dissociating the neural mechanisms of visual attention...

Dissociating the Neural Mechanisms of Visual Attention inChange Detection Using Functional MRI

Scott A. Huettel, Guven Guzeldere, and Gregory McCarthy

Abstract

& We investigated using functional magnetic resonanceimaging (fMRI) the neural processes associated with perform-ance of a change-detection task. In this task, two versions ofthe same picture are presented in alternation, separated by abrief mask interval. Even when the two pictures greatly differ(e.g., as when a building is in different locations), subjectsreport that identification of the change is difficult and oftentake 30 or more seconds to identify the change. Thisphenomenon of ’ ’change blindness’ ’ provides a powerful andnovel paradigm for segregating components of visual attentionusing fMRI that can otherwise be confounded in short-durationtasks. By using a response-contingent event-related analysistechnique, we successfully dissociated brain regions associatedwith different processing components of a visual change-detection task. Activation in the calcarine cortex was associatedwith task onset, but did not vary with the duration of visualsearch. In contrast, the pattern of activation in dorsal and

ventral visual areas was temporally associated with theduration of visual search. As such, our results support adistinction between brain regions whose activation is modu-lated by attentional demands of the visual task (extrastriatecortex) and those that are not affected by it (primary visualcortex). A second network of areas including central sulcus,insular, and inferior frontal cortical areas, along with thethalamus and basal ganglia, showed phasic activation tied tothe execution of responses. Finally, parietal and frontal regionsshowed systematic deactivations during task performance,consistent with previous reports that these regions may beassociated with nontask semantic processing. We concludethat detection of change, when transient visual cues are notpresent, requires activation of extrastriate visual regions andfrontal regions responsible for eye movements. These resultssuggest that studies of change blindness can inform under-standing of more general attentional processing. &

INTRODUCTION

Visual attention allows organisms to allocate processingresources to selected locations or objects in the visualfield. Lesion studies in humans and nonhuman primateshave suggested that visual attention depends upon adistributed network of brain regions, including theposterior parietal cortex (Posner, Walker, Friedrich, &Rafal, 1984; Mesulam, 1981), frontal cortex (Paus, 1996),and cingulate cortex, as well as the superior colliculusand thalamus (Posner & Petersen, 1990). In conjunction,functional neuroimaging studies have demonstrated theparticipation of the intraparietal sulcus (IPS), posteriorsuperior frontal gyrus (SFG), and precentral gyrus invisual attention tasks (Courtney, Petit, Maisog, Unger-leider, & Haxby, 1998; Nobre et al., 1997; Corbetta,Miezen, Shulman, & Petersen, 1993; Corbetta, Shulman,Miezin, & Petersen, 1995). This network may also in-clude visual cortical regions, depending upon the stimuliand task (e.g., Corbetta, Miezen, Dobmeyer, Shulman, &Petersen, 1990), in line with the distinction betweenventral and dorsal visual pathways (Ungerleider & Mis-hkin, 1982; Ungerleider & Haxby, 1994).

One challenge considered by recent functional neuro-imaging studies is the dissociation of different compo-nents of visual attention, such as cue-directed attentionversus target responses (Hopfinger, Buonocore, & Man-gun, 2000) or orienting to locations versus detection ofstimuli in unattended locations (Corbetta, Kincade, Ol-linger, McAvoy, & Shulman, 2000). In the present study,our goal was to dissociate, in a single experimental task,brain regions associated with attentional processingfrom those associated with other components of thetask, such as nonattentive perceptual processing orresponse execution. We employed a change detectionparadigm known as a ’ ’flicker’ ’ task (Rensink, O’Regan,& Clark, 1997; Rensink, 2000b) while testing subjectsusing functional magnetic resonance imaging (fMRI). Oneach trial, two photographs were presented in alterna-tion, separated by a short-duration mask. The imagesdiffered in one aspect, such as the presence/absence,color, or position of a single object. The subject’s taskwas to identify the change. Figure 1 provides an exampleof an image pair used in the current experiment. Thetwo photographs in the pair differ in that a sign in theupper right above the building is present in the leftimage but absent in the right image. The short-durationmask prevented automatic detection of change thatDuke University, Durham, NC

© 2001 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 13:7, pp. 1006–1018

occurs when low-level motion transients are available,such as when the interval between stimuli is less thanabout 80 msec (Pashler, 1988; Phillips, 1974). Behavioralevidence suggests that, when this automatic detectionprocess is not available, subjects engage in a controlledserial search among display elements (Rensink et al.,1997). The search process is guided by the semanticcontent of the image, such that changes are morequickly detected on objects that are named in verbaldescriptions (Rensink et al., 1997). As such, cuing hasrelatively little effect on search, with facilitation onlyreported for color changes (Aginsky & Tarr, 2000).Finally, detection of change is associated with the locusof attention rather than of eye position, although thetwo move similarly: Even when looking right at thechange location, subjects fail to detect 40% of changesacross blinks (O’Regan, Deubel, Clark, & Rensink,2000).

Change-detection tasks of this kind have two charac-teristics that map well onto fMRI analysis techniques.First, change detection is a slow process in comparisonto other visual searches (hence the name, ’ ’changeblindness’ ’). Response times in masked change-detec-tion tasks typically range from 5 to 40 sec or more,depending on the scene characteristics. In the presentstudy, mean response time was approximately 23 sec. Incontrast, most visual search tasks used in cognitiveexperiments have response times that are roughly oneorder of magnitude faster. Thus, the extended durationsof change-detection trials match well the temporalproperties of the fMRI hemodynamic response, whichrises and falls over a minimum of 10–15 sec. A secondcharacteristic is display homogeneity, in that the rawvisual stimulus has a constant pattern over an extendedinterval. Throughout each trial of the task, the subject

sees the same images and mask flickering at a constantrate, with no interruptions from cuing, extended inter-stimulus interval, or feedback. Differences in the patternof activation over time may therefore be attributed tocognitive processing and not to stimulus presentation.Despite these characteristics and the significant recentinterest in behavioral studies of change detection (e.g.,Rensink, 2000a; Simons & Levin, 1997), functional neu-roimaging studies of change detection in a flicker taskhave not been previously conducted.

The subject’s behavioral response in a change-detec-tion task provides an objective marker for the subjectiveprocess of visual search. Taking advantage of this, wedeveloped an analysis technique that uses response-contingent event-related fMRI. Because of the extendedduration of our experimental task, we can use informa-tion about the duration and timing of responses to guideanalyses. Typical short-duration visual search tasks donot temporally separate search and response processesby durations greater than the temporal resolution of thefMRI hemodynamic response. Furthermore, there islittle variability in the duration of the search process(e.g., from 1 to 2 sec). Our analysis uses the response-time variability over trials to identify voxels whoseactivity is associated with visual search. Simply put, theduration of voxel activation, if that voxel is associatedwith search, should show sustained activation through-out search, with short-duration activation when thetarget is found quickly and long-duration activationwhen the target is found slowly. In contrast, voxelsassociated with low-level visual processing should onlyshow phasic activation at trial onset, due to the stimulusappearance. Voxels associated with response processingshould show a hemodynamic response time-locked tothe behavioral response.

Figure 1. The flicker task usedin the present experiment.Shown in A is an examplestimulus pair from the currentexperiments. These two scenesdiffer in one aspect, the pre-sence or absence of a sign atupper right. Typical changesacross scenes were the pre-sence/absence of an object, thelocation of an object, or thecolor of an object. The order ofevents on any one trial is shownin B. Between each pair of trialswas a 2-sec fixation cross on ablack screen. For the first 30 secof each trial, the pictures werepresented in alternation, eachfor 300 msec with a 100-msecgrayscale mask between them.During the final 10 sec of eachtrial, the mask was removed andthe pictures were presented for400 msec.

Huettel, Guzeldere, and McCarthy 1007

We had two goals for the current study. First, weevaluated whether the extended-duration change-de-tection task, in conjunction with response-contingentanalyses, can be used to dissociate brain regions re-sponsible for different cognitive processes within acomplex task. We then investigated how patterns ofactivation associated with different components varywith processing demands of the task. For example, ifactivation in primary visual areas is associated with theduration of visual search, that result would suggest thatthose areas are susceptible to attentional influences.No duration-related changes in activity would suggestthat primary visual regions are not sensitive to atten-tional demands, at least from search tasks similar tothat conducted.

RESULTS

Behavioral Testing

For the behavioral analyses reported here, all trials werecategorized into one of four response bins according tothe subject’s response time on that trial: 0–10, 10–20,20–30, or 30–40 sec. These response categories wereused in the fMRI analyses reported in the next sections.For our experimental task and analysis techniques to beappropriate for fMRI, response times should be evenlydistributed across the trial interval, to ensure maximumvariability, but should be similar across subjects andstimuli.

The distribution of response-time categories for theindividual stimulus trials is shown in Figure 2. As isevident from the figure, response time was relativelyuniformly distributed across stimulus trials, with slightlyfewer trials in the 20- to 30-sec bin than in the others, sogreater variability was expected in the fMRI data result-

ing from that bin than in any other. Intersubject differ-ences in response time were small, in comparison. Therange of mean response times across subjects was 16–22sec (mean: 19 sec, standard deviation: 2 sec). Further-more, responses to each stimulus were significantlycorrelated across subjects, as revealed by Monte Carlosimulation of the subject response data ( p < .00001).So, the flicker task used provides the advantages of highresponse-time variability combined with low intersubjectand low interstimulus variability.

Brain Regions Associated with Trial Onset

Figure 3 presents the activation in the calcarine cortex(see Table 1 for voxel locations and statistical informa-tion). For every response bin, there was a transient peakresponse, followed by a return to a lower level. A two-factor analysis of variance investigated whether therewas a difference in calcarine activation as a function ofTR (– 8 to 38 sec) and response-time category (0–10, 10–20, 20–30, or 30–40 sec). Although the form of theresponse was visibly similar across all four response-timecategories, the ANOVA revealed significant differencesbetween the four response categories, such that moreactivation was found for the longer response categories,F(3,69) = 12.4, p < .0001. This difference was observedthroughout the trial interval, possibly representing dif-ferences in stimulus properties (e.g., number of objects)that contribute both to task difficulty and to greatervisual input. As evident from the task design shown atthe bottom of Figure 3, the transient response at taskonset resulted from the reduced visual input present inthe no-mask (i.e., not flickering) and fixation cross(black background) periods. The only other area activeto trial onset was the medial frontal gyrus, which was

Figure 2. The histogram of mean response time to stimulus pairs.Shown are the mean response categories for all stimulus pairs used inthe fMRI testing. A response category rating of 1.0 indicates that everysubject responded within 0–10 sec for that stimulus pair. Conversely, aresponse category rating of 4.0 indicates that every subject respondedwithin 30–40 sec for that stimulus pair. As is evident from the figure,the response times across stimuli were roughly uniformly distributedacross the range of possible values.

Figure 3. Trial-onset-related activation in calcarine cortex. Shown atright are the time courses of activation, in percent signal change over aprestimulus baseline, across the four response-time categories for theregion of active voxels in calcarine cortex (shown in the slice at left). Inthis and all subsequent figures, the colormap displaying fMRI activationindicates voxels whose time courses significantly correlated withexperimental hypotheses, with red indicating a mean correlation acrossconditions of .63 (p < .001) and yellow indicating a mean correlationacross conditions of .85 or greater. Activation in this region wasmodulated by trial onset, but did not show a temporal modulationassociated with duration of visual search.

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Table 1. Brain Regions Significantly Active Across Subjects, Organized by Analysis Condition and Lobe within the Brain

No. of Subjects MNI Coordinates Mean Size

Region L/R x y z L/R Notes

Trial onset

Occipito-parietal

Lingual gyrus/cuneus 9 14 – 68 – 8 2734/3244

Frontal

Precentral gyrus (L) 8 – 44 – 8 34 55

Medial frontal gyrus 9 – 6 8 52 84/42 A

Visual search

Occipito-parietal

Fusiform/lingual gyri 10 28 – 66 – 16 * B

Intraparietal sulcus 10 22 – 72 34 * B

Parahippocampal gyrus 6/7 – 22 – 34 – 8 70/85

Insular

Anterior insula 7/8 32 32 – 4 137/257

Frontal

Precentral gyrus 9/10 – 28 – 8 54 917/659 C

Medial frontal gyrus 9/10 8 6 54 90/215 A

Superior frontal gyrus (R) 7 16 66 – 10 141 D

Precentral sulcus 9 48 2 30 420/458

Inferior frontal gyrus (L) 7 – 42 16 26 405

Response

Frontal

Central sulcus (L) 7 – 44 – 36 54 658 E

Inferior frontal gyrus (R) 4 48 14 26 94

Anterior cingulate gyrus (R) 4 8 10 42 39

Medial frontal gyrus 7 2 10 54 363 F

Insular

Insula 6/7 44 20 – 14 272/506

Temporal

Hippocampus 2/5 14 0 – 20 65/75

Hippocampus (R) 2 24 – 26 – 14 53

Subcallosal gyrus (L) 4 – 22 8 – 14 70

Occipito-parietal

Parahippocampal gyrus (L) 2 – 18 – 38 – 14 39

Inferior parietal/cuneus (R) 2 40 – 60 42 67

(continued on next page)


also associated with visual search and will be describedin the next section.

Brain Regions Associated With Visual Search

A network of brain regions, including the extrastriatevisual cortex, frontal and supplementary eye fields, andinsular/inferior frontal cortex, was systematically acti-vated for the duration of visual search. Stereotaxic

coordinates for these activations are presented in Table1. Activation in the ventral extrastriate cortex is shown inFigure 4A. Bilateral activation along the posterior extentof the fusiform gyrus (FFG) was found in all subjects;this activation abutted the trial-onset activation found inthe calcarine cortex in superior slices. Additional foci ofactivation were found in the bilateral parahippocampalgyrus. The parahippocampal activation was not contig-uous with the FFG activation.

Table 1. (continued)

No. of Subjects MNI Coordinates Mean Size

Region L/R x y z L/R Notes

Subcortical

Subthalamic nucleus 5/4 4 – 20 – 6 66/170

Caudate 2/4 8 6 0 39/89

Putamen (L) 4 – 22 6 0 99

Thalamus 4/6 8 – 20 2 39/116

Deactivation

Occipito-parietal

Angular gyrus 9 – 52 – 64 16 4654/3383

Precuneus 9 0 – 62 16 3043

Inferior parietal lobule 6 32 – 40 68 285/1431

Temporal

Middle/superior temporal gyrus 9/8 – 58 – 16 – 32 1223/484

Superior temporal gyrus 8 60 – 6 4 476/918 G

Lateral sulcus (R) 8 60 – 32 18 707 G

Frontal

Frontal pole 4/5 – 34 60 – 18 155/143

Anterior cingulate 8 – 2 54 2 1172

Middle frontal gyrus (L) 8 – 22 54 28 369

Central sulcus 6/7 42 – 20 44 153/448

Cingulate gyrus 6 2 – 22 44 982

Superior frontal gyrus 7/9 – 24 28 50 995/850

Notes: A. This region shows significant positive correlation to both trial onset and experimental task (only such overlapping activation); areacorresponds to supplementary eye fields. B. Activation region is continuous from the fusiform gyrus in the temporal lobe through the IPS in theparietal lobe, corresponding to extrastriate visual cortical areas. This activation is sufficiently large to make voxel counts not relevant. C.Activation corresponds to frontal eye fields. D. Activation not present in random-effects analysis. E. Includes one subject whose activation wasright lateralized (see text). F. Activation is contiguous with SEF (note A), but does not overlap them. G. Contiguous activations in righthemisphere.

Indicated are all foci consisting of 35 or more contiguous suprathreshold voxels (t > 3.5; see text for analysis details). This cluster size thresholdwas chosen to reflect a volume equivalent to four or more uninterpolated voxels (acquisition voxel volume: 3.75 £ 3.75 £ 5 mm; normalizedvoxel volume: 2 £ 2 £ 2 mm). Shown for each focal activation are the numbers of subjects with significant activation in that region, thecoordinates in MNI space, and the size of the activation averaged across subjects. All activations were bilateral, unless explicitly indicatedotherwise. No voxel counts are provided for the activations in fusiform/lingual gyri and along the IPS, as they include the entire anatomicalregions identified.


Dorsal extrastriate activation is shown in Figure 4B.Visible at the top of the figure (parietal lobe) is bilateralactivation in the IPS. These activation foci extended fromthe superior parietal to occipital cortex, but did notextend into the calcarine cortex. The time course ofactivation was similar to that found in the FFG, save thatthe activation reached a peak slightly later in the IPS(12–14 sec) than in the FFG (8–10 sec).

Significant, well-defined activation was found in theprecentral gyrus and medial frontal gyrus, both bilater-ally (Figure 4B). These locations are consistent withprevious stereotaxic reports of the frontal eye fieldsand supplementary eye fields, respectively (Paus,1996). Additional foci of activation in the frontal lobewere found across subjects in the inferior frontal gyrus(IFG) and insular cortex (not shown, see Table 1 forlocations).

Brain Regions Associated With ResponseExecution

Response execution was associated with activationaround the central sulcus (contralateral hemisphere),anterior cingulate, insular cortex, basal ganglia, andcerebellum. Figure 5A indicates the activation aroundthe left central sulcus. Evident from the time courseplots is the systematic relation between time of behav-ioral response and onset of the hemodynamic response.The increased hemodynamic amplitude of the 30- to 40-sec bin is due to the lack of its within-category variabilityin subjects’ response times; that is, when the mask wasremoved, all subjects reported seeing the changes with-in the first few seconds. In the other categories, how-

ever, the subjects’ response times were roughlyuniformly distributed within the intervals. This resultedin relatively less temporal smoothing in the 30- to 40-secbin than in the other bins, allowing for a sharperhemodynamic time course. Similar activation timecourses were found for the anterior cingulate (activationshown in Figure 5A; time courses not plotted).

Significant activation was also found in the thalamusand basal ganglia. Figure 5B indicates the loci and timecourses of thalamic activation, which was observedbilaterally. As seen in the central sulcus activation, thedifferent response times evoked transient hemodynamicresponses with similar latencies. Bilateral caudate acti-vation was also observed (Figure 5C), with responsetime courses nearly identical to those from the thalami.Visible in Figure 5C are the observed activations in theputamen (larger spatial extent in left hemisphere) andinsular cortex (bilateral), whose time courses are notplotted. Cerebellar activation was bilateral, but of largerspatial extent in the hemisphere ipsilateral (right) to theresponding hand.

One subject exhibited the opposite pattern of lateral-ization from the other subjects. In this subject, thecerebellar activation was in the left hemisphere, theanterior insula activation was in the left hemisphereonly, the central sulcus activation was in the right hemi-

Figure 4. Visual-search-related activation in extrastriate visual regions.At right are the time courses of activation, in percent signal changeover a prestimulus baseline, for voxels in the FFG (A) and the IPS (B)foci. For both regions, activation rose at trial onset and fell followingthe cessation of visual search.

Figure 5. Response-related activation. At right are the time courses ofactivation, in percent signal change over a prestimulus baseline, forvoxels in the central sulcus (A), the thalamus (B), and the caudatenucleus (C). All regions showed transient hemodynamic responsestemporally consistent with response execution.


sphere, and the putamen activation was in the righthemisphere. As this subject was left-handed, we assumethat the subject turned the response box in the scannerso that he could use his dominant hand to press theresponse button.

Brain Regions Showing Deactivations During TaskPerformance

One limitation of any long-duration experimental task isthat subjects’ cognitive processing is heterogeneous, inthat different processes are likely to be invoked duringdifferent aspects of the task. In our experimental task,the stimuli remained on screen following the behavioralresponse, which indicated the conclusion of visualsearch. Therefore, we considered the possibility thatsubjects would engage in task-unrelated cognitive pro-cessing following the detection of the stimuli. As will beseen below, we identified a network of brain regions thatshowed systematic deactivations, relative to baseline,during the experimental task, including the angulargyrus, precuneus, middle frontal gyrus (MFG), and SFG.

Figure 6A shows regions evidencing deactivationsduring task performance. Plotted at right is the set oftime courses found for the angular gyrus, which showeda bilateral deactivation pattern. Regardless of eventualresponse time, activation in this region decreased overthe first 8 sec of the trial. Then, for trials where thesubject responded within the first 10 sec, the responsebegan returning to baseline. The trough for the other

three response categories occurred about 10 sec follow-ing trial onset, after which time the hemodynamicresponse returned to baseline. Interestingly, althoughthe three latter response categories began their returnto baseline at the same time point, the rate of return wasmost rapid for the earlier behavioral responses. Becausethis hemodynamic response antedated the subjects’button presses, the differential returns to baseline sug-gest that stimulus effects, such as complexity or numberof objects, might mediate both response time andhemodynamic properties in these regions. Similar timecourse patterns, with return to baseline faster for earlierbehavioral responses, were found for all brain regionsdiscussed in this section. Also visible in Figure 6A are theregions of activation in the precuneus and MFG (leftonly), whose time courses are not plotted. Bilateraldeactivation patterns were found in the SFG, as shownin Figure 6B.

Summary of Activations Observed

Figure 7 displays the patterns of activations found foreach of our experimental hypotheses. Readily apparent

Figure 6. Deactivations during task performance. At right are the timecourses of activation, in percent signal change over a prestimulusbaseline, in the angular gyrus (A) and in the superior frontal gyrus (B).These regions showed deactivations during the performance of thevisual search task, although the time course of the rise to baseline didnot match the time course of visual search (see text for details).

Figure 7. The overall patterns of activation in the current study.Shown are the voxels whose activation time courses were associatedwith task onset, visual search, response execution, or deactivationduring task performance. The overlays indicate areas active at asignificance threshold of p < .001.


is the complementary nature of the processing compo-nents: The areas associated with trial onset were adja-cent to those associated with task performance, whileneighboring parietal regions showed task-related deac-tivations.

DISCUSSION

We were able to dissociate and identify different brainsystems associated with attentionally guided search,response execution, passive perceptual viewing, andnon–task-related cognitive processing using fMRI in acomplex visual search task. Dissociation was accom-plished through a response-contingent analysis techni-que that utilized the variability in subject response time.Our technique readily segregated key components thatcan be confounded in short-duration visual attentiontasks. Here, we evaluate the results of this segregationfor understanding of attentional processing, while con-sidering extensions of this experimental design to othertopics.

Attention and Visual Search

The primary regions found to be associated with visualsearch were dorsal and ventral extrastriate visual corticalareas, along with the frontal and supplementary eyefields. In contrast, calcarine cortical areas were associ-ated with task onset, but not visual search. Regionsassociated with response execution included centralsulcus, insular, and inferior frontal cortical areas, thala-mus, and basal ganglia.

A central question in the visual attention literature isthe identification of brain regions whose activationappears to be modulated by visuospatial attention. Ourresults suggest a distinction between medial calcarinecortex, which likely includes primary visual areas, andthe surrounding visual cortex, such that the former isnot affected by attentional demands of the task whileactivation in the latter varies with task demands. Thisdistinction is consistent with most earlier work, whichhas suggested that extrastriate but not striate visualcortex is modulated by attention (Heinze et al., 1994;Moran & Desimone, 1985). However, a number ofrecent studies have demonstrated attentional effects inprimary visual cortex (Gandhi, Heeger, & Boynton, 1999;Somers, Dale, Seiffert, & Tootell, 1999), perhaps due toreentrant feedback from extrastriate areas (Martõ nezet al., 1999). More established attentional effects exist inparietal and frontal regions, which have been associatedwith spatial shifts of attention and attentionally guidedeye movements. Eye movements and spatial attentionshifts have been shown to elicit similar activation pat-terns when independently performed (Corbetta et al.,1998). In both cases, parietal cortex activation is tightlycoupled with activity in the precentral cortex, identifiedin the frontal eye fields (Courtney et al., 1998; Paus,

1996). Our results supported the functional associationbetween these regions, as their activation patternsacross response bins were similar. Gitelman et al.(1999) found a similar activation network in an fMRIstudy of spatial attention using a variant of the Posnercuing task (Posner, 1980). However, their network in-cluded activation in basal ganglia and thalamus, whichwere restricted to response execution in the presentstudy.

While our results were consistent with studies inves-tigating neural substrates of spatial attention, they wereonly partially consistent with research addressing therelated construct of spatial working memory. Our con-cept of working memory as advanced in the cognitiveneuroscience literature (e.g., Cohen et al., 1997) has twoprimary components: short-term storage and informa-tion manipulation/comparison. For subjects to performthe change-detection task used in the present experi-ment, they must attend to a spatial location, rememberobjects over a short interval (¹500 msec), evaluatewhether something changed, and select a new locationbased on their memory of what has been previouslysearched. Thus, the change-detection task required bothshort-term storage and comparison of presented andremembered displays. It differed from many workingmemory tasks in the absence of information rehearsal,because the continuous flicker cycle eliminated the needto hold information in memory for extended durations.

Our posterior regions of activation were similar tothose implicated in spatial working memory tasks(Belger et al., 1998; Smith & Jonides, 1997; Ungerleider,1995), in that significant parietal cortex activation tovisual search was largely restricted to the regions sur-rounding the IPS. Given that the flicker task sharesspatial comparison processes with many spatial workingmemory tasks, our results were consistent with the ideathat parietal cortex activation in spatial working memorytasks may be associated with the process of attending tospatial locations and evaluating their contents.

Spatial working memory studies have consistentlyshown dorsolateral prefrontal cortex (dlPFC) activationin the anatomical region of the MFG (D’Esposito et al.,1995; Cohen et al., 1994; McCarthy et al., 1994). In thepresent study, our primary activation foci to visualsearch in prefrontal cortex were not in the MFG, butinstead in the IFG near the MFG border. One possiblereason for this difference may reside in the diversity ofcognitive processes associated with working memory; indifferent experiments, ’ ’working memory’ ’ labels con-trolled selection (as in the present study), maintenanceof information over time, or response selection. Previousstudies that have found dlPFC activation typically requireextended memory maintenance (e.g., delayed match tosample or n-back tasks; Braver et al., 1997; Cohen et al.,1997; Courtney, Ungerleider, Keil, & Haxby, 1997; Hax-by, Ungerleider, Horwitz, Rapoport, & Grady, 1995) orinhibition of a prepotent response (e.g., oddball tasks;


McCarthy, Luby, Gore, & Goldman-Rakic, 1996, 1997). Incontrast, tasks involving shifting of attention rapidlyacross locations have not reliably elicited dlPFC activa-tion (Nobre et al., 1997; Corbetta et al., 1995; Corbettaet al., 1993). Furthermore, response execution, initself, did not activate dlPFC, as seen in Table 1. Giventhese previous results in conjunction with our find-ings, the role of dlPFC in spatial working memorytasks may be limited to maintenance processing or toresponse selection, rather than to spatial search.

Deactivations during Task Performance

An interesting and unexpected finding was the presenceof large, well-defined deactivations during task perform-ance. These were largely restricted to the angular gyrusand posterior cingulate in the posterior cortex (bothbilateral), and to the left MFG and bilateral SFG in thefrontal cortex. These deactivations were not temporallyassociated with subject response time, in that the troughlatency was similar for all response categories. However,the return to baseline was slower for the longer re-sponse categories.

Deactivation patterns similar to that found in thepresent study have recently been reported by twogroups using very different tasks. Binder et al. (1999)compared tone detection, semantic comparison, andphonetic comparison tasks with a no-stimulus rest peri-od in a blocked design. In the comparison rest–tones(e.g., areas more active during the rest period), theyfound activation in the angular gyrus, posterior cingu-late, and dorsal prefrontal cortex, primarily in the lefthemisphere. The stereotaxic coordinates of the activa-tion center of mass in these areas reported by Binderet al. fall within the activations observed in thepresent study, although our activations were generallybilateral. Furthermore, they reported deactivation inthe parahippocampal gyrus, which exhibited task-related activation in our experiment. With that excep-tion, our event-related design identified a similar set ofbrain regions as their earlier blocked design work.Binder et al. reported that similar regions are active ina semantic–tone comparison, concluding that theseregions underlie conceptual, not perceptual function-ing, due to their relative isolation from primary sensorycortices (Felleman & Van Essen, 1991; Mesulam, 1985).

Votaw et al. (1999) described a similar pattern ofdeactivations in a comparison between a confrontationalnaming task, which requires subjects to verbally identifythe name of a picture, and a control size-judgment task.In both PET and fMRI modalities, they demonstratedthat the naming task, relative to the size-judgment task,showed a decrease in activation in inferior parietal areas(nearly identical to our region labeled as ’ ’angulargyrus’ ’ ), the precuneus, and MFG and SFG (left lateral-ized, as in the present study). Increases in activationwere found in ventral visual areas, notably the FFG and

adjacent gyri. Because the regions of deactivation wereconsistent across both PET and fMRI and are presentwhether or not the PET data are globally normalized,Votaw and colleagues concluded that deactivations donot result from data analysis artifacts. Instead, they mayreflect areas inhibited during performance of the namingtask or preferentially activated by the size-judgment task.

The regions identified in the present study as lessactive during visual search may underlie baseline seman-tic processing that is interrupted when the subjectbegins the perceptual task. Deactivation of a similarneural system has been found during visual processingcompared to passive visual stimulation (Shulman et al.,1997). Importantly, in the present study, the return tosignal baseline does not occur following task comple-tion, but 8–12 sec after task onset. We suggest that thislatency delay resulted from the initial organization ofscene information during the first few seconds of thetask, at which time the subjects set up a general layout ofthe image and a search strategy. This delay has not beenpreviously reported in blocked designs (e.g., Binderet al., 1999), which confound time of task completionwith the onset of a rest period. Our results thus arguefor multiple dissociable neural systems, as proposed byBinder et al. (1999), including a perceptual processingsystem active throughout task performance and a con-ceptual/reflective processing system active following ini-tial task processing.

Change Detection and Change Blindness

The experimental task we used takes advantage of thephenomenon of change blindness to investigate visualattention. Although change blindness provides a fertileresearch paradigm for investigation of the neural sub-strates of perceptual and cognitive processing, otherimportant topics can be addressed using the experimen-tal techniques of the present study.

The central phenomenal aspect of change-detectiontasks is the shift in the display from a hidden changebefore detection to a conspicuous change afterward.During the postexperiment debriefing, our subjectsreported that the change ’’pops out at them’ ’ followingits detection, so that ’ ’ it is visible no matter where youlook.’ ’ The comparison between effortful search duringpredetection and effortless noticing during postdetec-tion is striking and impressive, as one of the hallmarks ofchange blindness is that even very large changes (e.g.,the reflection of a large building in water) can take tensof seconds for detection. Detection is phenomenallysimilar to unmasked flicker; in both cases, the changebecomes readily apparent without controlled attentionto its location. Although an understanding of the neuralprocessing underlying this phenomenal shift would beof considerable interest, our results suggested a poten-tial confound to be avoided: the presence of task-unrelated conceptual processing. As seen in Figure 7,


multiple brain regions showed increased activation fol-lowing change detection, although this activation can beattributed to conceptual processing rather than to phe-nomenal changes in the display. Careful attention tocontrol conditions will be necessary to deconfoundconceptual activation from perceptual changes.

A second aspect of change blindness that lends itselfto functional neuroimaging investigation is the phenom-enon of ’ ’ sensing’ ’ of change (Fernandez-Duque &Thornton, 2000; Rensink, 1998). Some subjects reportthe feeling that something is changing in the displaybefore they can explicitly identify the specific object thatis changing. Catch trials and conservative response-timecriteria validate the accuracy of the subjects’ sensingreports. In the current experiment, we provided subjectswith the option for making sensing responses, but it wasrarely used by the subjects in our sample. Previousreports from behavioral studies suggest that about 35%of individuals report sensing change (Rensink, 1998).Although our results could not address sensing directly,investigation of sensing of change may provide a techni-que for examining implicit perception.

METHODS

Subjects

Ten young adults participated in the study, which wasapproved by the Institutional Review Board of the DukeUniversity Medical Center.

Stimuli

On each trial, two versions of the same image werepresented in alternation separated by a short-durationgrayscale mask. Images were photographs of complexscenes acquired from publicly available image libraries.The grayscale masks were individually generated foreach image pair to match for mean luminance level.Each image or mask subtended about 158 by 108 ofvisual angle, presented against a black background. Thetwo images within a pair differed in either the presence/absence of a single object, the position of an object, orthe color of an object or part of an object. The exper-imental stimulus set consisted of 120 image pairs andtheir associated masks.

Experimental Design

Each subject participated in 10–12 experimental runs(mean: 11.1 runs), with each run consisting of 10stimulus trials. At the beginning of each run, the subjectsviewed a single practice image pair for 12 sec to ensurebaseline equivalence across all trials; the same pair ofimages was used at the start of all 12 runs. A fixationcross was next presented at the center of the screen for2 sec, followed by the first trial. Each trial consisted of

40 sec of alternating presentations of the two images inthe pair (see Figure 1). For the first 30 sec of the trial,the two images were presented for 300 msec, separatedby the 100-msec mask. The mask was removed duringthe last 10 sec of the trial, during which time the stimulialternated every 400 msec. This allowed all subjects tofind every stimulus change, even if previously unde-tected. Each run lasted 7 min 12 sec.

The subjects made behavioral responses on a fiberoptic response box. First, each subject pressed a buttonwith the left hand when he or she felt that there wassomething changing on the trial but could not identify it.This feeling of covert recognition is described as ’ ’ sens-ing’ ’ of change and is reported by a minority of subjects(Rensink, 1998). Second, each subject pressed a buttonwith the right hand when the specific change could beidentified. Four trials were randomly selected to becatch trials with no change present; no subject identifieda change occurring on those trials. Only two of thesubjects in our group reported sensing of change onmore than 20% of the trials, so no additional analyses ofthe sensing responses were conducted. All analysesreported in this manuscript investigate the identificationof change (right-button responses). Subjects were al-lowed to move their eyes freely during the experimentaltrials, with the one instruction that they were to keeptheir eyes on the display at all times.

fMRI Imaging Parameters

fMRI scans were conducted on a 1.5-T GE NVi SIGNAscanner with 41 mT/m gradients for fast echo-planarimaging. Image transfer and reconstruction was con-ducted using a GE Advanced Development Workstation.A vacuum-pack system restricted head motion withoutcompromising patient comfort. Axial slices, chosen par-allel to the line connecting the anterior and posteriorcommissures, were selected in each subject followinginitial sagittal structural imaging (2-D SPGR; nine slicesaround midline). These slices encompassed the entirecerebral cortex (22–24 slices, 5 mm thick, no skip). High-resolution spin-echo structural images were acquired foreach slice (in-plane resolution: 0.94 mm2). Functionalimages were acquired at the same slice locations usinggradient-echo echo-planar imaging (TR: 2000 msec, TE:40 msec, flip angle: 908, in-plane resolution: 3.75 mm2).

fMRI Preprocessing and Analysis

Initial preprocessing involved correction for head mo-tion and temporal order of slice acquisition within a TR,using SPM 99 software (Wellcome Department of Cog-nitive Neurology, London, UK). Following those correc-tions, each subject’ s brain was normalized into acommon stereotaxic space (MNI 305). No additionalspatial smoothing was performed. All subsequent analy-ses used custom MATLAB scripts written by the authors.


To analyze the fMRI data, we used response-contin-gent event-related analyses. For each trial, we identifiedan epoch of 24 fMRI time points that included four TRsbefore trial onset and the 20 TRs of the trial itself. Eachepoch was then categorized into one of four binsdepending upon when the subject made the change-identification response: 0–10, 10–20, 20–30, or 30–40sec. All trials in each bin were then averaged together,resulting in a set of four response-time–based activationestimates. These activation estimates were in turn aver-aged across subjects. We then statistically compared theactivation at each voxel in the brain to a priori exper-imental hypotheses, which were generated by convolu-tion of an empirically generated fMRI hemodynamicresponse with predictions of neural activity. Search-related voxels were identified as those with activitycorrelated to an extended waveform, where activationlevels rose at task onset to a maximum value (reached at8 sec) and returned to baseline following the subject’sresponse. Response-related voxels were identified asthose with transient activity beginning at the onset ofthe response bin (i.e., a hemodynamic response with arise and fall over 16 sec, time-locked to bin onset).Onset-related voxels had transient activity at the startof the task, regardless of eventual response bin. More-over, voxels were classified as having a deactivationpattern if their time courses followed an initial decrease,followed by a return to baseline after the conclusion ofsearch.

For each hypothesis tested, we measured the corre-lation of the experimentally observed activation at eachvoxel with the predicted pattern of activation. Thethreshold for voxel significance was set at a meancorrelation of r = .63 (t = 3.5, p < .001, for eachresponse bin) across the four response bins. To controlfor Type I error, we adopted a minimum cluster-sizethreshold, following the calculations reported by For-man et al. (1995) and by Xiong, Gao, Lancaster, and Fox(1995). Given the approximate number of voxels in ouruninterpolated brain volume (¹17,000) and our uncor-rected significance threshold (p < .001), a cluster size ofthree or more voxels controls for Type I error at anoverall value of .01. However, we adopted a minimumcluster size of four voxels because of suggestions ofdisagreements between empirical and theoretical dataat smaller cluster sizes (Xiong et al., 1995). To localizeactivations, the centroid of each cluster was identifiedwithin MNI stereotaxic space (Montreal NeurologicalInstitute), as reported in Table 1. All activation timecourses in Figures 3–6 are averaged across all activevoxels within the anatomical region discussed.

We additionally conducted a random-effects analysis(e.g., Postle & D’Esposito, 1999) to account for thepossibility that significant results in the group-averageddata could be driven by large effects in a few subjects.We repeated our hypothesis tests for each subjectindividually, resulting in a set of four t values at each

voxel, one for each of the hypotheses tested. Thenumber of subjects who had significant activation atthe locations indicated by the group data is shown inTable 1. We then used the individual subject t values asdependent measures of effect size for each subject, andconducted a t test on those t values. We set thesignificance threshold (different from a mean of zero)to p < .001. We found that this random-effects analysisprovided similar results to the fixed-effects analysisreported above. Only one area, the SFG activation tovisual search, was not active in the random-effectsanalysis at that threshold. Given this correspondence,we conclude that our results are generalizable to thepopulation from which our subject sample was drawn.

This analysis technique allows segregation of activa-tion components in a manner not possible with short-duration tasks. It is difficult, during short-duration tasks,to distinguish hemodynamic responses evoked during atask (e.g., visual search) from those evoked at its con-clusion (e.g., motor response). However, task-relatedand response-related activation patterns should changein different ways as response time increases. Our ap-proach uses the known variability in subject responsetime over the long response interval to identify differentpatterns in voxel activation.

Acknowledgments

The authors thank Jeff Singerman and Jeff Wu for assistance infMRI data analysis, Elizabeth Redcay for assistance in behavioraldata analysis, and Josh Wills for creation of the experimentalstimuli used. We also thank Ron Rensink for comments andsuggestions during the course of this project. This researchwas supported by NIMH grants MH-05286 and MH-12541 andthe Department of Veterans Affairs.

Reprint requests should be sent to Scott A. Huettel, BrainImaging and Analysis Center, Duke University Medical Center,Box 3918, Durham, NC 27710, or via e-mail: [email protected].

The data reported in this experiment have been deposited inthe National fMRI Data Center (http://www.fmridc.org). Theaccession number is 2-2001-111T9.

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