Neuropsychology1999, Vol. 13, No. 4, 564-574

Copyright 1999 by the American Psychological Association, Inc.0894-4105/99/S3.00

Striatal Activation During Acquisition of a Cognitive SkillRussell A. Poldrack, Vivek Prabhakaran, Carol A. Seger, and John D. E. Gabriel!

Stanford University

The striatum is thought to play an essential role in the acquisition of a wide range of motor,perceptual, and cognitive skills, but neuroimaging has not yet demonstrated striatal activationduring nonmotor skill learning. Functional magnetic resonance imaging was performed whileparticipants learned probabilistic classification, a cognitive task known to rely on proceduralmemory early in learning and declarative memory later in learning. Multiple brain regionswere active during probabilistic classification compared with a perceptual-motor control task,including bilateral frontal cortices, occipital cortex, and the right caudate nucleus in thestriatum. The left hippocampus was less active bilaterally during probabilistic classificationthan during the control task, and the time course of this hippocampal deactivation paralleledthe expected involvement of medial temporal structures based on behavioral studies ofamnesic patients. Findings provide initial evidence for the role of frontostriatal systems innormal cognitive skill learning.

The effects of experience on behavior are mediated bymultiple memory systems in the brain. Declarative memoryis characterized by conscious recollection of specific eventsand facts and is impaired by lesions to the hippocampus andrelated medial temporal lobe (MTL) structures (Cohen &Eichenbaum, 1993; Squire, 1992). Procedural (or non-declarative) memory is a collection of memory abilities thatare not impaired by lesions to the hippocampus and relatedMTL structures (Cohen & Squire, 1980). One type ofprocedural memory is skill learning, which involves theacquisition of new behavioral capacities through practicewithout the mediation of explicit, declarative memory.

Skill learning is thought to rely on changes at multiplelevels of neural organization and across multiple brainregions, but current understanding of the neural mechanismsof skill learning is limited. There is evidence that thestriatum (consisting of the caudate nucleus and putamen inthe basal ganglia) is important for many types of skilllearning. Studies of skill learning in patients with Hunting-ton's disease (HD) and Parkinson's disease (PD), both ofwhich compromise the function of the striatum, suggest thatthe striatum is involved in the acquisition of motor skills,including rotary pursuit (Heindel, Salmon, Shults, Wallicke,& Butters, 1989) and sequential reaction time (Willingham& Koroshetz, 1993). Neuroimaging studies using positron

Russell A. Poldrack, Carol A. Seger, and John D. E. Gabrieli,Department of Psychology, Stanford University; Vivek Prab-hakaran, Program in Neurosciences, Stanford University.

This work was supported by grants from the National Institute ofMental Health, the National Institute on Aging, and the McDonnell-Pew Program in Cognitive Neuroscience.

We thank James Brewer for assistance with localization ofmedial temporal lobe structures, Margaret Zhao for assistance withdata analysis, and Neal Cohen, Maggie Keane, Barbara Knowlton,and Jennifer Ott for helpful comments on this article.

Correspondence concerning this article should be addressed toRussell A. Poldrack, who is now at MGH-NMR Center, Building149, 13th Street, Charlestown, Massachusetts 02129. Electronicmail may be sent to poldrack@nmr.mgh.harvard.edu.

emission tomography (PET) and functional magnetic reso-nance imaging (fMRI) have provided converging evidencefor the involvement of the basal ganglia in normal motorskill learning (e.g., Grafton, Hazeltine, & Ivry, 1995; Rauchet al., 1997). It is thought that the role of the striatum inmotor skill learning may be directly related to the sequenc-ing of particular motor operations, whereas the frontalcortices may be involved in the formation of new motorprograms (Willingham, 1998).

Given that PD and HD overtly affect motor control, it isperhaps not surprising that they result in deficits of motorskill learning. However, these diseases also impair theacquisition of nonmotor skills, including perceptual skills,such as mirror reading (Martone, Butters, Payne, Becker, &Sax, 1984; Roncacci, Troisi, Carlesimo, Nocentini, & Calta-girone, 1996), and cognitive skills, such as the Tower ofHanoi task (Saint-Cyr, Taylor, & Lang, 1988). These find-ings demonstrate that the involvement of the striatum in skilllearning cannot be strictly related to motor processes,suggesting instead that it plays a more general role in theacquisition of novel skills. To date, however, there is noevidence that the striatum is involved in normal nonmotorskill learning in general or in cognitive skill learning inparticular.

Probabilistic Classification LearningOne cognitive skill of particular interest is probabilistic

classification learning. In this paradigm, objects are pre-sented for classification and participants are given probabilis-tic feedback on their classification decisions. For example,one version of this task is a weather prediction task(Knowlton, Squire, & Gluck, 1994), in which participantsare presented with sets of cards and asked to decide whethereach particular set of cards predicts an outcome of rain orsunshine. The relationship between cues and outcomes isprobabilistic; for example, the participant could be told on60% of trials that a certain set of cards predicts rain, whilebeing told on the other 40% of trials that the same set of

564

STRIATAL ACTIVATION IN SKILL LEARNING 565

cards predicts sunshine. Because of this probabilistic cue-outcome relationship, declarative memory for individualtrials may be counterproductive early in learning untilenough instances are accrued to support accurate perfor-mance. A more appropriate strategy is to average thestrength of each cue over a number of trials.

Patients with HD and PD were impaired at learning toperform probabilistic classification (Knowlton, Mangels, &Squire, 1996; Knowlton, Squire, et al., 1996), suggestingthat the striatum is essential for the formation of novelprobabilistic categories. Patients with frontal lesions, on theother hand, learned the task normally (Knowlton, Mangels,et al., 1996). Amnesic patients with MTL lesions exhibitednormal learning for the first 50 trials but then fell behindtheir controls (Knowlton et al., 1994), demonstrating that theMTL is important for later learning. These findings suggestthat striatally based procedural memory guides initial proba-bilistic learning but that hippocampally mediated declarativememory becomes involved at a later stage of skilledperformance. Consistent with this account, patients with PD(who are thought to have normal declarative memory) didlearn to perform the probabilistic classification task aftermore than 100 trials, suggesting that they came to usedeclarative memory to perform the task after extensivetraining (Knowlton, 1999).

We used fMRI to examine brain activation as participantslearned to perform the weather prediction task as describedby Knowlton et al. (1994). We compared the weatherprediction task with a perceptual-motor baseline task thatwas matched for motor and perceptual demands but did notintroduce any demands for learning or complex cognitiveprocessing. Across four scans, 96 weather prediction trialswere performed, providing the opportunity to analyze thechanges in neural activity that accompanied the shift be-tween nondeclarative and declarative memory that has beenproposed on the basis of neuropsychological data.

Most studies of skill learning have focused on regions thatexhibited increasing or decreasing neural activity withpractice (see, e.g., Grafton et al., 1995; Poldrack, Desmond,Glover, & Gabrieli, 1998). The assumption underlying thisanalysis is that changes in the activity of particular brainregions likely reflect neural plasticity associated with learn-ing. In the present study, we identified regions that exhibitedsignificant linear changes in activity across learning andinterpreted those increases and decreases in terms of changesin the organization of underlying cognitive processes. How-ever, regions that exhibit constant activation across trainingmay also be important in learning. Learning may beassociated with changes in neural activity within a particularnetwork, such as synchronization of timing of neuronalfiring, that do not result in changes in the level of overallsynaptic activity (and thus do not result in changes in fMRIor PET signal). In addition, regions that were initiallynecessary for performance of a particular task may remainactive even after they are no longer necessary for perfor-mance. For these reasons, we also examined regions thatexhibited constant activity across training, focusing particu-larly on those regions that are known from neuropsychologi-

cal studies to be necessary for learning of probabilisticclassification (i.e., the striatum).

Method

Participants

Eight normal right-handed adults (age range 19-43 years)participated in the experiment. Informed consent was obtainedfrom all participants prior to the fMRI scan.

Task

The task procedure followed that used by Knowlton et al. (1994).During each fMRI scan lasting 345.6 s, the task alternated for sixcycles between weather prediction, in which the participantspressed an optical switch if they thought the stimulus signified rain,and a perceptual-motor baseline task, in which the participantspressed an optical switch if two cards appeared on the screen (seeFigure 1). For the weather prediction task, participants wereinstructed that they should press the button whenever the cardssignified rain, guessing at the outset but using the feedback on eachtrial to learn which cards signified rain. Probabilities of each cuecombination followed those used by Knowlton et al. (1994) and arepresented in the Appendix.

Each individual trial lasted 7.2 s, and each of the four scansincluded 24 weather prediction and 24 baseline trials. The stimuluscards were presented with the instruction PRESS FOR RAIN in theweather prediction task and PRESS FOR TWO in the baseline task.After 2 s of presentation, the instructions flashed for 3 s; partici-pants were instructed that the flashing stimulus indicated that aresponse should be made. Following the flashing stimulus, feed-back was presented for 1.7 s in the form of the words RAIN orSUNSHINE for the weather prediction task and TWO or NOT TWO forthe baseline task, followed by a 0.5 s interstimulus interval. Taskorder (baseline or classification first) within scans and stimulus list(1-4) within scans were counterbalanced across participants.

Weather PredictionTask

PRESS FOR RAIN

PRESS FOR RAIN

Perceptual-MotorBaseline Task

PRESS FOR TWO

2 sees

PRESS FOR TWO

mvm 3 sees(flashing)

RAIN TWO

1.7 sees

Figure 1. Schematic of individual trial design for weatherprediction task. On each trial, either one, two, or three cardsappeared, with the average number of cards equalized between theclassification and control tasks; each particular card always ap-peared in a consistent spatial location. Fourteen different combina-tions of cards appeared across trials, with probability of each cuecombination determined as in Knowlton, Squire, and Gluck (1994).Participants responded by pressing an optical button for stimulithat they thought predicted rain and withholding responses other-wise; they were not told that the cue-outcome relationship wasprobabilistic.

566 POLDRACK, PRABHAKARAN, SEGER, AND GABRIELI

Imaging

Sixteen slices (20 cm field of view, 4 nun slice thickness, 1 mmskip, 2.2 mm inplane resolution) were imaged at a 25° angle to theaxial AC-PC line (see Figure 2). This resulted in coverage of theentire brain with the exception of the superior sections of theparietal cortex and the orbital frontal regions that are oftencompromised due to magnetic susceptibility. Tl-weighted spin-echo structural images were first collected for the inplane slices.Sixteen functional images were acquired every 2.88 s using aT2*-weighted gradient-echo spiral pulse sequence (Glover & Lai,1998) with parameters of repetition time (TR) = 1,440 ms, echotime (TE) = 40 ms, flip angle = 80°, and two spiral interleaves. Atotal of 120 image volumes were collected during each scan. Acustom quadrature receive-only birdcage head coil was used. Headmotion was minimized using a bite-bar formed to the participant's

dental impression; residual motion was corrected using an auto-mated registration algorithm (Woods, Cherry, & Mazziotta, 1992).

Data Analysis

Data were analyzed using SPM96 (Wellcome Department ofCognitive Neurology, London, England) implemented in MAT-LAB (Mathworks, Inc., Sherborn, MA). Following reconstructionand motion correction (Woods et al., 1992), we normalized allimages into a standard space (Talairach & Tournoux, 1988) byinitially rotating each image by 25° (to approximate the AC-PCalignment of the template) and then aligning each image to anormalized template using an 8-parameter linear transformation.Normalized images were then smoothed using a 10-mm full widthat half-maximum Gaussian kernel, and normalized images were

Figure 2. Depiction of image slice location from a single participant.

STRIATAL ACTIVATION IN SKILL LEARNING 567

1800-1 1• 1600- ^K• 1400- •

Illll

0.9-killBlock

Figure 3. Behavioral data for weather prediction task. Error barsrepresent within-subject standard error.

averaged across participants; a 10-mm kernel was used to ensurethe assumptions of the Gaussian field theory, which requires thatsmoothing approximates twice the voxel dimensions. After specify-ing the appropriate design matrix, we estimated the condition andcovariate effects according to the general linear model at each andevery voxel; the design matrix included global signal intensity as aconfounding covariate, and this analysis can therefore be regardedas an analysis of covariance (Friston et al., 1990). To testhypotheses about regionally specific condition effects, we com-pared the estimates using linear contrasts for either main effects(condition vs. baseline across all blocks) reflecting constant activityacross learning or interaction effects (Time X Condition interac-tions) reflecting changes in activity during learning. The resultingset of voxel values for each contrast constituted a statisticalparametric map, which was transformed to the unit normaldistribution and thresholded at p = .001 (uncorrected). Thestatistical significance of activation maps was then characterized

using distributional approximations from the theory of Gaussianfields (Friston, Jezzard, & Turner, 1994) in terms of the probabilitythat an observed peak height and cluster size could have occurredby chance across the entire imaged volume (i.e., a corrected pvalue).

ResultsBehavioral Performance

Behavioral data are presented in Figure 3. Classificationperformance (consistency of responses with the task prob-abilities) improved significantly across the four scans; lineartrend F(\, 7) = 5.81, p < .05. All participants performednumerically above chance by the final scan (percentagecorrect ranged from 57% to 90% across participants).Response time decreased with training across the four scans;linear trend F( 1,7) = 20.85, p < .001.

Imaging

Figures 4 and 5 present brain regions where significantactivation was found for probabilistic classification, andTables 1 and 2 present stereotactic locations of significantactivations and deactivations, respectively. Regions signifi-cantly more active (corrected p < .05) during classificationthan during the baseline task (main effect across all blocks)included bilateral dorsolateral and inferior prefrontal cortex,

Figure 4. A: Areas of significant activation during the weather prediction task across all scans(p < .05, corrected for multiple comparisons), rendered onto a standard brain. B: Areas of significantdeactivation across all scans during the weather prediction task.

568 POLDRACK, PRABHAKARAN, SEGER, AND GABRIELI

Figure 5. Regions of significantly greater (in red-yellow color scale) or less activity (in blue-whitecolor scale) during weather prediction relative to the perceptual-motor control condition (p < .001,unconnected) for selected slices projected onto averaged Tl -weighted structural images (labeled withTalairach Z coordinate). Activation is evident bilaterally in the cerebellum (Z = -28), bilateraloccipital cortex, inferior frontal cortex, and anterior frontal cortex (Z = -16, Z = -8, and Z = 4),caudate nucleus (Z = 4; noted with Arrow B), dorsolateral prefrontal cortex (Z = 16), and medialoccipital cortex (Z = 4 and Z = 16). Deactivation is evident bilaterally in the medial temporal(Z = -28 through Z = -8; noted with Arrow A) and lateral temporal lobes (Z = —28 throughZ = 4) and parietotemporal cortex (Z = 16), as well as in the medial frontal cortex (Z = —16through Z = 16) and posterior cingulate cortex (Z = 16).

medial frontal cortex, bilateral anterior prefrontal cortex,bilateral occipital cortex, and the right caudate nucleus (seeFigure 4, left panel; Figure 5 in red).

Several regions were deactivated during classificationrelative to baseline, including left MTL, posterior cingulategyms, and bilateral superior temporal gyri (see Figure 4,right panel; Figure 5 in blue; Table 2). The deactivation ofthe MTL was examined in individual participants to confirmits localization. Seven of the 8 participants exhibited signifi-cantly deactivated pixels in the MTL (p < .001, uncor-

Table 1Locations of Significant Activations During ClassificationTask Performance Across All Blocks (p < .05 Correctedfor Multiple Comparisons)

LocationMedial frontal gyrusR inf frontal gyrus (B A 45/46)R mid frontal gyrus (BA 9)L mid frontal gyrus (B A 9/46)

L mid frontal gyrus (BA 10)L mid-sup frontal gyrus (BA 46/10)L fusiform gyrus (BA 18/19)R fusiform gyrus (BA 18/19)Precuneus (BA 31)L supramarginal gyrus (BA 39/40)L inf parietal (B A 40)R caudate nucleusPost cingulate (BA 23/31)

X-25040

-54-46-44-36-36-44

324

-40-50

64

Y2826102444

6054

-76-74-68-48-36

2-22

Z361636244028-8

4-12-12

2832360

20

Max.Z8.728.568.458.528.127.907.077.558.508.247.957.795.145.185.10

reeled); 6 of these exhibited deactivated pixels in the lefthippocampus, 5 exhibited deactivated pixels in the righthippocampus, and 2 exhibited additional deactivated pixelsin the medial temporal (parahippocampal or perirhinal)cortices. Analysis of the time course of this deactivationacross participants showed that the fMRI signal from the leftMTL decreased from Scan 1 to Scan 2 and then increasedfrom Scan 2 through Scan 4 (see Figure 4).

Learning-related changes were examined using lineartrend analyses across the four scans with a threshold of p <.001 (uncorrected); locations of significant learning-relateddecreases and increases are presented in Tables 3 and 4,respectively. Significant linear increases in activation for theclassification task across the four scans occurred in the right

Table 2Locations of Significant Deactivations DuringClassification Task Performance Across All Blocks(p < .05 Corrected for Multiple Comparisons)

LocationL post cingulate (BA 31 )R ant cingulateL medial frontal gyrus (BA 11)L hippocampus

L inf temporal gyrus (BA 21)R mid temporal gyrus (BA 22/39)

L mid temporal gyrus (BA 22/39)

X-10

2-10-26-24-68

5450

-60-52

Y-50-646

-18-10-6

-62-70-64-68

Z2840

-16-20-16-16

20162024

Max.Z8.538.268.207.727.397.376.665.786.445.54

Note. Max. = maximum; R = right; inf = inferior; BA =Brodmann's area; mid = middle; L = left; sup = superior.

Note. Max. = maximum; L = left; BA = Brodmann's area; R =right; ant = anterior; inf = inferior; mid = middle.

STRIATAL ACTIVATION IN SKILL LEARNING 569

Table 3Locations of Significant Learning-Related Decreasesin Activation (p < .001, Uncorrected)

Table 4Locations of Significant Learning-Related Increasesin Activation (p <.001, Uncorrected)

LocationMedial frontal gyrus (BA 10)Ant cingulate-corpus callosumOrbitofrontal cortex

L mid frontal gyrus (B A 47/1 0)R inf occipital gyrus (BA 18/19)

X2

-2-18

010

-3236

Y602632524654

-80

Z80

-8-12

00

-4-12

Max. Z3.863.703.583.513.343.693.48

Note. Max. = maximum; BA = Brodmann's area; Ant =anterior; L = left; mid = middle; R = right; inf = inferior.

inferior prefrontal cortex (see Figure 6), bilaterally in theinferior parietal cortex, posterior cingulate, and right insula.Significant linear decreases in activation across the fourscans occurred in the medial prefrontal cortex, orbitofrontalcortex, left dorsolateral prefrontal cortex (DLPFC), and rightoccipital cortex. The caudate nucleus exhibited a slightdecrease in activation across the four training blocks (Figure4), but this decrease was not significant (linear trendp > .25).

Discussion

The present study examined brain activation during thelearning of a probabilistic classification skill. A network ofbrain regions, including bilateral frontal and parietal cortexand right caudate nucleus, was active during probabilisticclassification. The involvement of several brain regionschanged as learning progressed, with learning-related in-creases in the right frontal cortex and bilateral parietal cortexand learning-related decreases in the occipital cortex and leftprefrontal cortex. The MTL exhibited deactivation initially,but this deactivation subsided in the later stages of perfor-mance. These findings are the first demonstration of brainregions involved in cognitive skill learning and are consis-tent with the neuropsychological data in demonstratinginvolvement of the striatum during learning of the probabilis-tic classification task.

Process-Switching as a Learning Mechanism

The present findings are consistent with a number of otherrecent studies suggesting that skill learning involves changesin which neural networks are involved in performance.Studies of perceptual learning (Poldrack, Desmond, et al.,1998) and motor learning (Sakai et al., 1998; van Mier,Tempel, Perlmutter, Raichle, & Petersen, 1998) have foundshifts in activation between regions active during naive andskilled performance. These findings are consistent withpsychological theories of learning that posit changes be-tween different cognitive processes as learning progresses(e.g., Logan, 1988).

The identity of the regions exhibiting learning-relatedchanges in the present task suggests that learning may haveinvolved a shift from reasoning and memory encoding tomemory retrieval. Early in learning, performance may use

LocationR mid frontal gyrus (BA 10/46)L inf parietal (B A 40)R inf frontal gyrus (BA 45)R inf parietal (B A 40)L insulaPost cingulate-corpus callosum

X42

-48-70

5036

2

Y46

-1820

-26-6

-26

Z4

321236

0O

24

Max. Z4.173.873.433.643.493.46

Note. Max. = maximum; R = right; mid = middle; BA =Brodmann's area; L = left; inf = inferior.

effortful general-purpose reasoning processes (which alsohave substantial working memory demands) to compute aproblem solution. These early experiences are encoded intomemory, and later performance may be based on directretrieval of these previous solutions. The learning-relateddecrease in the left inferior prefrontal cortex observed in thepresent task occurred in a region associated with semanticprocessing and memory encoding (see, e.g., Gabrieli, Des-mond, Demb, & Wagner, 1996; Kapur, 1994); decreasedreliance on memory encoding may underlie the observeddecrease in activation. Conversely, an increased reliance onmemory retrieval is consistent with the observed increase inright frontal activation, which occurred in a region that hasbeen associated with declarative memory retrieval (see, e.g.,Buckner et al., 1995; Squire et al., 1992), particularly withstrategic control of memory retrieval (Wagner et al., 1998).

The Role of the Striatum in Skill LearningThe skill learning impairments observed on relatively

disparate motor and nonmotor tasks in patients with striataldysfunction suggest that the striatum may play a general rolein mediating the neural changes that occur during skilllearning. Activation of the striatum in the present study andin another study of nonmotor skill learning (Poldrack, Hsieh,& Gabrieli, 1998) was consistent with such a general role.This general role of the striatum in skill learning may berelated to the process-switching changes that have beenobserved in imaging studies. Skill learning impairmentsassociated with PD and HD may thus arise not from a lack ofplasticity in the individual neural networks involved inperforming a task but rather from an inability to optimize theparticipation of various neural networks in service of taskperformance. The striatum could thus serve as a neuralsubstrate for process-switching. A similar account of processcoordination, known as contention scheduling, was pro-posed by Norman and Shallice (1986), who suggested thatthe basal ganglia might mediate this process.

Although the connection between brain imaging resultsand neurophysiology remains tenuous, current theories ofcorticostriatal function are also consistent with the notionthat the basal ganglia may play a role in coordinating theinfluence of multiple neural processes. Widespread corticalregions (including the frontal cortex) project to striatal spinyneurons, which in turn project (via the thalamus) back to thesame region of the frontal cortex that innervated those

570 POLDRACK, PRABHAKARAN, SEGER, AND GABRIELI

-•- L Medial Temporal-•- R Caudate-*- R Frontal

Scan (24 trials each)

Figure 6. Percentage change in functional magnetic resonance imaging (fMRI) signal betweenweather prediction and the perceptual-motor baseline task for three pixel clusters: left (L) medialtemporal lobe (centered at —32, —16, -16), right (R) inferior frontal (centered at 42,46,4), and rightcaudate nucleus (centered at 9, 15, 4). Positive values represent activation, and negative valuesrepresent deactivation. Each cluster included 125 voxels extending in three dimensions (10 X 10 X 20mm) from the center coordinate. Error bars denote the standard error of the mean across participants.

striatal neurons. The output of these striatal neurons, andthus the activity of entire corticostriatal loops, is modulatedby a dopaminergic system that provides a reinforcementsignal for striatal neurons. Houk and Wise (1995) haveargued that these features of corticostriatal organizationallow the striatum to alter the dynamics of cortical process-ing and that Hebbian learning mechanisms in the cortex canserve to consolidate these alterations in the cortex. Thisaccount suggests that the role of the striatum in skill learningmay be analogous to the role proposed for the hippocampusin declarative memory: It serves to maintain cortical repre-sentations until they can be ingrained by local corticallearning mechanisms (cf. Gabrieli, 1995). This would pre-dict that striatal lesions should have a time-dependent effecton the retention of nonmotor skills, a prediction that to ourknowledge has not been tested. Alternatively, representa-tions underlying skill may be retained permanently in thestriatum, on the basis of long-term plasticity in corticostria-tal synapses.

Differences in the time course of striatal activation acrosstasks may provide additional evidence about the role of thestriatum in skill learning. In the present study, the caudatenucleus was active during initial performance, and itsactivity decreased slightly (but not significantly) with learn-ing of the task. This time course is consistent with theimpairment observed in patients with PD or HD, who areimpaired early in task performance. Constant activation ofthe striatum during learning has been found on other tasksthat are known to require the striatum for learning, such asrotary pursuit (Grafton et al., 1992). Other studies havefound increasing striatal activation during skill learning. Forexample, a study of skill learning in the mirror-reading task(Poldrack, Hsieh, et al., 1998) found that caudate activationwas initially weak but increased as participants acquired theskill. This time course is consistent with the finding thatpatients with HD exhibit impairments later in training butlearn relatively normally early in training (Martone et al.,1984). Studies of sequential motor learning have also foundincreasing activation of the striatum during skill learning

(see, e.g., Grafton et al., 1995). The particular nature of thetask may determine the time course of striatal involvement;further studies are necessary to determine exactly which taskfactors determine how striatal activation changes withlearning.

Role of Frontal-Striatal Networks in Skill Learning

It is noteworthy that the right DLPFC and striatum areactivated during a wide range of cognitive tasks. The rightDLPFC is active during a number of problem-solving orreasoning tasks in addition to the present task, such asRaven's Progressive Matrices (Prabhakaran, Smith, Des-mond, Glover, & Gabrieli, 1997), Tower of London (Baker,Rogers, et al., 1996), and conceptual reasoning (Rao et al.,1997). It is also active during working memory tasksinvolving both verbal and nonverbal materials (see Owen,1997, for review). Right frontal lesions impair performanceon reasoning tasks, such as Tower of Hanoi (Morris, Miotto,Feigenbaum, Bullock, & Polkey, 1997), and on workingmemory tasks (see, e.g., Bechara, Damasio, Tranel, &Anderson, 1998). The involvement of the right DLPFC onparticular working memory tasks seems tied more directly totask difficulty than to the nature of the materials; forexample, Klingberg, O'Sullivan, and Roland (1997) foundthat performance of a simple delayed matching task withpatterns (matching for pattern or color) activated only theleft DLPFC, whereas a complex delayed matching task(alternating between matching for pattern and color) alsoactivated the right DLPFC.

The right striatum, specifically the right caudate nucleus,has also been associated with working memory, though lessconsistently than the DLPFC has. Braver et al. (1997)demonstrated right caudate activation that was directlyrelated to working memory load for letters, and other studieshave found similar right caudate activation related to verbalworking memory (see, e.g., Rypma, Prabhakaran, Desmond,Glover, & Gabrieli, 1999). The right striatum is also activeduring some reasoning tasks; for example, Rao et al. (1997)

STRIATAL ACTIVATION IN SKILL LEARNING 571

found activation of the right striatum during a conceptualreasoning task similar to the Wisconsin Card-Sorting Task.These findings suggest that a right frontal-striatal network isengaged not only for reasoning tasks but whenever workingmemory is seriously taxed.

It is possible, therefore, to conceptualize of the role of thefronto-striatal network in skill learning as a function of itsrole in working memory. It is often the case that tasks inwhich participants exhibit skill learning have significantworking memory requirements early in performance. Thesedemands arise from the fact that higher order representationsof the task (which allow chunking and thus reduce workingmemory demands) do not yet exist. As the skill is acquired,the newly formed representations allow a greater amount oforganization to be placed on task elements, thus reducingworking memory demands. In this account, skill learningdeficits arise following fronto-striatal dysfunction becausethe participant is unable to perform the on-line manipulationof information necessary to construct an efficient andcompact representation that can guide later performance.

Dissociable Roles of Frontal Cortex and StriatumAlthough the frontal cortex and striatum form an inte-

grated network, several findings by Owen and colleagues(Owen et al., 1993, 1995) suggest that these structures mayplay dissociable roles in cognitive processing. In one study(Owen et al., 1993), patients with PD and frontal lesionswere tested on a version of the Wisconsin Card-Sorting Taskthat separately tested perseveration of previously learnedrules and "learned irrelevance" of previously irrelevantrules. Whereas patients with frontal lesions exhibited in-creased perseveration, medicated PD patients exhibitedincreased difficulty in attending to a previously irrelevantstimulus dimension (i.e., learned irrelevance). Owen et al.(1995) examined performance of PD and frontal lesionedgroups on a version of the Tower of London task, whichrequires planning a series of token manipulations. Whereasfrontal patients exhibited impaired accuracy but normalspeed, medicated early stage PD patients exhibited theopposite pattern of performance. In addition, in testing on aspatial working memory task for which both frontal and PDpatients were impaired, frontal patients exhibited significantperseveration whereas PD patients did not. These datasuggest that the striatum and frontal cortex may play relatedbut fundamentally different cognitive roles.

Whereas many studies have examined skill learning inpatients with PD and HD, little work has directly examinedthe role of the frontal cortex in skill learning. Those studiesthat have examined this question have found skill learning tobe normal following frontal lesions. Knowlton, Mangels, etal. (1996) examined patients with frontal lesions on theweather prediction task and found frontal patients to beunimpaired at learning the task (although HD patients wereimpaired). Intact learning of perceptual skill (mirror read-ing) in patients with frontal lesions was also found by Daumet al. (1995). Another set of studies by Doyon and colleagues(Doyon et ah, 1997, 1998) found that patients with frontallesions were unimpaired at learning on a serial reaction timetask, in contrast with patients with PD or cerebellar lesions,

who were impaired at learning on this task. These datasuggest that the frontal cortex is not critical for the acquisi-tion of skills to the same extent as the striatum. However,each of the studies discussed above tested patients withdorsolateral frontal lesions, and the possibility remains thatlesions to other frontal regions might impair skill learning onparticular tasks.

Medial Temporal Deactivation

Examination of the time course of left hippocampalactivity indicated that left MTL activity was depressed earlyin training and returned to baseline later in training. It isunlikely that this effect represents activation by the baselinetask, given its relative ease and the lack of any learning ormemory demands. One possible explanation for this hippo-campal deactivation may reflect active suppression of thisstructure by other brain regions during performance of theclassification task. Since declarative retrieval of specificprevious trials can impede performance early in training(because the cue-outcome relationship is probabilistic),brain regions supporting performance may have suppresseddeclarative memory retrieval early in training and thenfound less need to suppress declarative retrieval later intraining (after the larger number of memory traces came toaccurately reflect the cue-response probabilities). Thus,MTL suppression in normal participants may explain whyamnesic patients with MTL lesions were unimpaired in earlylearning (Knowlton et al., 1994).

The notion of active competition between declarativememory and habit systems is consistent with the evidencefrom studies finding enhanced habit learning in animals withfornix lesions. Using a win-stay maze task in which ratsmust learn to enter a lit maze arm for reward, Packard, Hirsh,and White (1989) found that rats with caudate lesions wereimpaired at learning the task. However, animals with fornixlesions (which interrupt hippocampal output) exhibitedenhanced learning of the win-stay task relative to controlanimals. Similarly, Eichenbaum, Pagan, Mathews, and Co-hen (1988) found that rats with fornix lesions exhibitedenhanced learning of an olfactory discrimination task rela-tive to controls. These data are consistent with the proposalthat the declarative and nondeclarative memory systemsmay compete with one another and that elimination of onesystem may allow the other system to learn more readily.This enhancement of learning is not necessarily for the best,however; the memory representations that are acquired bynondeclarative memory systems are less flexible than thoseacquired by declarative memory and may not supportperformance across the same range of task situations (Cohen& Eichenbaum, 1993; Eichenbaum et al., 1988).

The deactivation of the MTL in the present study must bereconciled with the fact that normal participants seemed torely on declarative memory after 50 trials in the weatherprediction task (Knowlton et al., 1994). This later relianceupon declarative memory retrieval suggests that declarativememory encoding must have occurred during the periodwhen the MTL was deactivated. Recent imaging studies ofmemory processing suggest that the anterior region of theMTL is involved in memory retrieval, whereas the posterior

572 POLDRACK, PRABHAKARAN, SEGER, AND GABRIELI

region is involved in memory encoding (Gabrieli, Brewer,Desmond, & Glover, 1997). Given that the deactivationobserved in the present study occurred in the anterior portionof the MTL, it is possible that this deactivation could reflectspecific suppression of neural processes related to memoryretrieval that allowed encoding of ongoing events intomemory to occur normally. Consistent with this account is afinding by Reber, Knowlton, and Squire (1996), whoexamined the nature of knowledge acquired during learningin the weather prediction task in amnesic patients andnonamnesic controls. Amnesic patients and control partici-pants exhibited similar learning over 50 trials, but controlparticipants exhibited greater declarative knowledge on atransfer test that examined flexible expression of knowledgeabout cue-outcome relations. This suggests that declarativeknowledge is being acquired during early learning but doesnot influence performance on the task during this stage oflearning.

A related account for the MTL deactivation in the presentstudy is that the use of working memory may result indeactivation of the MTL. Although many studies of workingmemory have not found MTL deactivation, it has beenreported in some tasks; a number of studies do not reportdeactivations, so it is difficult to know how widespread thisfinding is. Klingberg (1998) found bilateral MTL deactiva-tion during concurrent performance of auditory and visualworking memory tasks. Baker, Frith, et al. (1996) founddecreases in MTL activation with PET during the perfor-mance of a delayed matching task that required maintenanceof a rectangular shape in memory and suggested that thisdeactivation may have reflected a direction of attention fromthe retrieval of long-term memory to the maintenance ofinformation in working memory. A PET study of the Towerof London (Baker, Rogers, et al., 1996), a game that requiresplanning a sequence of moves, also found hippocampaldeactivation for difficult problems (which are workingmemory intensive) relative to easy problems (which are lessdemanding of working memory).

One possible interpretation of these results is that deacti-vation of the MTL may reflect a general phenomenon inwhich intensive attention to ongoing cognitive processesresults in a suppression of conscious retrieval of pastexperiences. A recent finding of reduced MTL metabolicactivity during free exploration in rats (Uecker, Barnes,McNaughton, & Reiman, 1997) suggests that the MTL maybe suppressed more generally whenever an organism isengaged in attentive processing. However, other findings ofboth increased MTL activation in humans during attentivedeclarative memory processing (Gabrieli et al., 1997; Schac-ter, Alpert, Savage, Rauch, & Albert, 1996) and the lack ofMTL deactivation across several studies of working memoryargue against the notion that MTL suppression is a phenom-enon generally associated with either attentional processingor working memory. Further experimentation is necessary tomore fully understand the conditions under which the MTLis deactivated. In any case, the present study supports afundamental distinction between a medial-temporal declara-tive memory system and a frontal-striatal procedural (orhabit) memory system (Cohen & Eichenbaum, 1993; Knowl-ton, Mangels, et al., 1996; Mishkin, Malamut, & Bacheva-

lier, 1984) and shows for the first time the normal involve-ment of the striatum in cognitive skill learning.

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Appendix

Trial Probabilities for Weather Prediction Taskfor Each Cue Combination

Cues0000000011111111

0000111100001111

0011001100110011

0101010101010101

P (pattern)

0.1400.0840.0870.0840.0640.0470.0410.1400.0580.0640.0320.0870.0320.041

—

P (rain)

0.150.380.100.620.180.500.210.850.500.820.430.900.570.79—

Note. Combinations (0,0,0,0) and (1,1,1,1) did not occur in thestimulus set. P = probability.

Received September 9, 1998Revision received April 7, 1999

Accepted April 8, 1999