Hemispheric asymmetries and individual di�erences in visualconcept learning as measured by functional MRI

Carol A. Segera,*, Russell A. Poldrackb, Vivek Prabhakaranb, Margaret Zhaob,Gary H. Gloverb, John D.E. Gabrielib

aDepartment of Psychology, Colorado State University, Fort Collins, CO 80523, USAbDepartments of Psychology and Radiology, and Program in Neurosciences, Stanford University, Stanford, CA 94305, USA

Received 17 December 1998; received in revised form 19 May 1999; accepted 26 October 1999

Abstract

Dynamic changes in brain regions active while learning novel visual concepts were examined in humans using functionalmagnetic resonance imaging. Participants learned to distinguish between exemplars of two categories, formed as distortions ofdi�erent unseen prototype stimuli. Regions of the right hemisphere (dorsolateral prefrontal and inferior parietal areas) wereactive early in learning and throughout task performance, whereas homologous portions of the left hemisphere were active only

in later stages of learning. Left dorsolateral prefrontal activation was found only in participants who showed superiorconceptual learning. Such a progression from initial right-hemisphere processing of speci®c instances to bilateral activity as left-hemisphere conceptual processes are recruited may underlie the development of many forms of visual knowledge. 7 2000

Elsevier Science Ltd. All rights reserved.

Keywords: Concept learning; Hemispheric asymmetries; fMRI; Visual learning; Neural plasticity

1. Introduction

Much of human cognition involves interpreting awide range of sensory inputs from the world in termsof a limited number of meaningful, abstracted cat-egories. This interpretation occurs in recognizingobjects and words and in tasks requiring special exper-tise such as diagnosis using X-rays by radiologists andidenti®cation of enemy airplanes by military pilots[22]. For all of these domains of visual knowledge,people must learn through experience how to categor-ize many visual inputs into meaningful categories.Visual knowledge, therefore, involves a transition fromseeing novel instances to classifying such instances intosigni®cant abstract categories. For example, in learning

to recognize the letters of the alphabet, one must learnthat Q, Q, q, Q, q, and Q are all instances of the letterQ, despite their physical di�erences.

The neural basis of such conceptual learning isunknown. Psychological studies presenting stimuli sep-arately to the right and left hemisphere suggest, how-ever, that the two cerebral hemispheres may playdi�erent roles in the development of categorical exper-tise. Studies of children learning to read or learning alanguage with a novel script ®nd a right-hemisphereadvantage at early stages of learning that shifts to aleft-hemisphere advantage as reading becomes skilled[32,33]. Similarly, in musical tasks, novices show aright-hemisphere advantage, whereas expert musiciansshow a left-hemisphere advantage [2]. These behavioralstudies suggest that the right hemisphere specializes inprocessing speci®c stimuli, whereas the left hemispherespecializes in processing patterns abstracted acrossstimuli [13].

Consistent with this specialization for speci®c stim-

Neuropsychologia 38 (2000) 1316±1324

0028-3932/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved.

PII: S0028-3932(00 )00014 -2

www.elsevier.com/locate/neuropsychologia

* Corresponding author. Tel.: +1-970-491-3540; fax: +1-970-491-

2265.

E-mail address: seger@lamar.colostate.edu (C.A. Seger).

uli, behavioral research indicates that the right hemi-sphere performs memory judgments about speci®cvisual items more quickly and accurately than the lefthemisphere [20,21]. The right hemisphere also is moreresponsive to novel stimuli than the left hemisphere[3]. Conversely, the left hemisphere performs judg-ments about prototypical examples of a visual conceptmore rapidly than the right hemisphere [19].

Behavioral studies can compare the relative speed oraccuracy of entire hemispheres, but cannot elucidatethe speci®c brain areas underlying categorization. Inorder to identify particular brain systems involved inconceptual learning, we studied brain activity whilepeople learned to distinguish between two novel visualconcepts using functional magnetic resonance imaging(fMRI). We used a simple conceptual learning task soas to examine modulation of neural activity during theshift from novice to skilled levels of performance.Stimuli in each of two concepts were distortions oftwo di�erent prototypes. Previous research has shownthat visual concepts formed in this manner are learn-able, and that learning is aided by feedback and lowlevels of distortion from the prototype [10,24]. Theseconditions were employed in order to monitor changesin patterns of brain activation that occurred in a singleimaging session as people acquired novel visual con-cepts. The transition from right-hemisphere dominant

naive performance to left-hemisphere dominant skilledperformance can occur within a single session, as seenin musical [16,36] and visuospatial [17] domains.

2. Method

2.1. Participants

Ten adults (seven men, three women; mean age 29)participated in this study. All participants were righthanded, spoke ¯uent English, and had no neurologicaldisorders. Participants gave informed consent using aform approved by the Stanford University InstitutionalReview Board.

2.2. Materials

The prototype stimuli were taken from Fried andHolyoak ([10], Experiment 1), and are shown in Fig. 1.Each prototype was a pattern of red and blue boxes ina 10 � 10 checkerboard grid. Concept exemplars wereformed by changing the color of each square with a7% probability (considered a low level of distortion byFried and Holyoak). Two lists of 48 exemplars wereformed; each list had 24 Smith and 24 Jones stimuli,randomly ordered with the constraint that there were

Fig. 1. (a)Concept prototype and sample stimuli for the two concepts used in the study. Stimuli were formed by randomly reversing the color of

7% of the square elements. (b) Regular checkerboard stimulus used in baseline trials.

C.A. Seger et al. / Neuropsychologia 38 (2000) 1316±1324 1317

six Smith and six Jones stimuli in each quarter of thelist. No stimulus was repeated within or across lists.

2.3. Procedure

Participants were instructed that they would belearning to distinguish between the paintings of twomodern artists, Smith and Jones. For each paintingthey were to decide which artist had painted it, andwould be given feedback. They were told that at ®rstthey would have to guess, but that eventually theywould know how to classify the items. The participantsdid not view any exemplars before scanning.

Each participant completed two scans, with a brief(approximately 2 min) break between scans. Thestimulus lists were counterbalanced for use in scans 1and 2 across participants. During the scans, stimuliwere presented for 2500 ms, during which participantsresponded by pressing optical switches. There wereseparate switches for Smith and Jones held in di�erenthands. After the stimulus disappeared, feedback (thesingle word `Smith' or `Jones') appeared for 500 ms.There was an intertrial interval of 500 ms. Blocks of12 classi®cation trials alternated with blocks of 12baseline task trials, for a total of four blocks of eachin each scan. Thus across both scans there was a totalof 96 categorization trials; these were broken intoquartiles (four groups of 24) for purposes of analysis.In baseline trials, participants were shown the baselinestimulus (a regular checkerboard, Fig. 1) and ran-domly pressed one of the two response keys. In theplace of feedback, the word `Board' was presented.

2.4. fMRI methods

Imaging was performed with a 1.5 T whole-bodyMRI scanner (GE Medical Systems Signa). A proto-type receive-only whole-head coil was used for signalreception. A bite bar formed with each participant'sdental impression was used to minimize head motion.Stimuli were visually presented by back-projecting theimages, via a magnet-compatible projector, onto ascreen located above the participant's neck. Imageswere viewed from a mirror mounted above the partici-pant's head. Sixteen contiguous 4 mm thick slices wereacquired parallel to the anterior commisure (AC)±pos-terior commisure (PC) plane, extending from 16 mmbelow to 44 mm above the (AC)±(PC) plane. Thus,there was incomplete coverage of inferior portions ofthe cerebellum and temporal lobe, and superior por-tions of the parietal and frontal lobes. T1-weighted¯ow compensated spin-warp anatomy images wereacquired for each of the slices imaged in the functionalscans. A T2�-sensitive gradient echo 3D spiral pulsesequence was used for functional imaging with par-ameters of TE=40 ms, TR=1080 ms, ¯ip angle=22,

four interleaves, and inplane resolution=2.35 �2.35 mm. The interscan interval was 4.32 s.

FMRI data were analyzed for activation related tothe concept learning task using the general linearmodel and the theory of Gaussian ®elds. Image recon-struction was performed o�-line. Functional imageswere motion corrected using an automated registrationalgorithm (AIR 3.0, [42]). Images were normalized to astandard space [38] using an eight parameter lineartransformation implemented in SPM 96 (WelcomeDepartment of Cognitive Neurology, London, UK).Normalized images were smoothed via SPM, using a6.0 mm Gaussian kernel. The fMRI statistics programof SPM 96 was used to estimate the e�ects of con-dition at each voxel according to the general linearmodel [11]. The analysis was entered as an epoch de-sign of the ®xed response/box car form. The designmatrix included global signal intensity as a confound-ing covariate and this analysis can therefore beregarded as an ANCOVA. No temporal derivative andno additional confounding variables were entered. Ahigh pass ®lter with a cut-o� period of 120 s was used.Linear contrasts were used to test hypotheses aboutregionally speci®c e�ects, forming a statistical para-metric map of voxel values for each contrast. Thesemaps were transformed to the unit normal distributionand thresholded at p � 0:001 (uncorrected). Distribu-tional approximations from the theory of Gaussian®elds were used to characterize the statistical signi®-cance of activation maps in terms of the probabilitythat an observed peak height or region size could haveoccurred by chance across the entire imaged volume[12].

3. Results

3.1. Behavioral results

Accuracy of classi®cation was calculated for eachparticipant across quartiles of 24 classi®cation trialseach. A one-way ANOVA indicated that participantsimproved their classi®cation across quartiles,F�3, 27� � 8:5, p < 0:001: Performance improved fromthe ®rst �M � 64%� to the second �M � 82%;t�9� � 3:92, p < 0:005� quartile, but remained constantin the third �M � 79%), and fourth �M � 79%� quar-tiles.

There were robust individual di�erences in classi®-cation performance (Fig. 3a). Six participants masteredthe classi®cation task, reaching a mean ®nal quartileclassi®cation score of 91%. Four participants, how-ever, showed little or no learning; their mean ®nalquartile classi®cation score was 61%. The cut-o�between learners and nonlearners was set at 83% (20out of 24 correct responses) in the ®nal quartile; lear-

C.A. Seger et al. / Neuropsychologia 38 (2000) 1316±13241318

ners ranged between 88 and 100% on the ®nal quar-tile, whereas nonlearners ranged between 50 and 75%.A two-way ANOVA with factors of group (learnersand nonlearners) and quartile found a main e�ect ofgroup, such that learners performed better than non-learners, F�1, 8� � 11:5, p < 0:01, and an interaction ofgroup and quartile, such that the di�erence betweenlearners and nonlearners increased across quartiles,F�3, 24� � 3:59, p < 0:05: Nonlearners did not showimproved performance across blocks: the simple e�ectof quartile for nonlearners was not signi®cant, F < 1:0:Previous categorization learning research has found in-dividual di�erences in learning [1,6], but the reasonsfor these di�erences are not well understood.

Response time decreased across quartiles for bothlearners and nonlearners. A two-way ANOVA withfactors of quartile and group (learners, nonlearners)found a signi®cant e�ect of quartile, F�3, 24� � 6:98,p < 0:005, but no e�ect of group and no interaction.

3.2. Classi®cation> baseline activations

Areas of greater activity during classi®cation thanbaseline across trials are shown in Table 1(a). Therewas prominent activation of right dorsolateral prefron-tal and bilateral parietal regions, areas associated withreasoning and working memory. In addition, acti-vation was found in the bilateral middle occipital cor-tices, bilateral inferior frontal areas, and the anteriorcingulate.

3.3. Learning-related changes in activity

Changes in activity in frontal and parietal brainareas were found across the four quartiles of visual

concept learning (Fig. 2). In the ®rst quartile, classi®-cation performance was poor across participants. Acti-vation in the classi®cation task relative to the baselinetask was limited to right-hemisphere areas of the fron-tal and parietal lobes. Beginning in the second quartile,a high level of classi®cation ability was attained, andleft hemisphere activation was present in addition toright-hemisphere activation. Left parietal activationwas present in quartiles 2±4. A linear contrast in SPMshowed a signi®cant increase in left parietal activationacross quartiles. Left dorsolateral prefrontal activationwas present in quartiles 2 and 3, and was present butdid not reach the threshold of signi®cance in quartile4, which is probably due to insu�cient power attribu-table to the low numbers of participants. The rightfrontal and parietal activity did not increase signi®-cantly across quartiles.

In addition to frontal and parietal areas, activationin both right and left occipital gyri increased acrossquartiles. A two-way ANOVA with factors of quartileand hemisphere (left, right) found a main e�ect ofquartile, F�3, 48� � 3:5, p < 0:05, but no main e�ect ofhemisphere or interaction. The dependent measure wasthe mean signal change at the cluster of 125 pixelsextending in three dimensions �8� 8� 16 mm) fromthe coordinates of the maximum Z score (as given inTable 1).

3.4. Individual di�erences

The performance di�erences in classi®cation betweenlearners and nonlearners were associated with one re-liable brain activation di�erence. The fMRI statisticsprogram (using parameters as described in section 2.3),was used to perform an interaction analysis of brain

Table 1

Areas of (a) classi®cation> baseline activations and (b) baseline> classi®cation activationsa

Region BA k Z x y z Change

(a)Classi®cation>baseline activations

Right prefrontal 9, 46 1690 7.3 48 38 32 =

Left inferior frontal 45, 47 709 5.9 ÿ34 24 0 =

Right inferior frontal 45, 47 661 7.6 34 30 0 =

Left inferior parietal 7 1619 6.2 ÿ26 ÿ74 48 +

Right inferior parietal 7 1998 6.9 20 ÿ74 44 =

Left middle occipital 18 678 6.5 ÿ32 ÿ88 12 +

Right middle occipital 18 806 6.3 32 ÿ80 32 +

Anterior cingulate 32 298 5.0 2 36 40 =

(b) Baseline> classi®cation activations

Bilateral superior, medial frontal 10, 11 3041 7.9 ÿ4 64 ÿ12 ÿBilateral post. cingulate, precuneus 31, 18 1417 6.7 ÿ8 ÿ52 28 +

Right superior temporal 22 2874 6.6 70 ÿ28 0 =

Left superior temporal 22 4452 6.5 ÿ48 ÿ20 4 +

a All areas had a corrected conjoint height and extent probability of p < 0:05: BA: Broadman's area. k: number of voxels. Z: maximum z

score. x, y, z: tailarach coordinates of the maximum z score. Change: direction of change across quartiles. +: increase; ±: decrease; =: no

change.

C.A. Seger et al. / Neuropsychologia 38 (2000) 1316±1324 1319

activation during categorization relative to baselineacross averaged volumes for learners and nonlearners.The left prefrontal cortex �x � ÿ54, y � 30, z � 24;BA 9/46; Fig. 3b) was signi®cantly more active forlearners than nonlearners. As shown in Fig. 3, acti-vation in the left prefrontal cortex for learnersincreased across quartiles, but activation in nonlear-ners remained constant across quartiles.

3.5. Baseline> classi®cation activations

Areas listed in Table 1(b) were less active duringclassi®cation than during the baseline task: the bilat-eral superior temporal gyri, superior and medial fron-

tal areas, and posterior cingulate and precuneus. Thesuperior frontal deactivation decreased across quar-tiles, whereas the precuneus/posterior cingulate deacti-vation increased across quartiles.

4. Discussion

The results presented here delineate a dynamic net-work of neural systems underlying visual concept ac-quisition. In the initial, naive stage of learning, stimulicould only be processed as speci®c visual patterns, andright-hemisphere areas were active, including right pre-frontal and inferior parietal. As learning progressed,

Fig. 2. Areas of activation across quartiles for all participants; each quartile consisted of two blocks of 12 classi®cation trials, and comprised half

of a scan.

C.A. Seger et al. / Neuropsychologia 38 (2000) 1316±13241320

the left parietal area was recruited. Left prefrontal acti-vation was found only in participants with highlydeveloped conceptual knowledge.

Several visual reasoning tasks have found a combi-nation of right prefrontal and bilateral parietal brainactivity. In one study, participants had to decide whichpattern would complete a sequence on the basis ofvisual features [26]. In another, participants classi®edpictures of animals on the basis of a previously learnedverbal rule [35]. In the present study, participantsformed rules to di�erentiate between novel visual pat-terns, then implemented these rules. In all of the tasks,

participants were required to analyze the features of avisual stimulus, decide on a rule that applies to thesefeatures, and make a decision on the basis of this rule.Thus, there is convergent evidence that right prefrontaland bilateral parietal areas are involved in visualreasoning. Furthermore, these areas have also beenshown to be active in visuospatial working memorytasks [14,34]. The consistency of right hemisphere(both frontal and parietal) activity during learningimplies that the right hemisphere plays a role in stimu-lus processing (such as visual working memory, or fea-ture analysis) that is independent of learning. The left

Fig. 3. (a) Percentage correct on the classi®cation task across quartiles for learners and non learners. (b) Areas of di�erential activation in lear-

ners and nonlearners. (c) Percentage change in fMRI signal between categorization and baseline tasks in learners and nonlearners for a pixel clus-

ter in the left dorsolateral prefrontal area. The cluster was centered at �x � ÿ54, y � 30, z � 24� and included 125 pixels extending in three

dimensions �8� 8� 16 mm) from the center coordinate.

C.A. Seger et al. / Neuropsychologia 38 (2000) 1316±1324 1321

parietal activity increased across quartiles, indicating alearning related change, but was not signi®cantly moreactive in learners than in nonlearners. It remains atask for future research to clarify the role that the leftparietal area is playing in classi®cation.

Left dorsolateral frontal activity was found only inparticipants who showed high levels of classi®cationlearning. This ®nding is consistent with research that®nds left frontal activation in analytic problem solvingtasks [25,26], in working memory tasks with verbaland analytic elements [34], and participation of lefthemisphere areas in formal, content-independent,reasoning [41]. The presence of left frontal activationas a marker for classi®cation learning indicates thatanalytic reasoning may be involved in induction ofpattern knowledge. Thus, left frontal areas may play acrucial role in gaining expertise in classi®cation.

What role might the left dorsolateral frontal acti-vation be playing in categorization? One possibility isthat it may re¯ect verbal rule formation. Typically,participants who perform well on this task report noti-cing and utilizing rules about the presence of particularvisual features (e.g., a staircase-like appearance).Another possibility is that it re¯ects other abstractionprocesses. There is much controversy in the conceptlearning literature revolving around whether partici-pants perform abstraction at the point of learning(storing information about the prototypical pattern),or whether participants store individual concept exem-plars and perform abstraction at test by computing thesimilarity of the test stimulus to the previously learnedexemplars [6]. The present results do not provide evi-dence for deciding between either possible represen-tation of conceptual knowledge. The left inferiorfrontal activation could be indicative of either formingand applying an abstract representation, or couldre¯ect the exemplar comparison and decision makingprocess.

It should be noted that the concept learned by par-ticipants in the present study was relatively simple, andmost participants achieved high degrees of classi®-cation accuracy in few trials. However, due to theshort training period participants did not achieve an`automatic' or `overlearned' level of performance. Itremains a topic for future research to explore theneural changes underlying classi®cation expertise afterextended training.

In addition to parietal and dorsolateral frontalareas, greater activation was found in the bilateral in-ferior frontal lobes, anterior cingulate, and bilateraloccipital lobes during classi®cation than during base-line. The bilateral occipital lobe activation increasedacross quartiles, whereas the other activationsremained constant. Bilateral inferior frontal activity inthe area of the frontal operculum is often found inworking memory tasks and is associated with mainten-

ance of information [5]; left opercular activity in par-ticular is associated with verbal rehearsal [9,34]. Theanterior cingulate is active in a wide variety of tasks,and may re¯ect attentional factors related to onlinemonitoring of task performance [4,9,27].

The bilateral occipital lobe activation, whichincreased across quartiles, may be related to visual fea-ture analysis in the extrastriate cortex. In a study ofconcept learning using dot pattern stimuli, Reber et al.[28,29] found greater occipital activation for exemplarsthan for random stimuli when participants made rec-ognition decisions, but less activation for exemplarsthan random stimuli when participants judged whetherstimuli were category members. These results, takentogether, indicate that occipital lobe activation can bealtered dynamically depending on task demands. It isunclear why category membership judgment led togreater activation than baseline in the present study,but lower activation than baseline in the Reber et al.studies and the present study. The key may be thedi�erence between the baseline tasks in the two stu-dies. In the present study, subjects always saw thesame stimulus and made the same response in thebaseline condition, and thus the baseline task requiredvery little feature analysis. In the Reber et al. studyparticipants saw novel random stimuli, with novel fea-tures, which may have required more feature analysisthan the familiar exemplars.

Several areas were less active during classi®cationthan during the baseline task: the bilateral superiortemporal gyri, superior and medial frontal areas, andposterior cingulate and precuneus. Due to the rela-tively simple nature of the baseline task, it is reason-able to interpret these activations as areas that aredeactivated during the classi®cation task. Deactivationin areas related to audition such as the superior tem-poral gyri may re¯ect a suppression of auditory atten-tion during a demanding visual task [31]. The leftsuperior temporal gyrus deactivation increased acrossquartiles, but not the right superior temporal, indicat-ing greater suppression in left auditory areas as learn-ing occurred. The bilateral frontal pole and medialfrontal cortices, and posterior cingulate and precuneuscommonly show lessened activation in more di�cultcognitive tasks relative to baseline tasks. These deacti-vations may re¯ect attention or extraneous thoughtprocesses during the relatively unconstrained baselinetask [30]. The superior frontal deactivation decreasedacross quartiles, whereas the precuneus/posterior cin-gulate deactivation increased across quartiles; it isunknown why these areas should change over trials,and why they should change in opposite directions.

The neuroimaging literature suggests several types oflearning-related changes, or neural plasticity, under-lying learning. The present study found a brain acti-vation associated with successful learning: high levels

C.A. Seger et al. / Neuropsychologia 38 (2000) 1316±13241322

of classi®cation learning were found only in partici-pants who also showed left frontal activation. Otherstudies ®nd other patterns of learning-related changes.In motor learning, Karni et al. [15] found increased ac-tivation in primary motor cortex for a learned ®ngertapping sequence. Conversely, priming studies ®nd arelative decrease in activation for repeated items rela-tive to novel items. Conceptual priming tasks involvedecreases in left inferior frontal cortex involved withconceptual processing [8,27] whereas perceptual prim-ing tasks involve decreases in occipital cortex involvedin visual processing [37]. In addition to local increasesor decreases in brain activation, shifts between brainareas have been found. During learning to read mirrorreversed text there is a shift in activation from rightdorsal visuospatial processing areas to left ventralvisual processing areas [23]. Thus, di�erent types oflearning are associated with di�erent patterns of neuralchange.

The present ®ndings provide a new perspective onparallel processing in the right and left hemispheres.There is a considerable literature indicating that, forwell-known information, the right hemisphere analyzesstimuli in terms of speci®c or metric properties,whereas the left hemisphere analyzes stimuli in termsof abstracted categories. For example, identical stimuliyield a right-hemisphere advantage when requiring ametric or coordinate analysis, but a left-hemisphere ad-vantage when requiring a categorical analysis [17].Commissurotomy patients analyze identical stimulibased on visual form in the right hemisphere, butbased on conceptual associations in the left hemisphere[18]. Patients with right-hemisphere lesions have di�-culty identifying objects presented in unusual views orshadows, where identi®cation depends upon access toa canonical or abstract view [7,39,40]. In contrast,patients with left-hemisphere lesions have di�cultymatching exemplars that have the same abstract mean-ing but di�er in visual details [7,40].

In the present study, the right-hemisphere frontaland parietal activations were present from the begin-ning and persisted throughout learning. They did notdi�er reliably in learners and nonlearners. Thus, theright-hemisphere activations appear to be related toconstant processes of visuospatial analysis of stimulusspeci®cs that are dissociable from categorical learning.Indeed, such right-hemisphere processes would have tobe insulated from categorical knowledge in order tomaintain their focus on stimulus-speci®c properties,and it is to be expected that this focus would be main-tained across the task. Left-hemisphere activationoccurred as participants learned abstract categories ap-plicable to the speci®c stimuli; the left-frontal acti-vations di�ered as a function of successful categorylearning. Thus, the left hemisphere demonstrated cat-egory-learning plasticity through experience. The rapid

educability of the left hemisphere would be useful inallowing people to quickly learn and apply newabstract categories. This study, therefore, reveals a pat-tern of a stable right-hemisphere system for analysis ofvisual speci®cs and a dynamic left-hemisphere systemfor learning visual categories. Such parallel processesin the cerebral hemispheres provide for a powerfulcombination of continuity and adaptive change invisual knowledge.

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