 5. VLPFC activity in the two experiments. The y-axes are presented in SPM co

occurred, they required an updating of stimulus information: A targetnormally presented in a particular location is now presented in adifferent one. Furthermore, infrequent targets would have more thanlikely required a change to the predominant action plan: Normallyeffective action plans dependent on the physical location of the targetwould have to be inhibited and supplanted on infrequent incongruenttarget trials.

On the surface, our findings appear to be at odds with thosepresented by Casey and colleagues (Casey et al., 2001). In that study,although they reported DLPFC findings similar to ours, they found thatVLPFC activation was greater in high probability blocks than in lowprobability ones. Twomethodological differencesmay account for thisdifference between those findings and ours. First, the ventralprefrontal areas tested there were quite different from ours. VLPFCactivation in that study was a composite of activity from inferiorfrontal and orbitofrontal regions (BA 47 and 11 respectively). Here,our VLPFC can be localized specifically to BA 44/45. Second, in thatexperiment, in high probability blocks, targets were more frequentthan distractors (i.e. targets accounted for more trials than distrac-tors). Plausibly, when targets clearly account for more trials thandistractors, a different task strategy will be employed as compared towhen targets are less probable than or equiprobable to distractors. Forexample, when distractors are rarer than targets, one might adopt astrategy emphasizing response inhibition, as opposed to oneemphasizing response initiation. We speculate that these differenceslikely account for the discrepancy between our findings and theirs.

The current proposal is also worth considering within thecontext of studies using the classic three-stimulus oddballparadigm. In that paradigm, infrequent targets and (equally)infrequent novel distractors (“novels”) are intermingled withmuch more frequent standard distractors (“standards”). A numberof fMRI studies using this paradigm have shown that both targets

ntrast of parameter estimates (in arbitrary units). Error bars denote 1 S.E.M.

Fig. 6. Conjunction of the [(TargetsT25 Block−DistractorsT25 Block)−(TargetsT50 Block−DistractorsT50 Block)] contrast from Experiment 1 and the [Infrequent−Frequent]contrast of Experiment 2. Whole-brain activation was thresholded at pb .001(uncorrected). Additionally, the depicted prefrontal activations passed a pb .05 smallvolume correction threshold.

661N. Hon et al. / NeuroImage 59 (2012) 655–662

and novels engage DLPFC, when contrasted against the “standards”(Bledowski et al., 2004a; Gur et al., 2007; Yamaguchi et al., 2004).This is consistent with the idea that DLPFC is sensitive to stimulusinformation that may be directly (as with targets) or potentially(as with the “novels”) relevant to the current behavioral context.What is particularly interesting, though, is that only targets appearto reliably activate VLPFC. This finding is also consistent with ourproposal. Note that, in typical versions of the paradigm, onlytargets require responses, while standards and novels do not. Assuch, relative to the more frequent standards, only targets wouldrequire action plan updating (a change from a “do not respond”plan to a “respond” plan).

In the past, studies of the target probability effect have oftenused a variety of target probability manipulations interchangeably.Certainly, very different manipulations often produce the samebehavioral effect: Less frequent targets are responded to moreslowly and with less accuracy than more frequent counterparts(Laberge and Tweedy, 1964; J.O. Miller and Pachella, 1973; Wolfeet al., 2007). Here, however, our data show that differentmanipulations may be very different in nature, having differentneural consequences. The finding that target-to-target and target-to-distractor probability manipulations have different frontaleffects leads naturally to a number of possibilities. For example,given the ability of frontal task representations to guide subse-quent cognitive processing and behavior (Bressler et al., 2008;Kastner and Ungerleider, 2000; Moore and Armstrong, 2003), it isworth considering the possibility that stimulus- and action-relatedprobability information may independently affect detection be-havior. Although beyond the scope of this current study, wesuggest that future studies specifically designed to employmethods like response time modeling in conjunction with event-related fMRI will likely shed more light on this issue.

Conclusion

In conclusion, our data speak to two main points. First, our datasuggest that different types of target probability have differentprefrontal consequences. The implication of this is clear: Differentmanipulationsmay not be directly comparable, despite the similarity intheir behavioral outcomes. Second, our data also shed light on thedifferent functions served by dorsal and ventral portions of prefrontalcortex. Specifically, our findings suggest that ventral and dorsal regionsof prefrontal cortex code for different aspects of the current task schema.Certainly, other studies have proposed differences in the processesserved by different subregions of prefrontal cortex (Badre andD'Esposito, 2007; Koechlin et al., 2003; Owen, 1997; Petrides, 2005).Here, however, we show, with exceedingly simple experimentalmanipulations, that differences between ventral and dorsal portions ofprefrontal cortexmay have to dowith the nature of the information thateach is sensitive to. This suggestion is more consistent with represen-tation-based views of prefrontal function (Wood and Grafman, 2003).

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