NeuroImage 89 (2014) 165–170

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Individuating the neural bases for the recognition of conspecificswith MVPA

Stefano Anzellotti ⁎, Alfonso CaramazzaDepartment of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138, USACenter for Mind/Brain Sciences, University of Trento, corso Bettini 31, 38068 Rovereto, Trentino, Italy

⁎ Corresponding author at: William James Hall, 33 KirCambridge, MA, USA.

E-mail address: anzellot@fas.harvard.edu (S. Anzellott

1053-8119/$ – see front matter © 2013 Elsevier Inc. All rihttp://dx.doi.org/10.1016/j.neuroimage.2013.12.005

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 5 December 2013Available online 13 December 2013

Keywords:ConspecificsHumansFacesvlPFCTPJ

Conspecifics are potentialmates, and canbe themost dangerous threats.With conspecificswe engage in complexsocial interactions. Therefore, it is important to rapidly detect the presence of conspecifics in a scene. Images ofhumans attract attention, and do so already in 9-months-old infants, showing that the distinction between con-specifics and other animals emerges early in development. However, despite a wealth of evidence on the behav-ioral differences between the processing of conspecifics and other animals, the neural mechanisms that underliethe recognition of conspecifics remain unknown. In this experiment, we used recursive feature elimination to in-dividuate brain regions that show selective effects for the faces of conspecifics, individuating reliable conspecificeffects in the right ventrolateral prefrontal cortex (vlPFC). Consistent with the importance of conspecifics recog-nition for reorienting attention and for social cognition, this region shows functional connectivity with thetemporo-parietal junction (TPJ), implicated in reorienting attention and in the attribution of mental states toothers. Our results suggest that the right vlPFC plays an important role for the recognition of conspecifics andmay function as a gateway for the attribution of mental states to an object.

© 2013 Elsevier Inc. All rights reserved.

Introduction

The ability to recognize conspecifics is crucial to individuate poten-tial mates, identify potential threats, and more generally to detect thepresence of agentswithwhomwe can engage in complex social interac-tions. Human infants distinguish conspecifics from other kinds of ob-jects at a very early age (Bonatti et al., 2002; Pascalis et al., 2002). Thisability is considered an important prerequisite for imitative learning(Meltzoff, 1996; Tomasello et al., 1993, 2005), and therefore for thetransmission of cultural knowledge (Gergely and Csibra, 2005). Further-more, recognizing an object as a conspecific can be important for trig-gering the attribution of mental states (Saxe, 2006). Given theimportance of conspecifics, it comes as no surprise that they are visuallysalient: people tend to look at images of other people in a scene(Buswell, 1935; Yarbus, 1967) and show selective novelty preferencefor human faces (Pascalis and Bachevalier, 1998; Pascalis et al., 2002).

Despite the wealth of behavioral evidence showing that conspecificsare processed differently from other animals (Bonatti et al., 2002;Herrmann et al., 2012; Pascalis and Bachevalier, 1998; Pascalis et al.,2002), the differences between the neural processing of conspecificsand other animals remain to be elucidated. Early fMRI research on theprocessing of face images found no differences between the responses

kland Street, Room 940, 02138

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ghts reserved.

to human faces and animal faces in the fusiform face area (FFA,Blonder et al., 2004; Tong et al., 2000). Some differences between theprocessing of human bodies and animal bodies have been reported(Downing et al., 2001), but they do not account for the salience ofhuman faces as compared to monkey faces (Pascalis and Bachevalier,1998; Pascalis et al., 2002). Recent work used multi-voxel pattern anal-ysis (MVPA) to investigate the representation of faces of humans anddogs (Looser et al., 2012) providing novel insights into how animacyis represented. However, that study lacked a control to distinguishbetween general information about species that would differentiate be-tween any pair of animal species from information specific for humans(“conspecific effects”).

In the present study, we included a control to resolve this issue, find-ing that only a subset of the regions that encode general informationabout animal species shows conspecific effects. Instead of focusing ona limited set of ROIs, we used recursive feature elimination (RFE) tosearch for information about conspecifics in the whole brain, individu-ating conspecific effects in the right ventrolateral prefrontal cortex(vlPFC). Since the individuation of conspecifics is important to reorientattention (Corbetta et al., 2000, 2008) and can play a role in the attribu-tion of mental states (Saxe, 2006), and since these processes engagethe temporo-parietal junction (TPJ) we hypothesized that the rightvlPFC region showing conspecific effects (“vlPFC conspecifics region”)interacts with the TPJ. Consistent with this hypothesis, functional con-nectivity analysis revealed significant connectivity between the vlPFCconspecifics region and the bilateral TPJ.

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Material and methods

Nine participants (age range 23–34, mean 26.1) took part in theexperiment. Their consent was obtained according to the Declarationof Helsinki (BMJ, 1991, pp. 302, 1194). The project was approved bythe Human Subjects Committees at the University of Trento andHarvard University. Participants looked at face images of a human, achimp and a dog and morphs between them. Images of a human, achimp, and a dog face were used for all analyses (Fig. 1A). For the func-tional connectivity analysis morphs between the species were also used(Supplementary Fig. 1). Stimuli were presented with Psychtoolbox(Brainard, 1997; Pelli, 1997) running on MATLAB, with the add-onASF (Schwarzbach, 2011).

Images were presented for 500 ms and followed by a 1500 msblank. A fixation cross was shown at the center of each image. Thecross was red in half of the trials and green in the remaining trials,and participants were asked to press one of two buttons depending onits color. This task was chosen to be orthogonal to the manipulation ofinterest (the species of the face). Trials were grouped in 16-secondblocks containing images of the same species. The experiment consistedof four runs, each composed by two 5-min long sessions (Fig. 1B). Ablock-design functional localizer with faces, houses and scrambled im-ages was administered at the beginning of the fMRI session. None ofthe faces shown in the localizer were presented during the other partsof the experiment.

The data were collected on a Bruker BioSpinMedSpec 4T at the Cen-ter for Mind/Brain Sciences (CIMeC) of the University of Trento using aUSA Instruments eight-channel phased-array head coil. Beforecollecting functional data, a high-resolution (1 × 1 × 1 mm3) T1-weighted MPRAGE sequence was performed (sagittal slice orientation,centric phase encoding, image matrix = 256 × 224 [Read × Phase],

Fig. 1. (A) Dog, chimp, and human faces used in the experiment. (B) Subdivision of theexperiment into runs (4) and sessions (8). (C)Map of themost informative voxels for clas-sifying between pairs of faces of different species (p b 0.05 corrected with permutationtests).

field of view = 256 × 224 mm [Read × Phase], 176 partitions with1-mm thickness, GRAPPA acquisition with acceleration factor = 2,duration = 5.36 min, repetition time = 2700, echo time = 4.18,TI = 1020 ms, 7° flip angle). Functional data were collected usingan echo-planar 2D imaging sequence with phase oversampling (imagematrix = 64 × 64, repetition time = 2000 ms, echo time = 33 ms,slice thickness = 3 mm, with 3 × 3 mm in plane resolution). Overfour runs, 1200 volumes of 34 slices were acquired in the axial planealigned along the long axis of the temporal lobe.

Data were analyzed with SPM8 (http://www.fil.ion.ucl.ac.uk/spm/software/spm8/) and MARSBAR (Brett et al., 2002) running onMATLAB 2011a, and with custom MATLAB software using the MATLABbioinformatics toolbox, and displayed with MRIcron (Rorden andBrett, 2000) and Caret (Van Essen et al., 2001). The first 4 volumes ofeach run were discarded and all images were corrected for head move-ment. Slice-acquisition delays were corrected using the middle slice asreference. Images were normalized to the standard SPM8 EPI template.The BOLD signal was high-pass filtered at 128 s and prewhitened usingan autoregressive model AR(1). Data were modeled using a standardGLM as implemented in SPM, with one regressor for each face imageand each session. Regressors were convolved with a standard hemody-namic response function (HRF). Six motion regressors were included inthe model.

Recursive feature elimination was implemented using the MATLABfunction svmtrain of the bioinformatics toolbox. For each pair of species,a support vectormachine (SVM)was trained to distinguish between theresponses to the two species using data from 3 of the 4 runs. The betavalues obtained from the GLM were used for classification. In the firstiteration, we used activity in all voxels in a gray matter mask (Supple-mentary Fig. 2). Themaskwas generated as the union of the segmentedgraymatters of the individual participants, intersected with an anatom-ical cerebrummask obtained from theWake Forest University PickAtlastoolbox for SPM (http://fmri.wfubmc.edu/software/PickAtlas). At eachsubsequent iteration, the 10% of voxels with smallest SVM weightswere eliminated, until less than 5000 voxels remained (approximately10% of the total number of voxels in themask). This processwas iteratedfor all choices of the run left out, and produced maps of ones and zerosindividuating the location of themost informative voxels. Themaps ob-tained for all runs and participants were smoothed with an isotropic2 mm FWHM kernel and averaged.

The significance of values in the averagemap for each pair of specieswas assessedwith permutation tests. If there is no signal in the data, thelocation of the informative voxels will change randomly from one sub-ject to another. Therefore, to assess significance the RFEmaps generatedwith the real data were shuffled reassigning every voxel in the graymatter mask to a new position within the gray matter mask randomlyfor each subject. The shuffled maps were smoothed with an isotropic2 mm FWHM kernel and averaged, yielding an average map for eachpair of species with a procedure identical to that used for the non-shuffled data. Note that the unsmoothed RFE maps were used forshuffling, so that smoothing occurred only once (and with the samesmoothing kernel) for both the shuffled and the non-shuffled data.The shufflingwas iterated 1000 times. A value in the non-shuffled aver-age map (“real data”) was considered at p b 0.05 if it was within orabove the range of the top 5% of the maxima of the shuffled maps, atp b 0.01 if it was within or above the range of the top 1% of themaxima,and so on. Note that this procedure yields p values that are corrected formultiple comparisons, since when p b 0.05, 95% of the average mapsgenerated with random shuffling are entirely below the threshold.To obtain a single map denoting how informative voxels are indistinguishing between different species, the three maps for the threedifferent species pairs were averaged. Stringent significance thresholdsfor this map were obtained by rank ordering the vectors of maxima ob-tained for the three species pairs and averaging them.

For the comparison between human vs animal distinctions and ani-mal vs animal distinctions, the data were divided into odd and even

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runs, and RFE was applied to the odd runs and the even runs separately.The RFE maps obtained for the chimp vs dog classification weresubtracted from the RFE maps obtained for the human vs chimp andthe human vs dog classifications to eliminate general effects of discrimi-nation between two species. The resulting difference maps weresmoothed with a 2 mm FWHM isotropic Gaussian kernel and averagedwithin subject, producing two difference maps for each subject:[humanVSdog–monkeyVSdog], and [humanVSmonkey–monkeyVSdog].These two maps were then averaged to obtain a map showing differen-tial effects for human vs animal discriminations as compared to animalvs animal discriminations.

In order to assess significance in individual subjects, the location ofthe highly informative voxels for each pairwise comparison betweenspecies for the odd and even runs was shuffled randomly andthe resulting maps were then processed identically to the real data(subtraction, smoothing, averaging). This procedure was iterated 1000times, and significancewas assessedwith the sameprocedure describedabove for the analysis of general effects of specie (Fig. 2A). A leave-one-run-out procedure was used to define the regions of interest (Fig. 2B)used as seeds for the functional connectivity analysis.

A further investigation of the neural responses to different species invlPFC was performed with representational similarity analysis (RSA).For each of the 8 participants inwhich a vlPFC region encoding informa-tion about conspecifics was individuated, a 12 mm spherical vlPFC ROIwas defined, centered on the average of the vlPFC peaks in the other 7participants. This procedure allowed us to define the ROI with indepen-dent data, while at the same time using the entirety of the data withineach individual participant for RSA. The data within the ROIs obtainedwith this procedure were used to compute a correlation matrixreflecting representational similarity in vlPFC.

Functional connectivity was tested by generating spheres of 6 mmradius centered in the right vlPFC peaks (Fig. 2B). Activity in the spheres

Fig. 2. (A) Information about conspecifics in the right lateral PFC in two representative subjectpeaks used as seeds for the functional connectivity analysis. Different colors correspond to direpresentations of the human (H), chimp (C) and dog (D) in vlPFC. (D) Brain areas showing sigselective for conspecifics. 1: right TPJ, 2: left TPJ, 3: left face subregion of the primarymotor corteold k N 20 voxels for visualization purposes.

was averaged and low-pass filtered (0.01 Hz cut-off frequency). Thesame procedure was applied to spheres of radius 6 mm centered ineach voxel of the gray matter, and the correlations between the timecourseswere computed. Correlationmaps for different runswere Fishertransformed, averaged within subject, and entered into a second-levelgeneral linear model using SPM 8. For significance testing, SPM's FWE(family-wise error) correction was used.

Results and discussion

Univariate analyses (t-tests) did not show any differences betweenspecies in thewhole brain (p b 0.05 corrected). Differenceswere absenteven when using a more liberal uncorrected threshold (p b 0.001 un-corrected, 5 voxel cluster threshold). A more sensitive investigation ofthe differences in the neural responses to human and animal faceswas performed with recursive feature elimination (RFE, see DeMartino et al., 2008), which allows to use the weights of support vectormachines to individuate the voxels that contribute the most indistinguishing between two species (“highly informative voxels”).Brain maps individuating highly informative voxels were generatedfor all pairwise classifications among the human, chimp, and dogfaces. These maps were averaged to individuate highly informativevoxels for distinguishing between different animal species. Informationabout different animal species (including humans) was found in theventral occipitotemporal cortex and in the anterior temporal lobe bilat-erally, as well as in several areas of the extended face network (Gobbiniand Haxby, 2007) such as the precuneus and medial prefrontal cortex(Fig. 1C).

It is interesting to note that the precuneus andmedial prefrontal cor-tex, associated with semantic processing (Binder et al., 2009; Fairhallet al., 2013) and social cognition (Mitchell, 2008b), contain informationthat contributes to distinguishing between different animal species

s (p b 0.05 corrected with permutation tests). (B) Regions of interest centered in the PFCfferent subjects. (C) Correlation matrix showing the similarity structure between neuralnificant (p b 0.05 FWE corrected) functional connectivity with the right lateral PFC regionx (M1), 4: left lateral PFC, 5: thalamus. Maps thresholded at p b 0.001with cluster thresh-

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despite the fact that participants were simply performing a color dis-crimination task on the fixation cross. A possible account for this findingis that participants might automatically retrieve semantic informationabout the different animal species when they are observing the images,even when the task does not require it. An alternative possibility is thatthe different species are automatically recognized, and cascading activa-tion spreads to the representations of associated semantic knowledge,but to an extent that does not lead to explicit retrieval.

Two informative patches were found in the right anterior temporallobe, while only one patch was found in the left. The anterior temporallobes have been implicated in the processing of information about peo-ple (Rajimehr et al., 2009; Simmons and Martin, 2009; Tranel et al.,1997) and animals (Anzellotti et al., 2011; Brambati et al., 2006). Fur-thermore, there is evidence of right lateralization of face responses inthe anterior temporal lobes (Anzellotti et al., 2013; Nestor et al.,2011), although there are some exceptions (Rajimehr et al., 2009).The differences in the findings could be due to the fact that studies find-ing right lateralization performed MVPA on individual faces, while thestudy that did not find lateralization investigated the overall BOLD re-sponses to faces.

If a brain region is selective for humans, voxels in that region will bemore informative for distinguishing between pairs of faces inwhich oneis human and the other is non-human, as compared to pairs of non-human faces. In order to individuate voxels more informative forhuman vs non-human classifications, the maps for the chimp vs dogclassification were subtracted from the maps for the human vs chimpand human vs dog classifications, and the two differencemapswere av-eraged. This procedure allowed to individuate regions that containmore information for distinguishing humans from dogs and chimpsthan for distinguishing dogs from chimps. In eight of the nine partici-pants, we found a region in the right vlPFC in which voxels are more in-formative for distinguishing humans from animals as compared toanimals from other animals (“vlPFC conspecifics region”: Figs. 2A, B).Representational similarity analysis (RSA) in vlPFC ROIs defined withindependent data (see Materials and methods) revealed a high correla-tion between the patterns of response to the chimp face and the dogface, and lower correlations between the patterns of response to the an-imal faces and the human face (Fig. 2C). This finding is consistent withthe RFE results.

As an additional test, the fusiform face area (FFA) was individuatedwith an independent functional localizer in individual participants.FFA was found in 7 of the 9 participants. To investigate whether theFFA differentiates between humans and animals, we performed multi-variate analyses in 6 mm and 12 mm radius spherical ROIs centeredin the FFA activity peaks. Two different radius sizes were tested becauseof recent results showing that the size of FFA ROIs has an impact on clas-sification accuracy for face stimuli (Anzellotti et al., 2013). We found noevidence of differences between human vs animal classifications ascompared to animal vs animal classifications in both 6 mm and12 mm radius ROIs (p N 0.1). However, this finding leaves open thepossibility that using a larger number of participants or more sensitivetechniques differences could be observed in FFA, and therefore the cus-tomary caution should be exercised in the interpretation of this null re-sult. In the 12 mmROIs, classification between species was significantlyabove chance (accuracy = 56.25%, t(6) = 3.0007, p b 0.05), consistentwith the finding that FFA encodes information that allows to distinguishindividual faces (Anzellotti et al., 2013; Nestor et al., 2011).

Regions that interact with the vlPFC conspecifics region were indi-viduated with functional connectivity. Functional connectivity corre-lates with structural connectivity (Hagmann et al., 2008), although thepresence of functional connectivity between two regions does not nec-essarily imply the presence of direct structural connections, and mightbe mediated in part by indirect connections (Honey et al., 2009). Tomeasure functional connectivity with the vlPFC conspecifics region,the human information peaks obtained in the RFE analysis were usedto generate spherical regions of interest of radius 6 mm (Fig. 2B).

Activity in the sphereswas averaged and low-passfiltered (0.01 Hz cut-off frequency). The same procedure was applied to spheres of radius6 mmcentered in each voxel of the graymatter, and the correlations be-tween the time courses were computed. Significant functional connec-tivity (p b 0.05 corrected) was detected in the left lateral prefrontalcortex, in the temporo-parietal junction (TPJ) bilaterally, in the thala-mus, in the posterior cingulate, and in the face specific subregion ofthe primary motor cortex (Fig. 2D).

The vlPFC conspecifics region individuated in this study could bespecifically involved in distinguishing between humans and non-humans. Alternatively, it could compute how different an object isfrom the self. If this were the case, we would expect this region to re-spond differently to the self vs others, and to own-race individuals vsother-race individuals. A review by Keenan et al. (2000) reports earlyevidence suggesting an involvement of the right PFC in self–others dis-tinctions. Images of one's own face have been shown to lead to strongeractivity in the right prefrontal cortex when compared to images of fa-miliar faces (Platek et al., 2004, see Platek et al., 2008 for a meta-analysis). Furthermore, Breen et al. (2001) reported the case of a patientwith right prefrontal damage and a deficit for self-identification in themirror. These lines of evidence suggest an involvement of the rightPFC in self–others distinction. With respect to the own-race/other-racedistinction, a study by Lieberman et al. (2005) reported negative corre-lation between right PFC activity and amygdala activity while process-ing faces of African Americans and Caucasians. The amygdala isimplicated in the processing of uncertainty (Whalen, 1998, 2007) andfear (Davis, 1992), as well as in judging the trustworthiness of a face(Engell et al., 2007). Lower activity in the right PFC could lead to per-ceiving the faces as farther from oneself, which would in turn lead to ajudgment of lower trustworthiness, to greater uncertainty/fear andthus to stronger amygdala activity. Consistent with this view, a recentstudy by Platek and Krill (2009) showed an interaction between theself–other distinction and the own-race/other race distinction: greatersimilarity of an other-race face to the self reduces the other-race effectin the amygdala. We hypothesize that this interaction might originatein the right lateral PFC.

Even though in this study the only region found to encode more in-formation for distinguishing conspecifics from other animal speciesthan for distinguishing pairs of other animal species is the vlPFC, futurestudies testing a larger number of participants or employing more sen-sitive data acquisition and analysis techniques might individuate addi-tional areas encoding information about conspecifics. Furthermore,methods employing higher spatial and temporal resolution mightreveal differences between the representation of information aboutconspecifics and about other animal species that are beyond the reachof current methods.

Functional connectivity analyses revealed significant connectivitybetween the right PFC human-selective region and the left lateral PFC,as well as the TPJ bilaterally, the thalamus and the face subregion ofthe primary motor cortex. The left lateral PFC might play a similar roleto the right PFC human-selective region, but it might show weakereffects because of the right lateralization of face processing (seeMinnebusch et al., 2009). The TPJ in both hemispheres has been impli-cated in theory of mind tasks (Saxe, 2006; Saxe and Kanwisher, 2003;Young et al., 2010), such as attributing desires and beliefs to others.Saxe (2006) proposed that the TPJ is part of a network involved inuniquely-human social cognition abilities. The TPJ has been also impli-cated in the process of detecting behaviorally relevant stimuli andreorienting attention (Corbetta and Shulman, 2002; Corbetta et al.,2000). It is currently debatedwhether the TPJ regions involved in theoryof mind and reorienting attention coincide with studies showing thatthere is overlap (Mitchell, 2008a) and studies showing that the overlapis only partial (Scholz et al., 2009). Recent studies using functional con-nectivity individuated spatially segregated regions in the TPJ, one show-ing connectivity with areas functionally associated with attention andthe other showing connectivity with areas functionally associated

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with social cognition and memory retrieval (Bzdok et al., 2013; Marset al., 2012). According to a recent proposal (Corbetta et al., 2008), theTPJ might contribute to theory of mind allowing the reorientation of at-tention from one's own perspective to another's. The interaction be-tween the TPJ and regions encoding information about conspecificsmight play a role in signaling the high behavioral relevance of conspe-cifics, might support uniquely human aspects of theory ofmind, or both.

Connectivity between the human-selective region and the face sub-region of the primary motor cortex (M1) could be due to the greatersimilarity of one's own motor system to the motor system of thehuman as compared to the non-human animals. Previous studies haveshown stronger BOLD signal (Uddin et al., 2005) and motor evokedpotentials (Papeo et al., 2011) in M1 when processing stimuli relatedto the self than to others. In the view that the human-selective regioncomputes how different an object is from the self, M1 could provide in-formation to the human-selective region to contribute to the recogni-tion of conspecifics, or alternatively the right PFC human-selectiveregionmight play a role in determining the amount of cascading activa-tion in M1 (Mahon and Caramazza, 2008) as a function of the similarityof a stimulus to the self. To investigate further these alternative hypoth-eses, it would be particularly interesting to study the timing of re-sponses in the primary motor cortex and the right PFC human-selective region with methods that offer high temporal resolution, todetermine whether M1 activity precedes or follows right PFC activity.

In conclusion, a region in the right lateral PFC encoding informationabout conspecifics was individuated. Information about conspecifics inthis region might arise as a computation of the difference between anobject and the self. The region shows functional connectivity with re-gions involved in attributingmental states to others and reorienting at-tention, and can be the basis of a mechanism for redirecting attentiontowards conspecifics and determining whether or not mental statesshould be attributed to an object.

Acknowledgments

We thank Talia Konkle for suggestions on data analysis, Liuba Papeofor discussion of the interpretation of the results, and Amitai Shenhavfor discussions on the amygdala and uncertainty. This work was sup-ported by the Provincia Autonoma di Trento and by the FondazioneCassa di Risparmio di Trento e Rovereto. Stefano Anzellottiwas support-ed by a grant from the Fondazione Cassa Rurale di Trento.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.neuroimage.2013.12.005.

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