B R A I N R E S E A R C H 1 2 9 9 ( 2 0 0 9 ) 3 3 – 4 4

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Research Report

Intracranial electroencephalography reveals two distinctsimilarity effects during item recognition

Marieke K. van Vugta,⁎, Andreas Schulze-Bonhageb, Robert Sekulerc, Brian Littd,Armin Brandtb, Gordon Baltuche, Michael J. Kahanaf

aCenter for the Study of Brain, Mind and Behavior, Green Hall Princeton University, Princeton, NJ 08540, USAbUniversity Hospital of Freiburg, Freiburg, GermanycVolen Center for Complex Systems, Brandeis University, Waltham, MA, USAdDepartments of Neurology and Bioengineering, University of Pennsylvania, Philadelphia, PA, USAeDepartment of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USAfDepartment of Psychology, University of Pennsylvania, Philadelphia, PA, USA

A R T I C L E I N F O

⁎ Corresponding author. Fax: +1 609 258 2549.E-mail addresses: mkvan@princeton.edu

sekuler@brandeis.edu (R. Sekuler), littb@maibaltuchg@uphs.upenn.edu (G. Baltuch), kaha

0006-8993/$ – see front matter © 2009 Elsevidoi:10.1016/j.brainres.2009.07.016

A B S T R A C T

Article history:Accepted 8 July 2009Available online 16 July 2009

Behavioral studies of visual recognitionmemory indicate that old/new decisions reflect boththe similarity of the probe to the studied items (probe–item similarity) and the similaritiesamong the studied items themselves (list homogeneity). Recording intracranialelectroencephalography from 1,155 electrodes across 15 patients, we examined theoscillatory correlates of probe–item similarity and homogeneity effects in short-termrecognition memory for synthetic faces. Frontal areas show increases in low-frequencyoscillations with both probe–item and item–item similarity, whereas temporal lobe areasshow distinct oscillatory correlates for probe–item similarity and homogeneity in thegamma band. We discuss these frontal low-frequency effects and the dissociation in thetemporal lobe in terms of recent computational models of visual recognition memory.

© 2009 Elsevier B.V. All rights reserved.

Keywords:OscillationRecognition memoryCognitive modelingSimilarity

1. Introduction

Similarity is an important factor in human forgetting, asinterference between items increases when those itemsare similar within a psychological representational space(Robinson, 1927). Although parametric investigations of simi-larity effects in recognition have been widely used in thecognitive literature, the neural correlates of similarity remainlargely unexplored. In visual item recognition tasks, partici-pants view a short sequence of study items, followed by aprobe item.Their task is to indicatewhether theprobematchesone of the study items. Here we examine the neural correlates

(M.K. van Vugt), andreas.l.med.upenn.edu (B. Litt),na@psych.upenn.edu (M.

er B.V. All rights reserved

of two distinct types of similarity effects in visual recognitionmemory. The first of these is participants' increased tendencyto endorse a test probe when it is similar to one or more listitems (e.g., Hintzman, 1988; Lamberts et al., 2003; Nosofsky,1991; Shiffrin and Steyvers, 1997). The second is participants'decreased tendency to endorse a test probewhen the list itemsare very similar to one another (Kahana and Sekuler, 2002;Nosofsky and Kantner, 2006; Visscher et al., 2007).

An influential class of cognitive models describing thisvisual recognition memory process (“summed similaritymodels”) assume that participants base their recognitiondecision on the sum of the similarities between the probe

schulze-bonhage@uniklinik-freiburg.de (A. Schulze-Bonhage),armin.brandt@uniklinik-freiburg.de (A. Brandt),J. Kahana).

.

34 B R A I N R E S E A R C H 1 2 9 9 ( 2 0 0 9 ) 3 3 – 4 4

and study items' memorial representations (Hintzman, 1988;Kahana and Sekuler, 2002; Lamberts et al., 2003; Nosofsky,1991; Nosofsky and Kantner, 2006; Shiffrin and Steyvers, 1997).Similarities can be computed when the multidimensionalrepresentations of the stimuli are known (as derived, forexample, from a multidimensional scaling procedure).Kahana and Sekuler (2002) in their Noisy Ex-emplar Model(NEMo) extended this basic framework by incorporating item–item similarity (the mean of all pairwise similarities betweenstudy items) in the basic model (Kahana and Sekuler, 2002;Sekuler and Kahana, 2008). They found that when the list ishomogeneous (i.e., the list items are highly similar to eachother), the probability of responding “yes" decreases. Visscheret al. (2007) showed that this was still the case when probe–item similarity was held constant. We will therefore refer tothe mean item–item similarity as a “homogeneity correction,”which will tend to push the decision in the opposite directionof probe–item similarity (Viswanathan et al., submitted forpublication). Including this extra term significantly improvedthe model's fit to the data. Probe–item similarity and thehomogeneity correction are formalizations of the two simi-larity types described earlier.

In this paper, we have two main objectives. First, we wantto examine whether oscillatory correlates of probe–itemsimilarity and the homogeneity correction can be found inintracranial electroencephalography (EEG) recordings. Second,we aim to exploit the neural data in a novel way, namely inorder to select among alternative models of the recognitionprocess. In particular, we compare the neural correlates ofprobe–item similarity and the homogeneity correction to seewhether the latter is a useful addition of NEMo to summedsimilarity models.

There are various possible outcomes of such an analysis.We predict that probe–item similarity and the homogeneitycorrection are associated with different neural correlates, sincebehavioral evidence suggests that they involve distinctcomputations (Visscher et al., 2009). It could, however, alsobe the case that probe–item similarity and the homogeneitycorrection are associated with the same neural signatures,which could point to generic mechanisms that sum similarityacross items. Thirdly, if probe–item similarity and the homo-geneity correction have similar neural signatures but areopposite in sign, that would indicate neural processes that areinvolved in computing the sum of probe–item similarity andthe homogeneity correction that forms the basis for therecognition decision (to which probe–item and item–itemsimilarity make opposite contributions). Finally, if the homo-geneity correction were to show little or no correlation withneural activity, this would make it a less plausible addition tothe summed similarity model. If we do not find any neuralcorrelates of NEMo, this would indicate that oscillatory activityis not the prime locus for visual recognition memory.

Whichbrain regionswould be expected to showcorrelates ofthe two similarity signals? There is an extensive literature onthe neural correlates of short-term memory for nonverbalstimuli such as faces, which are the stimuli we used in thisexperiment. For probe–item similarity only, we expected theneural correlates to be similar to differences in neural activityfor targets and lures (“old/new effects”) that have been reportedin the literature. These old/new effects have been found in the

left parietal lobe in scalp EEG (e.g., Curran, 2000; Düzel et al.,2005) and functional magnetic resonance imaging (fMRI)studies (e.g., Kahnet al., 2004;Wagner et al., 2005). Furthermore,targets and lures also showdifferential fMRI blood oxygenationlevel-dependent (BOLD) activity and potentials in intracranialEEG in the dorsolateral prefrontal cortex (DLPFC) and themedialtemporal lobe (MTL; Cabeza et al., 2001; Ludowig et al., 2008,respectively). Given that our focus here is on the integration oftwo similarity signals (probe–item similarity and the homo-geneity correction), we also expected to see correlates of both ofthese signals (probe–item similarity and the homogeneitycorrection) in the frontopolar cortex (Brodmann area 10). Thisis a region that has been associated with the integration ofinformation from multiple sub-tasks (e.g., deciding whethermultiple items share the same property) in working memorytasks (Braver and Bonglolatti, 2002; Koechlin et al., 1999;Koechlin and Hyafil, 2007; Ramnani and Owen, 2004; Reynoldset al., 2006). It also shows increased activation with supraspanmemory loads and during episodicmemory retrieval (Cabeza etal., 1997; Christoff and Gabrieli, 2000).

In addition to judging the plausibility of the model and itsneural correlates on their spatial locations, we will examinetheir location in frequency space. Based on a scalp EEG study(van Vugt et al., submitted for publication), we expect probe–item similarity to correlate with left parietal 4–9 Hz thetaoscillations. Oscillations in both the theta (4–9 Hz) and gamma(28–128 Hz) bands have frequently been associated withmemory and attentional processes (e.g., Osipova et al., 2006;Rizzuto et al., 2006; Sederberg et al., 2007b). If we fail to findoscillatory correlates in BA 10, parietal areas and the MTL, infrequencybands that include thetaandgamma, thenoscillatoryactivity does not play a large role in visual recognitionmemory.

To study the oscillatory correlates of probe–item similarityand the homogeneity correction we tested participants in avisual Sternberg task (Sternberg, 1966). The stimuli consistedof a set of synthetic male faces (Wilson et al., 2002). Theadvantages of using this face set are that, although the facestimuli are well controlled, they still can be identified withhigh accuracy (Wilson et al., 2002), and it is possible tomeasure their inter-item similarity precisely. Moreover, theface stimuli are realistic enough to generate strong responsesin the fusiform face area (Loffler et al., 2005), and we havepreviously shown how their similarity structure affectsrecognition memory performance (van Vugt et al., submittedfor publication) and the learning of name–face associations(Pantelis et al., 2008).

Detecting medial temporal lobe activity and separating itfrom, e.g., themedial temporal gyrus requires excellent spatialresolution, beyond that of scalp EEG. For that reason, we usedintracranial EEG (iEEG) recordings to study the oscillatorycorrelates of probe–item similarity and the homogeneitycorrection. Although we focus our analyses mainly on theabove-mentioned regions of interest, we will also use whole-brain analyses to examine the specificity of the effects.

2. Results

The Noisy Exemplar Model (Kahana and Sekuler, 2002)assumes that participants base their recognition decision

Table 1 –Modeling short-term recognition of faces: root-mean-square deviation (RMSD) and mean (standard errorof mean) NEMo parameters obtained using a geneticalgorithm, fitted for each participant separately. Note thatα1 was fixed to 1.

Parameters Values Parameters Values

β -0.38 (0.13) τ 8.87 (0.89)α2 0.88 (0.021) c1 0.35 (0.13)α3 0.79 (0.040) c2 0.32 (0.15)α4 0.68 (0.027) c3 0.34 (0.13)

c4 0.15 (0.36)σ1 0.068 (0.053) w1 0.088 (0.049)σ2 0.078 (0.051) w2 0.11 (0.058)σ3 0.080 (0.11) w3 0.29 (0.092)σ4 0.16 (0.039) w4 0.50 (0.11)RMSD 0.168 (0.012)

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largely on the summed pairwise similarity between an item'smemorial representation and the representation of the testprobe. Formally, this probe–item similarity is given by:

S =XL

i = 1

aie�sjsi−pj ð1Þ

Here L refers to the length of the study list, αi is a forgettingparameter as described below, τ determines the form of theexponential generalization gradient (Shepard, 1987), si is thevector representing the coordinates of the memorial repre-sentation of stimulus i, and p is the representation of theprobe item. The summed similarity of a target probe to themembers of the study list typically exceeds the summedsimilarity of lure probes to the members of the study list.NEMo extends the basic summed similarity framework foritem recognition (Hintzman, 1988; Nosofsky, 1991) by showingthat the degree of summed similarity required to respond“yes" varies with list homogeneity (i.e., the mean similarityamong list items). With greater list homogeneity, participantswill respond “yes" at lower levels of summed similarity(Kahana et al., 2007; Nosofsky and Kantner, 2006; Visscher etal., 2007). Formally, homogeneity is defined as the averagesimilarity among the L studied items:

H =2b

L L� 1ð ÞXL�1

i = 1

XL

j = i + 1

e�sjsi−sj j ð2Þ

In this equation, β determines the influence of list homo-geneity. According toNEMo, theprobability of responding “yes"is given by:

P yesð Þ = P S +H > cLð ð3ÞHere cL is a separate decision criterion for every list length.Each item's memorial representation, si, is defined by sum-ming the coordinates obtained from an MDS study (Panteliset al., 2008; van Vugt et al., submitted for publication) with anoise vector whose components are zero-mean Gaussianswhose standard deviation (σ) depends on the stimulusdimensions comprising that item. In using this noise term,we can adopt a fully deterministic decision rule (Ashby andMaddox, 1998). Variability in participant's responses from oneoccurrence to another are modeled by the sampling of eachitem from the distribution of its noisy representation si. Tosimulate forgetting, NEMo assumes that the most recentstimulus contributes the most to the summed similarity, andthat earlier items contribute less and less. This is simulated bythe parameter α, which increases with lag (i=1 indicating themost recently studied item). Finally, NEMo allows for thepossibility that participants differentially weight the contri-butions of different stimulus dimensions. These weights,which are not shown in equation (1), also insure that themeasured distances are not sensitive to absolute variations inthe scale of the dimensions.

The model parameters were estimated by using a geneticalgorithm (Mitchell, 1996) to minimize the root-mean-squaredeviation (RMSD) between the observed and predicted binnedaccuracy for each participant. Simulated participants respondyes when S+H>cL. The genetic algorithm was run for 20generations of 2000 individuals each. The obtained estimatesof the best-fitting parameters (Table 1) are similar to those

reported in previous applications of NEMo (Kahana andSekuler, 2002; Kahana et al., 2007; Nosofsky and Kantner,2006; van Vugt et al., submitted for publication; Visscher et al.,2007; Yotsumoto et al., 2007). In particular, the parameter β issignificantly smaller than zero [t(14)= -11.7, p<0.001]. Thisindicates that list homogeneity contributes significantly to theold/new decision.

2.1. Oscillatory correlates of probe–item and item–itemsimilarity

Our main research question was whether probe–item similar-ity and the homogeneity correction have distinct neuralcorrelates and whether these neural correlates supportNEMO. To investigate the oscillatory correlates of these twovariables simultaneously, we used a regression model, whichwe have introduced in previous scalp EEG studies (Jacobs et al.,2006; van Vugt et al., submitted for publication). We regressedoscillatory power on three factors: summed probe–itemsimilarity (S), mean interitem similarity (homogeneity, H),and lag (lag):

Targets model : Pfreq;electrode;time = b0 + b1S + b2H + b3lagT +a ð4Þ

Lures model : Pfreq;electrode;time = b0 + b1S + b2H + b3lagL +a ð5Þ

In these regression models, the lag variable measures howrecently a probe item has been last studied, either on thecurrent list (for targets) or on a previously studied list (forlures). This variable was included to statistically removerecency and proactive interference effects (e.g., McElree andDosher, 1989; Monsell, 1978). Since recency and proactiveinterference were not a focus of the present study, we will notdiscuss oscillatory correlates of the lag variable here.

We computed these regressions separately for eachelectrode, frequency band, and each of 10 successive 100-mstime intervals following probe onset. To avoid confoundingthe oscillatory correlates of probe–item similarity with well-known old/new effects (Düzel et al., 2003), we fit separateregressionmodels to data for targets and lures. The regressioncoefficients were combined in Brodmann areas as described inthe Experimental Procedures (see also Sederberg et al., 2007a).The results are presented in tabular format, in which apositive or negative sign indicates a significantly positive or

36 B R A I N R E S E A R C H 1 2 9 9 ( 2 0 0 9 ) 3 3 – 4 4

negative correlation between oscillatory activity and S or H inat least 1 of the 10 time bins. The number of participantscontributing electrodes to each region are shown in the lasttwo columns of each table. Note that this table shows 18significant results, of which 0.18 on average (i.e., 1%) arespurious according to the FDR procedure.

We will discuss each of the regions of interest in turn: firstthe frontopolar cortex, which, because of its role in informa-tion integration (e.g., Ramnani and Owen, 2004), should showcorrelates of both S and H. After that, we will discuss theDLPFC, the left parietal lobe, and lastly temporal regions. Theresults are presented in Table 2.

In Brodmann area (BA) 10 (the frontopolar cortex), 2–4 Hzdelta oscillations increase with both S and H for targets. For

Table 2 – Overview of significant correlations between probe–itefor lure probes.

“l” indicates left hemisphere, whereas “r” indicates right hemisphere. The lhomogeneity correction. Frontal areas have a blue background, memory-relareashave a green background.Brodmannareas are considered significant icolumns indicate thenumber ofparticipants (Ns) andnumberof electrodes (Nthe gammasubband: 1=28–48Hz; 2=48–90Hz; 3=90–128Hz.Abbreviations: B22=STG; entorh=entorhinal; hipp=hippocampus; BA 7=parietal; BA 39=pa

lures, 9–14 Hz alpha oscillatory power decreases and increaseswith S and H, respectively. Additionally, 90–128 Hz gammaoscillatory power is positively and negatively correlated with Sfor lures and targets, respectively. Note that this effect couldreflect task difficulty because higher probe–item similarity (S)is more difficult for lures and easier for targets.

In addition to the low-frequency integration signals in thefrontopolar cortex, we found similar effects in the DLPFC. Inright BA 46 (part of the DLPFC), 2–4 Hz delta oscillatory powerincreases bothwith S andH. Another region in the DLPFC, BA 9(DLPFC) shows a decrease in 28–48 Hz gamma power with S,which might again reflect difficulty.

Furthermore, we expected to see correlates of S in the leftparietal lobe, a region that has frequently been found to show

m similarity (S) and homogeneity (H) and oscillatory power

eft column represents probe–item similarity and the right column theated areas a yellow background, left parietal areas are pink, andmotorf they showa significant correlation inat least one timebin. The last two

e) in eachBrodmannarea. For thegammaband, the subscript indicatesA10= frontopolar cortex; BA46=DLPFC;BA9=DLPFC;MA21=MTG; BArietal; BA 40=parietal; BA 4/6=(pre)motor.

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old/new effects. These are notably absent. This might in largepart be related to the fact that there are relatively fewelectrodes in the left hemisphere (see Table 2).

The last regions of interest are those in the temporal lobe.These memory-related areas show different effects for S andH. In the left medial temporal gyrus (MTG; BA 21), a brainregion that was previously shown to exhibit subsequentmemory effects in the gamma band (Sederberg et al., 2007b),24–48 Hz gamma oscillatory power decreases with S. In theright superior temporal gyrus (STG; BA 22) on the other hand,14–28 Hz beta and 90–128 Hz gamma power increase with S.These regions on the temporal gyrus do not show a significantcorrelation with H. This is in contrast with deeper temporalregions, where alpha power in the right entorhinal cortex andleft hippocampus increase with H and there is no significanteffect of S.

To formally test the interaction between similarity effectand brain region/frequency, we performed a similarity type(S, H) by region (hippocampus, entorhinal, BA 21, BA 22) bytime bin (early, 0–500 ms; late, 500–1000 ms) by frequencyband ANOVA. This analysis revealed significant main effectsof similarity type [F(1, 7112)=9.78, p<0.01], region [F(3, 7112)=11.67, p<0.001], and frequency [F(6, 7112)=21.52, p<0.001].Crucially, there is also an interaction between similarity typeand region [F(3, 7112)=9.68, p<0.001]. This interaction indi-cates that the temporal gyrus areas has larger regressioncoefficients for S, and the MTL areas (hippocampus andentorhinal cortex) have larger regression coefficients for H.

We then performed a whole-brain analysis to check thespecificity of the effects. Appendix A shows that in addition tothe above-mentioned regions of interest, there are significantsimilarity effects in BA 4/6, the (pre-)motor cortex. ThisBrodmann area shows (for targets) an increase of 4–9 Hztheta oscillatory power with S, but a decrease with H (Table 2).For lures, 9–14 Hz alpha power increases with probe–itemsimilarity. This comports well with behavioral findings ofKahana and Sekuler (2002) and Nosofsky and Kantner (2006),which show that probe–item similarity and the homogeneitycorrection are summed with opposite signs, such that theypush the yes/no decision in opposite directions.

3. Discussion

We demonstrated that correlations can be found betweenoscillatory brain activity and two theoretical constructs:probe–item similarity and the homogeneity correction. Thehomogeneity correction is a relatively recent addition to thesummed similarity framework, and our finding of neuralcorrelates provides further support for its addition. Foroscillatory activity to show clear involvement in visualrecognition memory, we required three parts of the brain(BA 10, left parietal lobe and the MTL) to show significantcorrelations with the model. We found neural correlates ofNEMo in two of these three areas. In BA 10, oscillatory gammapower decreased with S for targets and increased with S forlures. In the temporal lobe, we found in fact a dissociationbetween S and H, where S was related to gamma oscillatorypower in the temporal gyrus, and H to alpha oscillatory powerin the entorhinal cortex and hippocampus. This suggests that

probe–item similarity and the homogeneity correction areindeed two different neural processes. Not anticipated was anincrease of 4–9 Hz theta oscillatory power with S and adecrease with H in the motor cortex (BA 4/6), although, as wewill elaborate on, this could be expected based on theperceptual decision making literature. We will now discussthe caveats of the study, followed by the implications of eachof our main findings in turn.

3.1. The relation between probe–item similarity and thehomogeneity correction

First, one might worry that S and H are not completelyindependent, which might lead to collinearity issues in theregression analysis. Although their correlation is fairly weak(r= -0.12 on average across participants). This correlation isstronger for targets (r= -0.40) than for lures (r=0.01). One caneasily intuit that by realizing that when probe–item similarityis high, necessarilymany of the list items reside close togetherin similarity space. In addition to theweak correlation of S andH themselves, their neural correlates also do not showevidence for a strong correlation, given that the effects inTable 2 sometimes go in the same direction for S and H, andsometimes in opposite directions. Nevertheless, it is helpful tokeep in mind that there may be some instability in theobtained regression coefficients, due to the small correlationbetween S and H. Although there is a potential instability inthe individual-subject regression coefficients, the effect on theoverall results is small because we use an across-subjectsanalysis where individual-subject noise will cancel out.

3.2. Similarity between neural correlates for targetsand lures

On the basis of NEMo and other summed-similarity models,we expected to find similar neural correlates of S for targetsand lures. A potential explanation for the differences betweentarget and lure effects reported in Table 2 is that the neuralpopulations that correlate with components of summedsimilarity do not show enough variance in their similarity-related responses for both targets and lures to be detected,since summed similarity is known to vary less for the targetpopulation than for the lure population (Sekuler and Kahana,2008). Another possibility is that S has different effects ontargets and lures, contrary to the assumptions of NEMo andother summed similarity models.

3.3. Alpha oscillations

In the literature on oscillations in the hippocampus andentorhinal cortex, the most prominent frequency is theta (4–9 Hz; Buzsaki, 2002). In our study, we reported changes in theslightly higher-frequency alpha oscillations with summedsimilarity. Although previous literature focuses mostly ontheta activity, recently oscillations in the alpha frequencyhave also been related to spatial learning (DeCoteau et al.,2007) and environment familiarity (Nerad and Bilkey, 2005) inrats. In addition, it is important to note that in the animalspatial navigation literature, inmany cases theta band activityis defined up to about 13 Hz, which is what we refer to as alpha

38 B R A I N R E S E A R C H 1 2 9 9 ( 2 0 0 9 ) 3 3 – 4 4

activity in the current study. In human spatial learning too,prominent alpha activity is found in the MTL (Ekstrom et al.,2005). We propose that learning in humans too might not berestricted to the theta band, although this frequency band hasbeen emphasized in the previous literature (e.g., Kahana et al.,2001; Sederberg et al., 2003).

3.4. Role of frontopolar cortex in visual recognitionmemory

Earlier, we suggested that failure to find a correlate of NEMo inthe frontopolar cortex would cast doubts on the neuralcorrelates of the model, because this brain area has beenimplicated in integrating information in memory tasks. Inte-grating information is a crucial component in computing thesum of the similarities between two items. In our analysis wefound that delta and theta oscillations in the frontopolar cortex(BA 10) increased with both S and H. This finding comports wellwith neuroimaging studies that have shown the frontopolarcortex to be important for subgoal processing during workingmemory tasks (Braver and Bonglolatti, 2002), and to workingmemory updating (Van der Linden et al., 1999). The sub-goal inthis context of the decision-making process is the summing ofthe similarities of items. A recent study found activation of theleft frontopolar cortexwhendetermining thesimilarity betweenword pairs (Green et al., 2006). The right-hemisphere lateraliza-tion in our study, using nonverbalizable faces, is in agreementwith findings that show right-hemisphere working memoryprocessing for nonverbal stimuli and left-hemisphere proces-sing for verbal stimuli (e.g., Kelley et al., 1998).

We propose that in the frontopolar cortex, a sum iscomputed of the similarities between the probe and listitems, and among the list items themselves. In order to addthese two factors into the decisionmaking process, the sign ofthe interitem similarity term will need to be inverted, becausethis factor pushes the recognition decision in the oppositedirection of probe–item similarity. We propose that thishappens in a different brain area. For this proposal to hold, Sand H should correlate with different subpopulations of cellswithin the frontopolar cortex.

Another region that shows this low-frequency effect is BA46 in the DLPFC. Previous studies have found that BA 46 is veryimportant in perceptual decision-making (Heekeren et al.,2008), where it has been proposed to aggregate the accumu-lated sensory information to come to a perceptual decision. Inaddition, it shows differential activity for targets and lures(e.g., Cabeza et al., 2001). We propose that in this case BA 46works together with the frontopolar cortex to sum thesimilarities of study items to each other and to the probe. Itwould be interesting to apply accumulator models of recogni-tionmemory (e.g., Lamberts et al., 2003; Nosofsky and Palmeri,1997) to these data, and to examine their neural correlates tostrengthen evidence for this proposal.

3.5. Dissociation in temporal regions between probe–itemand item–item similarity

S was associated with gamma oscillations in the left medialtemporal gyrus (MTG) and right superior temporal gyrus (STG),whereas H was associated with an increase in alpha oscilla-

tions in the right entorhinal cortex and left hippocampus. Thismight be analogous to a finding by Konishi et al. (2006), whofound that encoding of isolated items in a word list wasassociated with more lateral temporal activation, whereasencoding of words focusing on the relations between items ledto more hippocampal involvement.

In our study, the dissociation between the two similarityeffects in brain regionsand frequency could reflect the fact thatin computing S one needs to combine currently availableinformation (the probe) with retrieved information fromepisodic memory (the more distant list items). Therefore, inthe case of H, the focus is on relations between items in thestudy list, whereas for S, there is a larger focus on the probeitem. Binding probe and item information requires rehearsalmechanisms and episodic memory, both of which have beenassociated with the right STG (Klingberg et al., 1996; Osakaet al., 2003) and the leftMTG (Elliott andDolan, 1998).Moreover,the right STG has also been associated with distinguishingprobes from similar lures (Kensinger and Schacter, 2007). H, incontrast, almost only uses retrieved list item information, andhence it will recruit more strongly structures that are involvedin episodic memory (Buckmaster et al., 2004).

Another possibility is that H is mainly computed during thelist presentation, instead of during probe presentation. In termsused to describe algorithms in computer science, these could becalled the eager and lazy strategies (e.g., Aha, 1997). It will beimportant to examine at what time H is computed, because inthe previous literature the eager approach has been implicitlyassumed but never tested. In fact, it couldwell be that the neuralcorrelates of H reported here are only the tip of the iceberg, andthat much stronger effects are present during list presentation.

3.6. Similarity effects in motor regions

The motor regions were not among our a priori regions ofinterest. Nevertheless, we found an increase in 4–9 Hz thetaoscillatory power with S in the motor cortices, and a decreasewithH. A closer examination of the literature reveals that this isnot implausible. BA 4/6 has often been reported to be activated invisual and spatial working memory (e.g., Garavan et al., 2000;Postle and Hamidi, 2007; Rypma et al., 1999; Stern et al., 2000).Moreover, it has recently been suggested that “the humanmotorsystem also has an important role in perceptual decisionmaking” (Heekeren et al., 2008). Our finding of a positive relationbetween theta oscillations andS, and adecreasewithH is exactlywhat one would expect if the motor cortex implements alowering of the decision threshold (which is the homogeneitycorrection) as part of the decisionmaking process (Nosofsky andKantner, 2006; Viswanathan et al., submitted for publication). Ifthe mean interitem similarity is high, then a smaller level ofprobe–item similarity is required to evoke a yes response thanwhen interitem similarity is low (Kahana and Sekuler, 2002;NosofskyandKantner, 2006;Visscheret al., 2007;Viswanathanetal., submitted for publication). The localization of this effect inthetaoscillations comportswellwith the findingofRaghavachariet al. (2001) that theta oscillations gate the Sternberg (1966) task.

In summary, we have shown that neural correlates ofNEMo can be found, and these occur mostly in the expectedbrain regions and frequency ranges. This indicates thatoscillatory activity plays a large role in visual item recognition.

39B R A I N R E S E A R C H 1 2 9 9 ( 2 0 0 9 ) 3 3 – 4 4

The main correlates of probe–item similarity (S) and thehomogeneity correction (H) were found in the frontal lobe andMTL. Whereas the frontal areas show generally similarincreases in low-frequency activity for S and H, the correlatesfor the two similarity signals are distinct in memory-relatedareas. In these memory-related regions, S is related to gammaoscillations in the superior and medial temporal gyrus, and His related to increases in alpha oscillations in the entorhinalcortex and hippocampus. S and H are correlated in oppositedirections with theta oscillations in the (pre-)motor cortex, inagreement with their relation in NEMo. Together, thesefindings provide further support for the idea of adding H tosummed similarity models of recognition memory, as wasproposed by Kahana and Sekuler in their NEMo model.

4. Experimental procedures

We recorded iEEG in 1,155 electrodes from 15 neurosurgi-cal patients (ages 15–58; six female, nine male) beingtreated for pharmacologically intractable epilepsy. Thesepatients were implanted with arrays of subdural and/ordepth electrodes for seizure localization and functionalmapping. The clinical team determined the placement ofthe electrodes with these goals in mind. Our researchprotocol was approved by the appropriate institutionalreview boards, and informed consent was obtained fromthe participants and their guardians. The recordings tookplace at Brigham and Women's Hospital in Boston, theHospital of the University of Pennsylvania in Philadelphia,and Universitäts Klinikum Freiburg in Germany.

4.1. Behavioral task

Participants performed a short-term item recognition task(Fig. 1). Lists consisted of one, two, and three faces for whichthe similarity space has been well characterized (Pantelis etal., 2008; van Vugt et al., submitted for publication;Wilson etal., 2002).

The faces were designed to vary along the four principalcomponents of a 37-dimensional face space (Wilson et al.,2002). This face space had been created by taking 37measurements on a set of (normalized) photographs ofCaucasianmales, and then reconstructing the faces fromtheprincipal components that could be extracted from the

Fig. 1 – Trial structure of the short-term item recognition task. Paprobe. Their task is to indicate whether the probe matches one orandomly drawn from a pool of 16 stimuli, as described in the te

matrix of these measurements. A stimulus set of 16 faceswas created from all permutations of steps of one standarddeviation away from the mean face (of the 37-dimensionalface space) in the directions of each of the first four principalcomponents (one standard deviation is approximately thethreshold for 75% correct discrimination between two facesthat are flashed for 110 ms; see Appendix A). To determinethe psychological representation of our stimuli, we per-formed a separate multidimensional scaling (MDS) study,which is described more extensively in Pantelis et al. (2008).Briefly, 23 participants saw all combinations of three facestwice, and were requested to identify the “odd-one-out”.From these ratings, a similarity matrix was constructed byincreasing the similarity value for eachof the twononchosenfaces (see Kahana and Bennett, 1994, for details). Thissimilarity matrix was transformed into similarity coordi-nates for every face using individual differences multi-dimensional scaling (INDSCAL/ALSCAL; Takane et al.,1977). A four-dimensional solution was chosen, which wasa good fit according to an inspection of the scree plot, andalso corresponded to the number of dimensions on the basisof which the faces had been generated. The MDS-derivedcoordinates were then used in the model fitting process.

During the recognition memory trials, the stimuli wereconstrained such that none could be repeated on twosuccessive lists, with the exception of the lure probes thatcould have been a study item 1, 2, or 3 lists ago (a “recentnegatives” manipulation; Monsell, 1978). Each trial startedwith presentation of a fixation cross (1000–1075ms, jittered).Each list item was then shown for 700–775 ms, with a 275–350 ms interstimulus interval. The participant was asked toindicate whether the probe item that appeared after a delayof 3000–3300 ms was or was not a member of the just-presented list (i.e., target or lure), and was given immediatefeedback on their accuracy. The participant could theninitiate the next trial with a button press. Temporal jitterwas employed to avoid spurious correlations betweenongoing oscillations and the structure of the task. Theexperiment was programmed in the freely availablePython-based experiment programming library PyEPL(http://pyepl.sourceforge.net; described in Geller et al., 2007).

We ensured equal probabilities of target and lure items, ofthe three list lengths, of the three levels of proactiveinterference (the list lags of the lure probes), and of thethreepossible item lagsof targetprobes.Wecreatedblocksof

rticipants see a sequence of one to three faces followed by af the study items. This illustrates a trial with three stimulixt.

Fig. 2 – Significant increases (red) and decreases (blue) of oscillatory activity with probe–item similarity and the homogeneitycorrection for targets. Brodmann areas shown in black do not meet our criterion for doing a statistical analysis. In each row, theviews are from left to right: left sagittal, inferior, right sagittal.

40 B R A I N R E S E A R C H 1 2 9 9 ( 2 0 0 9 ) 3 3 – 4 4

trials such that each block comprised 15 lists in which thereare an equal number of target and lure trials, and each itemserved as a lure only once. We randomized the order ofblocks across participants but kept the list order withinblocks fixed, which ensured that every participant saw listswith the same levels of proactive interference. Every sessionwas preceded by two 16-trial training blocks, plus 40additional one-item lists to familiarize the participant withthe face stimuli. Participants were given feedback on theiraverage accuracy and RT at the end of each block. Incorrecttrials and trials with RTs shorter than 200 ms or longer than3500 ms were removed from the analysis.

4.2. iEEG recordings

The iEEG signal was recorded from either subdural grids ordepth electrodes, using either a NeuroFile or Nicolet digital

recording system (depending on the site). Depending onthe amplifier, the signals were sampled at 250, 256, 400,512, or 1024 Hz, and band-pass filtered between 0.3 and70 Hz or 0.1 and 100 Hz. Data were subsequently notch-filtered with a Butterworth filter with zero phase distor-tion between 48 and 52 Hz or 58 and 62 Hz to eliminate therelevant line noise. Intervals of interest in the EEG signalwere scanned for artifacts by means of a kurtosis thresh-old; events were discarded if their kurtosis exceeded athreshold of 5 (Delorme and Makeig, 2004).

To synchronize the electrophysiological recordingswith behavioral events, the experimental computer sentpulses through the parallel or USB port via an opticalisolator into an unused recording channel or digital inputon the amplifier. The time stamps associated with thesepulses aligned the experimental computer's clock with theiEEG clock to a precision well under the sampling interval

Fig. 3 – Significant increases (red) and decreases (blue) of oscillatory activity with probe–item similarity and the homogeneitycorrection for lures. Brodmann areas shown in black do not meet our criterion for doing a statistical analysis. In each row, theviews are from left to right: left sagittal, inferior, right sagittal. There is additionally a decrease of 9–14 alpha oscillationswith thehomogeneity correction in the hippocampus.

41B R A I N R E S E A R C H 1 2 9 9 ( 2 0 0 9 ) 3 3 – 4 4

of the iEEG recording (<4 ms). For all participants, thelocations of the electrodes were determined by means ofcoregistered postoperative CTs and preoperative MRIs, orfrom postoperative MRIs, by an indirect stereotactictechnique, and converted into Talairach coordinates.

4.3. Data analysis

After resampling the data to 256 Hz, we used the Morletwavelet transform with a wave number of 6 to computespectral power as a function of time for the EEG signals ofinterest (Tallon-Baudry et al., 1997; van Vugt et al., 2007).For each trial, the time window of interest extended from1000 ms before probe onset, until 2000 ms after the probe.The first and last second of this interval only served as abuffer to avoid edge artifacts and were subsequently

discarded. For every frequency, we z-transformed thedata to the mean and standard deviation of the powerduring the 1000-ms fixation interval. This normalizes anydrift that may be in the data and ameliorates the low-frequency bias (Freeman, 2006). We assumed that theeffects of interest took place at a 100-ms timescale, andtherefore calculated the average oscillatory power withineach 100-ms time interval.

For the analysis of the retrieval interval we utilized amultiple regression approach to predict the influence ofmultiple interference variables simultaneously (Jacobs etal., 2006; van Vugt et al., submitted for publication). Thismodel was applied separately to every electrode, timeinterval, and frequency, after which the maximumregression coefficient was taken within each frequencyband. We then asked whether the regression coefficients

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of the electrodes within any particular Brodmann area(defined by the Talairach Daemon; Lancaster et al., 2000)significantly differed from zero. This hypothesis wastested with aWilcoxon signed rank test. Final significancewas determined using a p-value threshold set by a FalseDiscovery Rate (FDR) procedure (Benjamini and Hochberg,1995). A FDR of 0.01, which is the threshold we used,means that on average 1% of the significant electrodesacross all frequency bands and time intervals could befalse positives. Note that this differs from conventional p-value testing, where a p-value of 0.05 indicates that onaverage 5% of cases that in fact do not reject the nullhypothesis will be called significant. In our study, a FDR of0.01 corresponded to a p-value threshold of 1.4⁎10-4. OnlyBrodmann areas with a minimum of 10 electrodes and 4contributing participants were included in the analyses.

To examine whether the regression coefficients differedbetween regions and similarity type (probe–item similarity,item–item similarity/homogeneity correction), we applied amultiway ANOVA on the regression coefficients with thefactors frequency band, time interval (early (0–500 ms) vs.late (500–1000 ms)), Brodmann area, and similarity effect(probe–item or item–item). The number of time bins wasreduced to 2, because an inspection of the time course of theresults revealed that the effect is on the order of 500ms. Thisreduction in time bins us to simplify the analysis. For theregion of interest analysis, we then separately plotted thetime courses for each of the regions of interest (i.e., frontalandmemory regions). Tovisualize thewhole-brain analyses,we overlaid the Brodmann areas defined by the TalairachDaemon on the standard MNI brain by using information inthe WFU PickAtlas toolbox (Maldjian et al., 2003).

Acknowledgments

The authors acknowledge support from the CELEST grant SBE0354378, Conte center grant P50MH062196 andNIH grants RO1MH061975, MH068404, EY002158 and MH055678. Further sup-port was provided by the Deutsche Forschungsgemeinschaft(SFB 780, Synaptic Mechanisms, TPC3). The authors would liketo thank Josh Jacobs, Sean Polyn, and Christoph Weidemannfor helpful discussions and suggestions.

Appendix A. Whole-brain analyses

In Figs. 2 and 3, we report the oscillatory correlates of probe–item similarity and the homogeneity correction for targets andlures, respectively. Black regions indicate those Brodmannareas that we excluded from our analyses because theycontain data from fewer than 4 participants and 10 electrodes.Blue and red regions show significant decreases and increasesof oscillatory power with the regressor, respectively.

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