theories of person perception predict patterns of neural

Cerebral Cortex, October 2018;28: 3505–3520

doi: 10.1093/cercor/bhx216Advance Access Publication Date: 22 August 2017Original Article

O R I G I NA L ART I C L E

Theories of Person Perception Predict Patternsof Neural Activity During MentalizingMark A. Thornton and Jason P. Mitchell

Department of Psychology, Harvard University, Cambridge, MA 02138, USA

Address correspondence to Mark A. Thornton, Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138, USA.Email: [email protected].

AbstractSocial life requires making inferences about other people. What information do perceivers spontaneously draw upon tomake such inferences? Here, we test 4 major theories of person perception, and 1 synthetic theory that combines theirfeatures, to determine whether the dimensions of such theories can serve as bases for describing patterns of neural activityduring mentalizing. While undergoing functional magnetic resonance imaging, participants made social judgments aboutwell-known public figures. Patterns of brain activity were then predicted using feature encoding models that representedtarget people’s positions on theoretical dimensions such as warmth and competence. All 5 theories of person perceptionproved highly accurate at reconstructing activity patterns, indicating that each could describe the informational basis ofmentalizing. Cross-validation indicated that the theories robustly generalized across both targets and participants. Thesynthetic theory consistently attained the best performance—approximately two-thirds of noise ceiling accuracy––indicating that, in combination, the theories considered here can account for much of the neural representation of otherpeople. Moreover, encoding models trained on the present data could reconstruct patterns of activity associated withmental state representations in independent data, suggesting the use of a common neural code to represent others’ traitsand states.

Key words: functional magnetic resonance imaging, multivoxel pattern analysis, social cognition, theory of mind

IntroductionHumans enjoy social lives of tremendous complexity. To suc-cessfully navigate this complexity, we must form perceptionsof other people. Accurate impressions of others serve theinvaluable purpose of allowing us to predict, at least in limitedfashion, people’s likely thoughts, feelings, and actions. Theneed to form and use perceptions of others raises the questionof how our minds spontaneously organize our perceptions intouseful, coherent models of other people. What, if any, implicitpsychological taxonomy do we apply to the occupants of oursocial lives? A popular meta-theory, which spans many exist-ing theories, posits the existence of a representational “space”within which we meaningfully array the people we know. Here,we test 4 specific instances of this dimensional meta-theory ofperson perception, as well as one synthetic theory derived from

their components. We aim to examine whether any of thesetheories can explain the organization of person knowledge,measured using pattern analysis of brain activity, and if so,which perform better or worse than others, and how well theyall compare to a hypothetical ideal theory.

Theories of the class we consider propose dimensional struc-tures for describing the domain of people. For instance, the ste-reotype content model (Fiske et al. 2002) hypothesizes that weorganize our perceptions of others by evaluating them on 2 qual-ities: their warmth and competence. These qualities form thedimensions of a representational space within which the coordi-nate positions of an individual summarize the person’s keysocial properties. A person or social group’s position in stereo-type content space thus influences the types of motivations andemotions that perceivers direct toward that person or group.

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Such dimensional theories of person perception—models ofhow we organize our knowledge of others—have been developedto address a number of specific phenomena within social psy-chology. For instance, the stereotype content model was origi-nally developed for explaining phenomena in an intergroupcontext (Cuddy et al. 2008). The goal of the present study is toexamine whether, and how well, 4 prominent dimensional theo-ries from the social and personality literature generalize fromtheir original domains to describe overall perceptions of others.These theories have proven highly successful in dealing withphenomena in their native context, so each might also provide avaluable characterization of general representations of other peo-ple. If so, the broader applicability of these theories would allowus to draw on them to explain a much wider range of thoughtsand behaviors.

Like the stereotype content model, the 3 other theories weconsider were also developed to address relatively specific phe-nomena. The 5-factor model of personality –consisting of the“Big 5” trait dimensions of openness, conscientiousness, extra-version, agreeableness, and neuroticism—is a the leading con-temporary model of personality (McCrae and Costa 1987;Goldberg 1990). As such, it generally aims to capture the objec-tive reality of personality—that is, latent variables that explainindividual differences in people’s behavioral tendencies—rather than just how personality is perceived by others.Nonetheless, the empirical basis of the five factor model reliesextensively on peer perceptions and lay conceptual judgmentsof trait terms, making it very much a theory of person percep-tion. The third theory we consider is a 2-factor model of mindperception, consisting of the dimensions of agency and experi-ence (Gray et al. 2007). This theory is based on people’s tendencyto attribute a mind to various entities. Two factors account formuch of this tendency: an entity’s perceived capacity for subjec-tive experience and its agentic capacity to influence the world.The fourth and final model we consider is a 2-factor theory ofsocial face perception (Oosterhof and Todorov 2008). This theoryis based on the observation that most of the trait inferencesthat people make on the basis of viewing others’ faces can bereduced to 2 dimensions: trustworthiness and social domi-nance. Certain facial features are associated with each dimen-sion: for instance, wide set eyes and upturned mouths drivetrustworthiness judgments.

In addition to these 4 extant theories, we also examine afifth, synthetic theory that combines the dimensions of thesetheories and other social features. This theory represents ourattempt to generate and test the best synthesis of existing con-ceptions of person perception. By doing so, we hope to estimatean upper bound on the field’s current explanatory ability. Wecan also thereby measure the converse—how far our theorieshave left to go.

Despite the success of the 4 extant theories in describingthe phenomena to which they were originally tailored, thereare also reasons to believe that these theories might not gener-alize to predicting person perception outside of their originalcontexts. Even if the meta-theory of a representational spacefor other people is true in general, it is not clear which specificdimensions embody the successful instantiation of the meta-theory. The stereotype content model was conceived as anexplanation for intergroup affect—will it retain explanatorypower when group membership is not at the fore? Do the Big 5traits characterize actual behavior better than perceptions ofothers’ behavior? Might the dimensions of mind perception mat-ter less when all of the perceptual targets are clearly endowedwith minds? Can the dimensions that describe face-based trait

inference still shape social judgments when no faces are visible?The answers to any of these questions might easily be “no,” inwhich case the dimensions of the corresponding theory mightnot be able to serve as the informational basis of mentalizing.Thus, it is far from a forgone conclusion whether the theorieswe consider will be successful accounts of the mental represen-tation of other people, and if they are, which theories will provethe more or less successful accounts.

Addressing this issue poses a significant challenge due tothe diversity of the theories in question. The phenomena, para-digms, and measures applied to these theories thus far have lit-tle in common; a new shared framework is needed to comparethem directly. Here we import such a framework, pioneered inother areas cognitive neuroscience (Mitchell et al. 2008; Huthet al. 2016), into the social domain. This framework combinesfunctional magnetic resonance imaging (fMRI) and multivoxelpattern analysis (MVPA). This approach has multiple advan-tages, several due to the nature of fMRI as a measure. First,BOLD fMRI is an implicit measure, as people are not con-sciously aware of oxygenated blood flow in their brains, andthis should minimize the effects of social desirability on out-comes. Second, fMRI is inherently a rich multidimensionalmeasure, because signal is collected simultaneously from voxelsthroughout the brain. Finally, fMRI permits the use of a wide vari-ety of tasks that lack informative behavioral responses becausesuch responses need not be the primary source of outcome datain an fMRI paradigm.

The modeling approach we adopt here enhances theseadvantages. Standard fMRI analyses focus on differences in theabsolute level of activity across experimental condition, makingsuch analyses well-suited for identifying regions that are acti-vated by a particular cognitive process. This approach of map-ping cognitive functions onto brain regions has yielded awealth of valuable results, such as the discovery that socialprocesses are mediated by neural substrates distinct fromthose that subserve comparable cognitive processes (Mitchellet al. 2002; Saxe and Kanwisher 2003; Saxe and Wexler 2005;Mitchell 2009). However, because few traditional social psycho-logical theories make explicit predictions about the spatial dis-tribution of neural activity, it has been difficult to test suchtheories directly using standard univariate analyses. In con-trast, MVPA—the general term for the techniques we employ—focuses on spatially extended patterns of brain activity acrossmany voxels or regions (Haxby et al. 2001). By analyzing distrib-uted patterns of activity, MVPA can go beyond the examinationof process to reveal much about the “content” of cognition(Kriegeskorte and Bandettini 2007; Mur et al. 2009). Thisapproach has already borne fruit in social neuroscience, yield-ing insight into the mental and neural organization of mentalstate representations (Skerry and Saxe 2015; Tamir et al. 2016)and social categories (Stolier and Freeman 2016). By consideringensembles of voxels together, MVPA is also frequently moresensitive than analogous univariate approaches, even for thedetection of univariate signals (Davis et al. 2014). These fea-tures make it a natural choice for understanding how the con-tent of person perception is organized.

In the present study, we employ 2 types of MVPA. The pri-mary analysis, we adopt is a form of encoding model that wewill refer to as “feature space” or “voxelwise encoding” model-ing. In this approach, one or more theories are compared interms of their ability to predict patterns of brain activity associ-ated with particular stimuli (Mitchell et al. 2008). In the presentcase, for instance, a feature space model based on stereotypecontent would be trained to “know” what pattern of brain

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activity is associated with thinking about a warm person, andwhat pattern is associated with thinking about a competentperson. Then, if a new target person is “introduced” as havingspecific amounts of warmth and competence, the feature spacemodel can predict what pattern of brain activity the targetshould elicit by mixing together its canonical warmth and com-petence patterns in the appropriate ratio. Such predicted pat-terns can be compared with the actual patterns elicited by thetargets to determine the model’s accuracy. This accuracy inturn reflects how well the feature dimensions of each theoryserve as a basis for describing spontaneous neural activity dur-ing mentalizing.

We supplement feature space modeling with the use of repre-sentational similarity analysis (RSA), which we use to assess theability of each individual dimension to explain the (dis)similaritybetween target person-specific patterns of brain activity(Kriegeskorte et al. 2008). We also use this approach to test 2accounts of pairwise similarity between target people, based onholistic ratings and biographical text analysis, respectively.Simultaneously, we take advantage of the relative computationalsimplicity of RSA to probe method-related variance in our find-ings, assessing their robustness to various analytic choices.

By combining the methodological advantages of neuroimag-ing with these recent innovations in computational modeling,we can directly test 5 theories of person perception against thenull of baseline performance and against each other. If the fea-ture space models accurately predict patterns of neural activityelicited by making diverse social judgments about a large set ofpeople, this would suggest that the extant theories generalizewell beyond their original purposes and describe the generalframework our minds spontaneously deploy to organize repre-sentations of others. Direction comparisons of the theorieswould reveal which provide better accounts for the neural orga-nization of person knowledge. In addition to learning which ofthe theories provide better and worse characterization of howwe represent people, the current study will allow us to quantifyhow well important theories perform relative to hypotheticalideal. This uncommon opportunity will help us understandwhether we, as a field, are close to solving the problem of per-son perception or only just beginning.

More broadly, the success of the feature space encoding mod-els would support the meta-theory that we represent others’within a multidimensional representational space. This findingin itself would not be trivial, as a number of alternatives mightobtain: for example, an arbitrary pattern might encode each tar-get person, with no relationship between interpersonal similarityand pattern similarity, a variant of the sparse coding or (pattern-wise) “grandmother cell” hypothesis (Gross 2002). Even if a low-dimensional representational space does exist, the relationshipbetween the dimensions and patterns of brain activity might behighly complex or nonlinear, or encoded at a spatial scale inac-cessible to fMRI. Finally, the dimensions of such a space mightaccord with inscrutably deep computational variables, ratherthan familiar social dimension to which we have explicit con-scious access. If any of these possibilities obtained, it wouldinvalidate not just one of the specific theories we are testing, butthe general meta-theory they together embody.

Materials and MethodsCode and Data Availability

Behavioral data, norming data, and processed neuroimagingdata, as well as custom analysis and stimulus presentation

code, are freely available on the Open Science Framework (OSF)(https://osf.io/32wrq/). We report how we determined samplesize, all data exclusions, all manipulations, and all measures inthe study.

Participants

The desired sample size for the neuroimaging experiment wascalculated via a resampling-based power analysis using datafrom a previous study (Tamir et al. 2016). This study was simi-lar in design to the current study, with the same number ofstimulus conditions, similar trial durations and counts, andconducted on the same fMRI scanner. Moreover, it was a rareexample of a condition-rich domain mapping study in thesocial domain, and thus judged more likely to produce realisticeffect size projections than other nonsocial studies might.Participants in that study made judgments about the extent towhich various pretested scenarios would elicit each of 60 men-tal states such as “embarrassment” or “planning” while in fMRIscanner. Activity patterns associated with each of the 60 stateswere calculated using the general linear model (GLM), and patterndissimilarity (correlation distance) between these patterns wascalculated within regions showing a univariate effect of mentalstate identity (P < 0.0001, in an omnibus voxelwise ANOVA).Pattern dissimilarities were then correlated with the absolutedifferences between the mental states on principal componentstermed rationality, social impact, human mind, and valence.Valence proved the smallest significant predictor of patternsimilarity, with an average r = 0.05, and thus we targeted thiseffect size in the power analysis to be conservative. Simulatedsamples ranging in size from 10 to 40 were generated by boot-strapping participants from this data set. RSA was conductedon each participant in the bootstrapped sample, regressingneural pattern similarity between the 60 mental states onto thebehavioral predictions of the 3 orthogonal dimensions. Theresulting coefficients were entered into random-effects t-testsfor each dimension, and the resulting P-values aggregatedacross bootstrap samples to estimate power. This procedure isanalogous to the representational similarity analyses we con-ducted in the current study. A target of 30 participants was esti-mated to provide power greater than 0.95.

Imaging participants (N = 30) were recruited from theHarvard University Psychology Study Pool. One participant wasexcluded due to failure to respond on 45% of experimentaltrials (4.7 SDs below mean response rate). The remaining parti-cipants (18 female; age range 18–28, mean age = 22.7) wereright-handed, neurologically normal, fluent in English, and hadnormal or corrected-to-normal vision. Two pilot versions of theimaging task (Ns = 51, 45) were conducted outside of the scan-ner to ensure the functionality of the task and to assist in itemselection. Online participants in the stimulus norming surveys(Ns = 316, 648, and 858) were recruited via Amazon MechanicalTurk. All participants provided informed consent in a mannerapproved by the Committee on the Use of Human Subjects inResearch at Harvard University.

Stimuli and Behavioral Procedure

We applied 2 criteria in selecting a set of 60 people to serve asmentalizing targets: familiarity and diversity. Familiarity wasnecessary to ensure that the task was feasible for participantsto complete—that is, that they knew the targets about whomthey were asked to make inferences. Diversity, within the con-straint of familiarity, was desirable to maximize observable

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effect sizes and ensure the generalizability of our findings tothe broader set of targets about whom people actually think ineveryday life. To generate a set of targets with these 2 proper-ties, we employed a 2-pass selection procedure. The first passused automated Internet-based methods, while the secondpass validated and refined the first through the use of largeonline norming surveys. We began with web scraping and textanalyses because these techniques are fully automated, andrequire no input from human participants. Given the initiallylarge search space, consistently of potentially hundreds orthousands of potential target people, eliciting human judg-ments (particularly of pairwise similarity) would have beenimpractical.

Web Scraping and Text AnalysisTo select a set of plausibly famous individuals in a minimallybiased way we turned to Wikipedia traffic statistics (main-tained at www.stats.grok.se). A list of the 1000 most frequentlyviewed pages (during March 2014) was surveyed for pagesabout individual people. Fictional characters were excluded.Additionally, Adolf Hitler and Joseph Stalin were excludedbecause we anticipated that the extreme nature of these indivi-duals might compress the range of judgments made about theothers. This process yielded a set of 245 individuals. To confirmthat these people had indeed achieved some degree of lastingfame, we then programmatically retrieved their traffic statisticsduring a separate time period (June 2014). Anyone whoachieved at least 30 000 views during this period (1000 per dayon average) was retained in the set, yielding 223 individuals.

The next step in the process was to reduce the famousgroup to a diverse and minimally redundant set. To this end,the text of the remaining individuals’ biographies was retrievedusing the Wikipedia API for Python (http://pypi.python.org/pypi/wikipedia/). This text was cleaned by the removal of num-bers, punctuation, one letter words, and stopwords (content-free grammatical words such as articles and conjunctions). Thefrequencies of the remaining words were then tabulated and amaster list of words used across the person pages was assem-bled. To eliminate very low frequency words (many of whichwere, in fact, nonword artifacts) we required that wordsincluded in the master list appear at least twice each in at least2 biographies. Additionally, to remove non-discriminative highfrequency words, any word that appeared in more than 90% ofpages was removed from the master list. The final list consistedof 8260 unique words. The frequency of each word on the mas-ter list was calculated for each biography (normalized by the

total number of words on that page, prior to the elimination ofvery high and low frequency words). The semantic “distance”between any pair of pages could then be calculated as the sumof absolute differences in their respective word frequency vec-tors. To form an ideal list of 150 people for behavioral testing,the person with the greatest semantic distance to all otherswas first selected. The person with the greatest semantic dis-tance to the target people already selected was then added tothe list iteratively until the desired number had been achieved.

PretestingAfter the completion of the Internet-based stimulus selection,2 online surveys were used to validate these measures and fur-ther refine the stimulus set. Participants (N = 316) rated howwell they knew each of the 150 target people selected in theearlier selection stages on a continuous line scale from 0 (Notat all) to 100 (Extremely). Ratings of 10 or less on this scale wereclassified as “unknown.” We discarded target people who wereunknown to at least 25% of raters, leaving 73 target people atthis stage. In a second survey, participants (N = 648) rated thepairwise similarity of the remaining targets. Each participantwould be assigned one reference target and then judge the sim-ilarity of all 72 other targets to that person on a continuous linescale from “Very Different” to “Very Similar.” At this point, wemanually removed 3 more potentially problematic targets:Jeffrey Dahmer, due to the extreme ratings, Steve McQueen,due to the presence 2 relatively famous people sharing thisname, and Anne Frank, due to being the only nonadult on thelist. We then sequentially identified the pairs of people whowere rated most similar to each other and eliminated the lessfamous person in the pair until our final goal of 60 target peoplehad been achieved.

To locate the 60 final targets on the dimensions of the theoriesof interest, an additional sample of online participants (N = 869)provided norming ratings (Table 1). Each participant was asked torate all 60 targets with respect to only 1 dimension. They weregiven a brief description of the dimension in question to maintainconsistent definitions across participants. They were then pre-sented with the targets in a random order and asked to rate themon a continuous line scale with anchors appropriate to the rele-vant dimension. There were 13 dimensions in total: warmth,competence, agency, experience, trustworthiness, dominance,openness to experience, conscientiousness, extraversion, agree-ableness, neuroticism, attractiveness, and intelligence. Althoughthe last 2 of these dimensions do not together constitute anextant model, we included them because they are extremely

Table 1 The reliability of norming ratings of targets on psychological dimensions

Dimension Theory n Mean inter-rater r Cronbach’s α

Agency Mind perception 60 0.13 0.90Agreeableness Five factor personality 58 0.31 0.96Attractiveness None 71 0.29 0.97Competence Stereotype content 69 0.38 0.98Conscientiousness Five factor personality 65 0.41 0.98Dominance Social face perception 72 0.32 0.97Experience Five factor personality 52 0.35 0.97Extraversion Five factor personality 72 0.40 0.98Intelligence None 65 0.52 0.99Neuroticism Five factor personality 62 0.26 0.96Openness Five factor personality 66 0.16 0.93Trustworthiness Social face perception 64 0.39 0.98Warmth Stereotype content 65 0.30 0.97


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widely discussed features of other people in both the scientific lit-erature and lay parlance. Ratings for each target were averagedacross participants to provide a single scores on each dimension.To exclude participants who did not comply with the task, onlythose who provided at least 10 unique rating values wereincluded in the composite.

Dimensionality ReductionTo generate an optimal synthetic theory, principal componentsanalysis (PCA) was applied to the correlation matrix betweenthe 13 rating dimensions described above. Comparison oforthogonal and oblique solutions suggested better fit for corre-lated component solutions, and thus a direct oblimin rotationwas adopted. An optimal solution, as measured by Velicer’sMAP and Very Simple Structure (complexity 2), was obtainedwith 3 principal components. These components loaded mostlyhighly onto dominance, warmth, and extraversion (reflected),respectively (Fig. 1B). We named them power, valence, andsociality to distinguish them from the rated variables. All of theoriginal dimensions were reasonably well explained by the fac-tor solution, with a mean communality of 0.88 and a minimumof 0.71. Component 1 correlated with component 2 at r = 0.26,and with component 3 at r = 0.36 components 2 and 3 were cor-related at r = 0.22. While nontrivial, these correlations were suf-ficiently small to minimally complicate interpretation, relativeto an orthogonal model. The scores from the 3 factors wereused in place of ratings for the purposes of the constructing

encoding models for the resulting 3-component synthetic the-ory. Note that the component scores remain virtually identical(rs > 0.99) if the same PCA is applied to theoretical dimensionsonly, excluding the additional dimensions of intelligence andattractiveness.

Experimental ParadigmThe imaging paradigm consisted of a modified version of amentalizing task used in previous research (Mitchell et al. 2006;Tamir and Mitchell 2010, 2013). On each trial, the name of oneof the 60 targets would appear in the top center of the screen.After 500ms, 1 of 12 social judgment items would appear onthe screen along with a 5-point Likert type scale. These itemsconsisted of statements such as “likes debating issues withothers” and “would grieve the loss of a pet.” Participants woulduse a button box in their left hand to rate how well theybelieved the statement applied to the target person in questionfrom 1 (not at all) to 5 (very much). We anticipated that partici-pants would generally not know the correct answers to thesequestions with certainty, but would instead have to make aninference based on their overall knowledge of the target. Theitem and scale remained on the screen during a 3.25 s responsewindow. This period would be followed by a 250ms minimumfixation period, and a variable jitter fixation period (mean jitter =1.33 s, approximately Poisson distributed in 2 s increments).Each run of the experiment consisted of 60 trials: one for eachof the target people. The runs were also balanced with respect

Figure 1. Dimensions organizing target people. (A) Correlations between dissimilarity matrices produced by taking the absolute differences in positions of targets on

each of the 13 rated dimensions. Two pairwise measures of (dis)similarity were also included: textual dissimilarity of Wikipedia biography pages, and holistic pair-

wise ratings. (B) Loadings of the 13 rated dimensions onto the 3 components of the optimal factor solution. Note that the sign of factor solutions is arbitrary, so for

name-consistency the direction of the sociality dimension is sign-reversed in later analyses.


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to the social judgment items, with 5 of each of the 12 appearingin every run. An additional 6 s fixation period was allowed atthe end of each run to ensure capture of hemodynamicresponses from the final trials. Over the course of 12 runs, eachtarget person appeared with each of the social judgment itemsonce, fully crossing these factors.

Prior to entering the scanner, imaging participants alsocompleted 2 rating tasks. First, they rated themselves on the 12social judgment items described above. Second, they indicatedtheir liking of, familiarity with, and similarity to each of the 60target people. These 3 questions were presented in randomlyordered blocks, with targets randomized within each block.Randomization was conducted independently for each blockand for each participant.

Item Selection and ValidationThe 12 items (Table 2) used in the imaging paradigm wereselected for minimal redundancy from a larger set of 24 itemstested in 2 pilot versions of the behavioral task conducted out-side the scanner. The initial set of 24 pilot items was selectedmanually based on 3 criteria: (1) applicable to all target people,(2) not widely known (for a fact) for most target people, and (3)variety amongst the items. Using the pilot data, we first calcu-lated the average response for each target person on each ofthe 24 items. Then we selected items sequentially based onmaximal residual variance. The first item chosen was thus theone with the greatest variance across the target people. Thesecond item chosen had the largest residual variance after con-trolling for the first item. The third item had the largest resid-ual variance after controlling for the first 2, and so forth. Thisapproach helped to ensure that each item in the final set of 12differentiated the target people in a minimally redundant way.

One potential concern regarding the imaging results mightbe that the social judgment items chosen for the study could bebiased toward eliciting representations more consistent withone theory rather than the others. Although we cannot com-pare the chosen questions to the entire domain of hypotheticaljudgments, we can compare the questions to each of the theo-ries. To this end, we calculated the average response (acrossparticipants) to each social judgment item for each target per-son. We then performed a purely behavioral RSA comparingthe 4 extant theories to the social judgment items. For each ofthe 4 theories, predictions about similarity between targetswere derived by taking the Euclidean distance between the tar-gets on the dimensions of the theory. Similarity estimates werederived from the social judgment items in the same way—that

is, by taking the Euclidean distance between targets in the 12-D“space” defined by the items. The predictions of the theorieswere correlated with the item response similarity estimates tosimilar degrees: stereotype content, r = 0.69; social face percep-tion, r = 0.65; 5 factor personality, r = 0.71; mind perception, r =0.76. Given the small range of these correlations and the factthat their magnitudes do not covary with model accuracy(below), it seems unlikely that item choice spuriously producedthe imaging results. Note that the generally high correlationsbetween the item-space and extant theories are to be expectedand desired, as these theories were in general crafted to explainthe general principles behind specific interpersonal inferences.

Behavioral Data AnalysisResponses on the imaging task were analyzed to assess theirconsistency with previous results in the mentalizing literature.We combined Likert ratings from this task with participantsprescan ratings of themselves with respect to the same socialjudgment items to calculate trial-wise self-other discrepancyscores. We analyzed these scores using mixed effects modeling,including fixed effects for similarity, reaction time, and theirinteraction, random intercepts for participant, social judgmentitem, and target person, and a random slope for reaction timewithin participant. Reaction times were mean centered, andtrials with reaction times less than 500ms were excluded fromanalysis (1.5%). Similarity ratings were scale-centered.Statistical significance was calculated using the Satterthwaiteapproximation for degrees of freedom.

Imaging Procedure

Acquisition and PreprocessingImaging data were acquired at the Harvard University Center forBrain Science using a 3 Tesla Siemens Tim Trio scanner(Siemens) with a 32-channel head coil. Functional gradient-echoecho-planar images were obtained from the whole brain using asimultaneous multi-slice imaging procedure (69 interleavedslices of 2mm thickness, TR = 2000ms, TE = 30ms, flip angle =80°, in-plane resolution = 2.00 × 2.00mm, matrix size = 108 ×108 voxels, 162 measurements per run). Functional images werepreprocessed and analyzed using SPM8 (Wellcome Departmentof Cognitive Neurology) as part of the SPM8w package (https://github.com/ddwagner/SPM8w), and in-house scripts in MATLABand R.

Data were spatially realigned via rigid body transformationto correct for head motion, unwarped, and then normalized toa standard anatomical space (2mm isotropic voxels) based on

Table 2 Item statistics from imaging task

Item text Mean response Mean SD of response Mean reaction time (s)

Loves to solve difficult problems 3.24 1.18 1.79Enjoys spending time in nature 3.18 0.97 1.80Enjoys learning for its own sake 3.42 1.10 1.80Likes debating issues with others 3.29 1.09 1.81Thinks that a firm handshake is important 3.41 1.00 1.82Likes to deal with problems alone 3.02 0.97 2.01Prefers to avoid conflict when possible 2.92 1.00 2.02Dislikes traveling by airplane 2.38 0.92 1.86Finds the use of profanity offensive 2.32 1.04 1.95Thinks the wealthy have a duty to the poor 3.17 1.06 1.90Would like to learn karate one day 2.89 0.99 1.83Would grieve the loss of a pet 3.51 0.78 1.75


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the ICBM 152 brain template (Montreal Neurological Institute).The GLM was used to analyze each participant’s data in prepa-ration for MVPA. Each target person was modeled as a condi-tion of interest (60 total) using a boxcar regressor which beganon each trial when the name of the target appeared and lasteduntil the participant responded or the response window ended.The regressors were convolved with a canonical hemodynamicresponse function and entered into the GLM along with addi-tional nuisance covariates: run means and linear trends, 6motion realignment parameters, and outlier time points(defined by the Artifact Detection Toolbox). Regression coeffi-cient maps from this analysis were smoothed with a 6mmFWHM Gaussian kernel to improve interparticipant alignmentand increase voxelwise reliability.

To remove the influence of global background patterns fromthe baseline contrast, each voxel was z-scored across targetsprior to MVPA. Note that this z-scoring might introduce a slightdependence between test and training sets in subsequent cross-validation. However, the representational similarity analyses weconducted could not be influenced by dependency between testand training sets (as they do not use cross-validation at all) andthese analyses yet still manifest robust effects. It should also benoted that z-scoring could not adversely influence the across-participant or across-data set cross-validation analyses we per-formed, as the dependency introduced by z-scoring applied onlywithin participant.

Reliability-Based Voxel SelectionInterparticipant reliability in univariate responses to the 60 tar-gets was calculated at each voxel in the brain using the formulafor standardized Cronbach’s α. This approach is based on ear-lier stability-based approaches to feature selection (Mitchellet al. 2008). To select voxels for inclusion in inferential analy-ses, this measure of voxelwise reliability was combined withpattern similarity reliability. This process proceeded as follows:for each voxelwise threshold between 0 and the maximum vox-elwise threshold (in increments of 0.01) the set of voxels withreliability equal to or greater than the threshold was retained.Patterns of neural activity for each of the 60 targets wereextracted from the retained voxels, and these patterns werecorrelated with each other to produce neural similarity matri-ces for each half of the data set. The lower-triangular portionsof these matrices were then correlated to estimate the reliabil-ity of pattern similarity across the halves. After this processhad been repeated at each voxelwise threshold, the thresholdwith the highest pattern-wise reliability was used to select afinal set of voxels. This voxel set maximized both the reliabilityof target-specific patterns across the social brain network and,subject to that restraint, the reliability of person-specific activ-ity within individual voxels.

Note that reliability-based voxel selection is in fact indepen-dent of the hypotheses being tested. None of the rating dataplayed a role in this analysis, and so the selection was not artifi-cially biased toward any of the tested theories. Voxels wereselected solely on the basis of whether they individually, or aspart of a pattern, reliability represented specific target people. Inthe presence of a real effect, this method of voxel selection willlead to larger effect sizes than using randomly selected voxelsdue to the reduction of correlation attenuation. However, unlikeproblematic forms of double-dipping, this technique will notlead to the creation or amplification of spurious relationships. Infact, the increase in effect size in this case only serves to providea more accurate estimate of the performance which would occur

under ideal noise free circumstances. Monte Carlo simulationsattesting to these facts are available in the OSF repository. Usingthe full set of targets and participants for feature selection mightintroduce a small bias into the cross-validation process describedbelow, but this same bias should affect permuted versions of themodels, and thus be easily measurable and discountable for thepurposes of significant testing, as described above with referenceto z-scoring. Moreover, in the representational similarity analy-ses described below, we directly compare the reliability-basedfeature selection with an independent mask, and find no evi-dence of bias in the former relative to the latter.

Feature Space ModelingFeature space modeling is a form of generative computationalencoding modeling of neural activity (Mitchell et al. 2008), inwhich feature dimensions are used to predict voxelwise activityand thereby reconstruct distributed patterns across the brain.In the present study, the 4 theories of person perception dis-cussed above, and the synthetic 3-component theory generatedby PCA, were analyzed and compared through feature spacemodeling. Three different forms of training and validation wereapplied to these encoding models to ensure the robust gener-alizability of our results.

Leave-One-Target-OutFirst, each model was trained separately for each of the 29 par-ticipants. Canonical patterns for each dimension were gener-ated by taking the average—weighted by z-scored dimensionratings—of 59 of the 60 targets. These canonical patterns werethen multiplied by the dimension ratings of the left-out targetperson and averaged to predict the actual pattern of neuralactivity associated with that target. This process was repeatedleaving out each target sequentially, with total accuracy foreach participant calculated as the average correlation betweenpredicted and actual patterns. In addition to calculating thesevalues within the feature-selected region, we repeated theanalysis with 7 brain networks defined by functional connectiv-ity in previous work (Yeo et al. 2011). For each network, we cal-culated the effect size in terms of Cohen’s d, allowing fordescriptive comparison amongst the networks. The goal of thisrepetition was to examine whether model fit was indeed betterwithin social brain regions (the default network) than in clearlynonsocial regions (e.g., visual and somatosensory cortices).

For the analyses conducted within the feature-selectedregions, confidence intervals (CIs) around mean performancewere obtained via percentile bootstrapping of participants (with10 000 samples). Percentile bootstrapping was again used to testpairwise difference in model performance via CIs around meandifferences with 100 000 bootstrap samples. The resulting CIswere Bonferroni-corrected to account for all pairwise compari-sons, and thus statistically significant results pass an uncor-rected 0.005 threshold.

Direct null hypothesis significance testing of model perfor-mance (versus chance) was also carried out non-parametricallyvia permutation-based prevalence testing (Allefeld et al. 2016).For each permutation (1000 total) of a given model, the coordi-nates of the target people in the dimensional space were ran-domly shuffled with respect to the corresponding patterns ofbrain activity prior to starting the cross-validation process.These individual-participant permutations were aggregatedinto second-level permutations by randomly sampling 1 of the1000 permutations for each participant. The minimum valuesof each of 108 such second-level permutations were combined


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into null distributions, and compared to the actual minimalperformance (across participants) from each model. We gener-ated P-values for the global null hypothesis of each model bycounting the number of times the permuted statistics werelarger than the true minimal statistics (plus one), and thendividing by the number of permutations (plus one). These val-ues allowed us to calculate the lower bound of the 95% CIaround the population prevalence of each model’s influence.This value can be obtained by subtracting the (Nth root of the)P-values described above from the (Nth root of the) of the αthreshold (i.e., 0.05(1/29)) and dividing the result by one minusthe (Nth root of the) P-values.

Leave-One-Participant-OutThe second cross-validation procedure was used to assess gen-eralization across participants rather than across target people.Patterns from 28 of the 29 were averaged to create a single setof 60, and these were then combined via dimension-weightedaverage to produce canonical patterns for each of the dimen-sions of the theories in question. Predicted patterns were thengenerated for each of the 60 target people based on the canoni-cal patterns and the targets’ scores on the dimensions. Thesepredicted patterns were then respectively correlated with the60 actual patterns from the left-out participant. The patternreconstruction correlations were averaged to produce a singlemeasure of model performance for that participant, and theprocess was repeated leaving out each of the remaining partici-pants in turn. The leave-one-participant-out cross-validationscheme also allowed for the estimation of a noise ceiling onperformance. To calculate this ceiling, the 60 patterns gener-ated by averaging across 28 participants were directly corre-lated with the corresponding 60 patterns from the left-outparticipant without intervention from any dimensional model.The resulting mean pattern correlations indicated the highestmeaningful performance a model could achieve, given theinterparticipant reliability of the data. Bootstrapping acrossparticipants is not appropriate in this case due to the nature ofthe cross-validation, but permutation testing as describedabove was again used to perform direct significance testing.

Across-Data Set PredictionThe final validation procedure was not cross-validation withinthe current sample, but rather prediction of a completely inde-pendent test data set. In a previous study of similar design, theauthors examined the neural representation of others’ mentalstates (Tamir et al. 2016). Two of the theories tested in the presentstudy—the stereotype content model and the agency and experi-ence model of mind perception—were also examined in thatstudy. Additionally, a synthetic 3-factor model emerged from PCAof the ratings of the mental states along 16 theoretical dimen-sions under consideration. In terms of component loadings, thedimensions of that model—which we named rationality, socialimpact, and valence—closely resemble the 3 factors extracted inthe present study: power, sociality, and valence respectively.Using the 2 extant theories and the 3-component synthetic the-ory, we tested whether encoding models trained on neural repre-sentations of others’ traits could reconstruct patterns of activityelicited by thinking about others’ mental states.

To achieve this, we extracted patterns for the 60 mentalstates in the earlier study from within the feature-selectedregions in the current study. We then averaged patterns acrossparticipants—29 in the current study, and 20 in the previousstudy—to produce 60 patterns of activity associated with target

people and 60 patterns associated with mental states. We gen-erated canonical patterns for the dimensions of the theories inquestion via weighted-averaging across the target person pat-terns as described above. We then generated predicted patternsbased on recombining the canonical patterns with weightsdetermined by the z-scored dimension ratings of the mentalstates. Pattern reconstruction accuracy was measured as themean correlation between predicted and actual patterns. Weagain calculated noise ceilings for the purpose of comparison,this time based on a leave-one-target-out cross-validation pro-cedure for each model within the mental states data. Theseceilings reflect how well each model could predict neural repre-sentations of mental states when trained on the same data set.

Note that this validation approach represents a particularlystringent challenge for the feature encoding models due to thedifferences between the 2 data sets. First, completely differentsets of participants took part in each study, making inter-individual generalization a necessary condition for successfulprediction. Second, numerous imaging parameters—such asspatial and temporal resolution—differed between studies.Third, although both studies involved making inferences aboutthe minds of other people, the mechanics of the tasks werequite different. In the current study, participants made infer-ences about how well statements applied to well-known tar-gets, whereas in the earlier study participants judged which of2 scenarios would elicit more of a particular mental state in ageneric other person. The influence of task differences on activ-ity patterns would likely work against the encoding models, atleast by adding noise. Finally, the stimulus-patterns corre-sponded to people in the present study, but to mental states inthe former study. Thus for the encoding models to successfullyreconstruct patterns, it would be necessary for a common neu-ral code—correlated with the theoretical dimensions—to repre-sent both people’s traits and mental states. The presence of aneural code transcending the trait-state boundary would be asubstantial discovery in itself, though in this case it representsbut one of many barriers to accurate pattern reconstruction.

Representational Similarity AnalysisRSA (Kriegeskorte et al. 2008) was used to probe the influence ofindividual dimensions on neural similarity. We employed repre-sentational similarity analysis as a complementary approach tothe feature encodings in 2 respects: First, it is much more compu-tationally tractable, allowing for the examination of numeroussingle dimensions, crossed with several methodological robust-ness checks, in a reasonable amount of time; Second, RSA makesa natural vehicle for the assessment of feature-less models whichonly describe pairwise differences between stimuli.

The 11 dimensions from the extant theories, as well as thedimensions of intelligence and attractiveness, were tested. Inaddition to these dimensional accounts, 2 holistic predictors ofpattern similarity were entered into the same analysis. Predictionsof dissimilarity between target people were calculated by takingall of the pairwise absolute differences between targets along eachdimension in turn. These measures were the average pairwiseholistic similarity ratings provided during pretesting, and the simi-larity measure computed from the targets’ Wikipedia text. Thesemeasures were converted to dissimilarities by reflection wherenecessary. We also included 3 task features as predictors in theRSA: the average button response and reaction time to each target,the character lengths of their names. Neural dissimilarity was cal-culated as the correlation distance between patterns of neuralactivity for each pair of targets. These neural dissimilarity


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measures were Pearson correlated with the predictions of dissimi-larity described above, considering only the nonredundantlower-triangular elements of each square dissimilarity matrix.Significance testing was conducted directly via permutation test-ing (N = 1000) by randomly flipping the signs of the correlationcoefficients to generate a null distribution for the average corre-lation, and indirectly via percentile bootstrapping (N = 10 000)across participants.

In addition to this “standard” analysis, which mirrored theanalytic path of the encoding models as closely as possible, wealso examined 5 variants to assess the methodological robustnessof our approach. In the first variant, we replaced the Pearson cor-relation between predicted and actual dissimilarities with aSpearman rank correlation, which has been recommended forthis purpose (Kriegeskorte et al. 2008). Second, we calculated pat-tern dissimilarity via Euclidean distance rather than correlationdistance. Third, we used unsmoothed regression coefficient mapsfrom the GLM, rather than smoothed patterns. Fourth, we usedan alternative independent feature selection approach—a maskdefined as sensitive to mental state representation in a previousstudy (Tamir et al. 2016).

Fifth, we controlled for 2 visual confounds in the imagingtask: the length of targets’ names, and the feedback providedduring the task (i.e., the selected value on the Likert scale light-ened slightly from gray to white). We computed the length oftargets names directly from character length, and calculated theaverage slider position for each target within each participant.These values were then converted to dissimilarity values by tak-ing the absolute differences between targets. The resulting RSAswere partial correlations between the theoretical dimensionsand neural pattern similarity, controlling for the dissimilaritiespredicted by the visual confounds. Note that this procedure isquite conservative, because it assumes that response-relatedvariance in pattern similarity stems solely from the visual feed-back. A plausible alternative might be that responses only corre-late with pattern similarity because responses are themselvesprimarily influenced by social dimensions.

Permutation testing and bootstrapping were repeated foreach variant. Additionally, noise ceilings were calculated foreach of the 4 method variants, as well as the standard model.This was achieved by averaging the neural dissimilarity matri-ces of 28 of the 29 participants and correlating the resultingcomposite with the neural dissimilarity matrix of the left-outparticipant. This process was repeated leaving out each partici-pant in turn, with the final noise ceiling taken to be the averagecorrelation across participants.

RSA was also used to assess the extent of overlap between the4 existing theories. In this analysis, the dissimilarity predictionsfrom the dimensions of each theory were fit to the average neuraldissimilarity matrix via multiple regression. The fitted valuesfrom these regressions were then correlated with each other.This approach effectively uses the neural data to determine theappropriate weighting for combining the dimensions within eachtheory. The resulting correlations of fitted values reflect theextent to which each of the theories makes the same accuratepredictions as the others with respect to neural pattern similarity.The 2 visual confounds described above were also included toestimate the degree of the confound.

ResultsBehavioral Results

Participants responded to an average of 96.5% (SD = 4%) of trialsin the imaging task, indicating high engagement. Responses

from the imaging task were combined with participants pre-scanning ratings of themselves with respect to the same socialjudgment items to calculate trial-wise self-other discrepancyscores. We analyzed these scores using mixed effects modeling,including fixed effects for similarity, reaction time, and theirinteraction, random intercepts for participant, social judgmentitem, and target person, and a random slope for reaction timewithin participant. Reaction times were mean centered, andtrials with reaction times less than 500ms were excluded fromanalysis (1.5%). Similarity ratings were scale-centered. Statisticalsignificance was calculated using the Satterthwaite approxima-tion for degrees of freedom. Analysis of the behavioral resultsreplicated a number of well-established findings in the litera-ture. Increased similarity between the participant and the targetpredicted diminished self-other discrepancy (b = −0.04, β =−0.10, P < 2 × 10−16), as predicted by the similarity-contingencymodel (Ames 2004). Longer reaction time predicted greater self-other discrepancy (b = 0.12, β = 0.07, P < 3 × 10−4), replicatingfindings on egocentric anchoring and adjustment in mentalizing(Epley et al. 2004). Finally, consistent with recent results (Tamirand Mitchell 2013), similarity moderated the relationshipbetween reaction time and self-other discrepancy, such thatanchoring and adjustment was observed more for similar thandissimilar targets (b = 0.03, β = 0.04, P < 2 × 10−5). The aforemen-tioned effects remained statistically significant when familiarityand liking ratings were also included in the model. Together,these results provide a clear indication that participants in theimaging experiment were performing mentalizing as previouslydefined in the literature.

The positions of the target people on the dimensions of 4extant theories were established through additional online norm-ing surveys. The idiosyncrasy of such perceptions was reflected inmodest average inter-rater correlations (Table 1). However, thecomposites all ultimately achieved high levels of reliability, withan average Cronbach’s α = 0.96 (SD = 0.02). We observed large cor-relations between the dimensions of person perception theories,indicating considerable redundancy between the predictions ofthe theory dimensions (Fig. 1A). We conducted PCA on the traitsto synthesize a single parsimonious theory that encapsulates theunique predictions of all rated dimensions. The 3 components ofthe optimal solution loaded most heavily on dominance, warmth,extraversion (Fig. 1B). To distinguish between manifest and latentvariables, we labeled the components power, valence, and social-ity, respectively. The components scores were used as the dimen-sions of a synthetic fifth theory. The synthetic theory thuseffectively spanned the representational spaces described byexisting conceptions of person perception, allowing us to use itto estimate an upper bound on the explanatory ability of existingresearch.

Feature Selection

The reliability-based feature selection method yielded a set of10216 voxels for further analysis. The regions selected over-lapped substantially with regions previously implicated insocial cognition (Mitchell et al. 2002; Saxe and Kanwisher 2003;Saxe and Wexler 2005; Mitchell 2009), including medial prefron-tal cortex, posterior parietal cortex, the temporoparietal junc-tion, and portions of the lateral temporal lobe (Fig. 2). Thisconfirms that thinking about different well-known target peo-ple elicits reliability differentiable neural activity within thesocial brain network.

We also examined the reliability of target pattern similarityin a number of a priori ROIs defined from functional connectivity


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networks in previous research (Yeo et al. 2011). We found thatthis reliability was highest in the default mode network (α =0.54). The lowest pattern reliability was observed in somatosen-sory (α = 0.13) and visual cortices (α = 0.20). Intermediate valueswere observed for other association cortex networks: dorsalattention—0.29, ventral attention—0.21, limbic—0.24, and fronto-parietal—0.47. The residual pattern similarity reliability in thedefault network—controlling for pattern similarity in the visualand somatosensory cortices—was 0.49. Together these resultssupport the conclusion that target-specific activity patterns aremost pronounced in the default/social brain network, and helpto rule out any possibility of visual or motor confounds mightaccount for observed effects.

Feature Space Modeling

In the first cross-validation approach, encoding models were fitseparately to each participant’s data and tested using a leave-one-target-out procedure, in which performance was measuredin terms of the reconstruction accuracy of the model (i.e., the cor-relation between predicted and actual patterns for the left-outtarget person). Results reflected statistically significant (Ps <0.001) performance for all 5 theories (Fig. 3A) as assessed boot-strapping. The lower bound of the 95% CI on population preva-lence of each model was 77%, though this figure is likelyconservative, as it is determined exclusively by the sample sizeand number of permutations we could compute in a reasonabletime (Allefeld et al. 2016). Average pattern reconstruction accura-cies were 0.070 for the 5-factor model (Cohen’s d = 1.89), 0.080 forthe mind perception model (d = 2.16), 0.083 for the social face per-ception model (d = 1.94), 0.081 for the stereotype content model(d = 1.90), and 0.104 for synthetic the 3 principal component model(d = 2.41). The pattern reconstruction values reflect the averagecorrelation between predicted and actual patterns of neural activ-ity for each target person. Model performance (as measured byCohen’s ds) in analogous analyses conducted in brain networksderived from functional connectivity (Yeo et al. 2011), yielded con-sistent results (Table 3). The 3-component synthetic modelachieved the highest performance in all brain network except thelimbic system, where it was slightly outperformed by the stereo-type content model. Similarly, model performance was highest inthe default network for every model except the stereotype con-tent model, which was better fitted to the limbic system.

Pairwise indirect difference tests (via bootstrapping) in thereliability selected regions indicated that the 3-componentmodel significantly outperformed the 4 extant models, and thesocial face perception model significantly outperformed the 5

factor personality model (Table 4). Note that the latter outcomedemonstrates that more parsimonious theories (the 2-D faceperception model) can indeed outperform more complex theo-ries (the 5-factor model) in the present framework, despite theabsence of discounting for higher dimensional theories.

The second validation procedure was leave-one-participant-out cross-validation: the feature space encoding models weretrained on patterns averaged across all but one participant andthen tested on the excluded participant, iteratively. Again, all 5models performed significantly above chance (Ps < 0.001). Thefact that the encoding models generalized across participantsindicates the existence of a common representational topologyacross brains—that is, the same voxels appear to encode thesame dimensions in the brains of different participants. Theabsolute levels of pattern reconstruction accuracies were some-what lower in this case then when trained and tested withinparticipant: 0.063 for the 5-factor model, 0.053 for the mind per-ception model, 0.056 for the social face perception model, 0.061for the stereotype content model, and 0.072 for the 3 principalcomponent model. This drop in performance indicates that notall of the reliable variance in neural activity patterns is sharedacross participants, though this may be merely due to imper-fect alignment rather than substantively idiosyncratic codingschemes. Note that we cannot report valid Cohen’s ds for thisanalysis because the outcomes of individual participants are nolonger independent due to the nature of the cross-validationprocedure. It is worth observing that the 5-factor model—whichhad the worst performance in the leave-one-target-out case—here outperformed the other 3 extant models. This hints thatthe neural topography of the encoding of the Big 5 traits maybe more universal than that of, for instance, the social face per-ception dimensions.

The leave-one-participant-out cross-validation approachallowed for an alternative way of assessing performance: rela-tive to the possible performance attainable given the sharedvariance across participants. To implement this approach, wedivided the raw performance values above by the data’s noiseceiling. The results indicated that the 5 models achievedapproximately half to two-thirds of the maximum performancepossible given the variance shared across participants: 0.59 forthe 5-factor model, 0.49 for the mind perception model, 0.52 forthe social face perception model, 0.56 for the stereotype con-tent model, and 0.67 for the 3 principal component model. Weapplied the same approach to voxelwise model performance:that is, we calculated correlations between predicted and actualactivity across the 60 targets (Fig. 4) within each voxel of thefeature-selected region.

Figure 2. Reliability-based feature selection. Univariate reliability across targets (A) was calculated for each voxel via the formula for standardized Cronbach’s α. Thereliability of the correlation matrix between patterns was assessed at each voxelwise reliability threshold between 0 and the observed maximum (B). The voxelwise

reliability threshold that maximized pattern correlation reliability was used to select voxels for further analysis (C).


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In a third validation method, we used a completely indepen-dent data set in a study which probed the neural representa-tion of mental state representation (Tamir et al. 2016). Despitethe particularly high barriers to accuracy in this case, 2 of the 3theories successfully reconstructed patterns in the other dataset (Fig. 3B)—the stereotype content model, with a mean pat-tern reconstruction of 0.083 (P < 0.005), and the 3-componentmodel, with a mean pattern reconstruction of 0.12 (P < 0.001).

The mind perception model failed to significantly predict, witha mean pattern accuracy of −0.0003 (P > 0.1). Both successfultrait-trained theories achieved 0.54 of their models possibleperformance by this metric. Pairwise difference testing indi-cated that the synthetic 3-component model significantly out-performed the mind perception model. There was considerablevariance in the accuracy of pattern reconstruction between dif-ferent states, but we observed negligible correlations (rs < 0.1)

Figure 3. Feature space modeling performance. Large black circles indicate mean pattern reconstruction, and the error bars around them indicate 95% CIs. Brackets

indicate significant pairwise differences. Boxplots indicate empirical null distributions with whiskers at the 0.975 and 0.025 quantiles. Model performance in leave-

one-target-out cross-validation analysis (A) was above chance for all 5 models. Small gray points indicate the performance of individual participants. Across-data set

pattern reconstruction accuracy (B) was above chance for 2 of 3 theories—stereotype content and the synthetic 3-component model. Small gray circles indicate pat-

tern reconstruction accuracy for each of 60 states.

Table 3 Leave-one-target-out cross-validation performance (Cohen’s d) by brain network

Brain network Five factor personality Mind perception Social face perception Stereotype content Three component

Visual 0.94 1.28 1.10 1.30 1.33Somatosensory 0.71 0.77 1.06 0.95 1.17Dorsal attention 1.27 1.52 1.44 1.51 2.00Ventral attention 0.74 1.04 0.99 0.86 1.24Limbic 1.34 1.13 1.06 1.74 1.61Frontoparietal 1.19 1.32 1.39 1.39 1.68Default 1.45 2.16 1.73 1.59 2.03

Table 4 Pairwise model comparisons in leave-one-target-out cross-validation

Pair Model 1 Model 2 Bootstrap medians 95% CI lower bound 95% CI upper bound

1 Five factor personality Mind perception −0.010 −0.027 0.0092 Five factor personality Social face perception −0.013 −0.027 0.0003 Five factor personality Stereotype content −0.010 −0.022 0.0014 Five factor personality 3-component −0.034 −0.044 −0.0245 Mind perception Social face perception −0.004 −0.018 0.0126 Mind perception Stereotype content −0.001 −0.018 0.0147 Mind perception 3-component −0.025 −0.041 −0.0108 Social face perception Stereotype content 0.003 −0.011 0.0189 Social face perception 3-component −0.021 −0.033 −0.00910 Stereotype content 3-component −0.024 −0.035 −0.012

Values reflect differences in mean pattern reconstruction (r) between theoretical models (Model 1–Model 2). CIs are Bonferroni-corrected for multiple comparisons.


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between accuracy and the dimension of the theories and noobvious pattern in which patterns were most (in)accuratelyconstructed.

The functional topography of the 3-component model(Fig. 5) trained on the across-participant averaged data suggestsgeneral activation in response to higher power targets, generaldeactivation in response to higher valence (more positive) tar-gets, and a mixture of activation and deactivation for high soci-ality targets. However, allowing for these broad directionaldifferences, there was considerable heterogeneity in terms ofwhich regions were more or less associated with each compo-nent dimension. Correlating these maps voxelwise with theanalogous correlation maps produced from the mental statesdata set (after Fisher’s transforming both), we observed valuesof r = 0.43 for power/rationality, r = 0.28 for valence, and r =0.29 for sociality/social impact. This suggests that the dimen-sion of power/rationality is the most conserved across the trait-state divide.

Representational Similarity Analysis

RSA was conducted to test the ability of individual dimensions toexplain the (dis)similarity of patterns of neural activity associatedwith different target people (Fig. 6). The 2 pairwise predictors—holistic ratings and Wikipedia text—clearly achieved the best

performance. However, only one psychological dimension—neuroticism—consistently failed to significantly predict patternsimilarity. The task features of average response button (1–5)and reaction time for each target both predicted pattern simi-larity, but the character length of the target names did not.

Examining the influence of methods also yielded clearresults: results for each dimension were highly consistentacross variant methodologies. The standard and Spearmananalyses performed almost identically in every case. The alter-native feature selection also produced results consistent withthe standard approach, though sometimes slightly better orworse. The consistency between these features selection meth-ods confirms the unbiased nature of the reliability-based fea-ture selection. The analysis of the unsmoothed data was highlyassociated with the other methods across dimensions, but wasconsistently lower in absolute value (though frequently greateras a proportion of noise ceiling). The Euclidean distance metricwas the least consistent methodological variant—it yieldedhighly variable estimates both within dimensions and acrossmodels. Controlling for potential visual confounds (responsefeedback and target name character lengths) resulted in reduc-tions of the brain-behavior relationship across dimensions.Three previously significant dimensions were reduced to non-significance by controlling for these features: agency, agree-ableness, and warmth. However, again, many of the predictors

Figure 4. Voxelwise model performance in leave-one-participant-out cross-validation. Voxelwise correlations between model predictions and actual activity in a

leave-one-participant-out cross-validation analysis are shown for each of the five theories under consideration: the 5-factor model (A), the mind perception model

(B), the social face perception model (C), the stereotype content model (D), and the synthetic 3-component model (E).


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that were significantly related to pattern similarity in the stan-dard analysis remained so, and the overall pattern of perfor-mance across models remained highly consistent with theother method variants. This suggests that the performance ofthe encoding models may be somewhat inflated, but cannot beentirely attributed to these confounding factors.

Multiple regression RSA indicated considerable redundan-cies among the 4 extant theories, in terms of the (accurate) pre-dictions they made about neural pattern similarity. Weobserved correlations between fitted values from the substan-tive models and the fitted values of the putative visual con-founds of 0.61 for the 5-factor model, 0.40 for the mindperception model, 0.83 for the social face perception model,and 0.45 for the stereotype content model. Combined with theother RSA results reported above, this suggests that theresponse button makes similar prediction to the extant models.Correlations between fitted values for the extant models rangedfrom 0.36 to 0.69. The predictions of the mind perception modelwere the most unique, with correlations of only r = 0.35 withthe 5-factor model, and rs = 0.36 for both the social face percep-tion and stereotype content model. The 5-factor model wasinvolved in the 2 highest correlations between model fitted val-ues: r = 0.69 with the social face perception model, and r = 0.63with the stereotype content model. The social face perceptionand stereotype content models made moderately similar accu-rate predictions about neural pattern similarity: r = 0.47.Together these results suggest that the theories in questionattained their similar accuracy for fairly similar reasons.

DiscussionIn the present study, we tested how well 4 prominent theoriesof person perception (McCrae and Costa 1987; Goldberg 1990;Fiske et al. 2002; Gray et al. 2007; Cuddy et al. 2008; Oosterhofand Todorov 2008)—and 1 synthetic theory combining theirdimensions—predict patterns of neural activity elicited by

mentalizing. The primary result was clear: encoding modelsbased on all 5 of theories performed substantially abovechance. Indeed, each encoding model positively predicted thebrain activity of nearly every participant, and performedbetween half and two-thirds as well as a hypothetical ideal the-ory. This outcome strongly supports the idea—shared acrossthe tested theories—that people represent others within a mul-tidimensional social representational space. This indicates thata distributed population code linearly encodes psychologicaldimensions used to organize social knowledge. Moreover, thefact that the theory-predicted neural activity patterns were eli-cited by making a variety of social inferences suggests that thebrain may draw upon a target person’s coordinates within therepresentational space to inform judgments about that target.In other words, the dimensional theories tested here may serveas a substantial part of the informational basis of mentalizing.

The results of 3 model validation approaches suggest thatthe theories in question are highly generalizable. Cross-validation indicated that the models could generalize to pat-terns of activity associated with “unseen” targets. In otherwords, encoding models trained on the current data set shouldbe able to predict patterns of activity associated with thinkingabout any other person, assuming that person’s coordinates onthe relevant dimensions are known. The encoding models alsoeffectively generalized across participants, indicating that thedimensions of the 5 theories in question are encoded with acommon functional topography shared across brains. This find-ing suggests that the content-based organization of the por-tions of neocortex involved in social cognition may ultimatelyresemble that of better-understood regions, such as sensorycortices, which possess highly consistent topographic maps(Kaas 1997).

The ability of 2 theories of person perception—the stereo-type content model and our synthetic 3-component model—toreconstruct activity patterns across-data sets also carries signif-icantly implications. This generalization indicates that the

Figure 5. The functional topology of the 3-component encoding model. Colors reflect a voxelwise mapping of correlations between average univariate activity and the

3 principal components of the synthetic theory: power (A), valence (B), and sociality (C). Activity in orange areas is thus positively associated with thinking about

dominant, warm, and extraverted public figures, respectively.


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neural patterns that encode theoretical dimensions are robustto many low-level differences between data sets, such as par-ticipant samples, imaging parameters, and task demands.Overcoming these barriers makes the encoding models farmore useful in practice, as they can potentially be appliedacross many data sets without the need to train the modelsseparately for each sample or participant.

Moreover, the encoding models generalized despite the factthat they were trained on data in which patterns representedindividual people, and then tested on data in which patternsrepresented mental states. This finding suggests that a commonneural code represents both the temporary and enduring fea-tures of other people. Thus, the way in which we think about aperson who is momentarily feeling a positive state, such as“happiness,” may be fundamentally similar to the way we thinkabout a person who manifests a lasting positive trait, such as“trustworthiness.” Although the biological reality of personalitytraits and mental states might differ, the brain appears to repre-sent our lay conceptions of them using at least some of the

same dimensions, and encodes those dimensions with similarpatterns of brain activity. This finding helps to clarify the degreeto which the brain recognizes the trait-state divide (at least at aconceptual level), a topic of longstanding interest to psycholo-gists (Mischel 1968).

Comparing the 4 extant theories of person perceptionrevealed surprisingly similar performance. There were fewpairwise differences in model performance, and most thatwere observed stemmed solely from the superior performanceof the synthetic theory. Moreover, all of the theories appearedto explain neural activity to similar degrees across differentbrain regions. The behavioral predictions of the theoreticaldimensions were often highly correlated, and the accurateneural predictions of the extant theories also overlapped consid-erably. These results suggest that the study of several differentphenomena—intergroup affect, personality, mind perception,and face perception—may have given rise to generally similartheories of person perception. This may suggest that the brainapplies a single unifying approach to organizing social knowl-edge, rather than developing specialized representational frame-works for different social domains.

Across all testing approaches, our 3-component synthesis ofthe dimensions of the extant theories outperformed all of theoriginal theories. As stated above, it also closely approximates—and cross-decodes—a similar model of mental state representa-tion. On this basis, we suggest that people may consistentlyconsider 3 fundamental qualities of other people: the likelihoodof another person interacting with them in a significant way(sociality/social impact), the ability of that person to enact theirwill (competence/power), and the likelihood that they will begood or bad (valence). These qualities will depend on both thetraits and states of others, accounting for the partially sharedrepresentational space. Determining others’ coordinates withinsuch a three-dimensional space would make it possible tocogently engage in a wide range of adaptive behavioralresponses. Directly testing this adaptive account of socialknowledge organization should be a topic for future research. Ifvalidated, this framework might suggest additional dimensionswhich help complete our theories of person perception.

The performance of the synthetic principal componentencoding model could be compared to noise ceilings in 2 of the3 cross-validation procedures we conducted (across participantsand data sets). Dividing the average pattern reconstruction ofthe synthetic theory by these noise ceiling indicates how wellthe theory does in comparison to a hypothetical ideal theory.Since the synthetic theory combines the components of the 4theories we considered, as well as the dimensions of intelli-gence and attractiveness, its accuracy offers a rough estimate ofthe overall explanatory power of the person perception litera-ture. The results thus indicate that our modern theories predictthe neural content of person perception about half to two-thirdsas well as a hypothetical ideal model. Whether this outcome isencouraging or discouraging depends on one’s perspective, butwe suggest that it is quite positive, given the nascent state ofcomputational approaches in social psychology. In comparison,considerably more complex computational models have beenapplied to visual cortex with less success (Khaligh-Razavi andKriegeskorte 2014). What an ideal theory of person perceptionwill look like remains unknown—it may simply require theaddition of more dimensions, or it may require the relaxation ofthe assumption that linear dimensions characterize the entirerepresentational space. Application of structure-discovery algo-rithms (Kemp and Tenenbaum 2008) to condition-rich data setsmay help to address such questions.

Figure 6. RSA. The correlation between neural pattern dissimilarity and 15 dif-

ferent predictors was assessed under a standard analysis reflecting the feature

space encoding models and 5 reasonable methodological variants. Colored cir-

cles indicate the magnitude of the correlations for each predictor, with 95% CIs

around them. Gray circles indicate noise ceilings, and gray bars indicate the

0.975 quantile of permutation distributions.


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Notably, all encoding models in the present study relied on astrong assumption: that psychological dimensions are encodedby the linear activation of a single canonical activity pattern.Testing under this assumption allows us to go beyond merelyasserting that the social brain network contains person-specificactivity patterns, to describe the nature of the encoding schemewithin the network—that is, the meta-theory of a representa-tional space for other people, as a opposed to other schemes suchas sparse or temporal coding. The linear dimensional meta-theory is among the simplest imaginable population codingschemes, and yet despite this naïve assumption, we observedremarkably robust performance. It is possible that modelingbased on more complex assumptions—for example, genuinelybipolar dimensions, or nonlinear mappings between activity pat-terns and psychological dimensions—might produce even betterperformance. However, we eschew such elaboration for themoment, both because more complex techniques are commensu-rately harder to interpret, and because they are prone to overfit-ting, especially with the limited amount of data available to us atpresent. The fact the models provide quite accurate despite theirsimplicity carries implications for our understanding of how thebrain encodes social knowledge. Specifically, this results suggestthat additive patterns of brain activity, distributed across thesocial brain network in relatively coarse activity patterns, are line-arly related to recognizable dimensions from psychological theo-ries. Arbitrary “pointer” patterns for individual people may stillexist in the brain—perhaps as finer spatial scales, such as withinthe hippocampus—but a sparse coding account is not supportedby the present findings. Thus, these results validate the overallmeta-theory of a representational space for people, as well as thespecific instantiations of that meta-theory which we borrowedfrom the psychological literature.

The results of the present study provide support for the con-clusions of a number of previous investigations. A recent fMRIinvestigation found that the Big 5 dimensions of agreeablenessand extraversion could be decoded from portions of the socialbrain network, findings we replicate using RSA (Hassabis et al.2014). In their investigation of hippocampal brain activity,Tavares et al. (2015) found evidence for a social representa-tional space based on the dimensions of “power” and “affilia-tion.” Although we do not observe reliable target-related signalin the hippocampus itself, we do observe that the conceptuallysimilar dimensions of competence and warmth from the ste-reotype content model quite accurately predict patterns ofbrain activity in the limbic system more generally (Table 3).Purely behavioral results from our dimension pretesting studiesalso support recent research that suggests that valence can beseparated into social and moral components (Brambilla andLeach 2014; Goodwin 2015).

Our examination of method-related variance in the presentstudy suggests that the conclusions we draw are quite robustto reasonable alterations of our analytic approach. However,the relative importance of the various trait dimensions we con-sider still likely depends in part on the particular stimuli andtask used in this study. Making inferences about others’thoughts, opinions, feelings, and preferences is a fairly generalsocial process, but is certainly not representative of all possiblecontexts in which person perception might occur. The use ofmore naturalistic stimuli and tasks would serve to expand theconclusions one might justifiably make from this research.Furthermore, even with fairly elaborate stimulus selectiontechniques, the constraint of using well-known target peopleplaces severe strain on the representativeness of the targets.Replicating this work with alternative sets of target people will

be crucial in ensuring our results extend to the broader set ofpotential real-world mentalizing targets.

The visual confound of character name length and responsebutton feedback (the selected response lightened slightly whenchosen) place an additional limitation on the interpretation ofthe present findings. These variables account for the influenceof 3 psychological dimensions on pattern similarity, suggestingthat the performance of the encoding models may be some-what inflated. However, the effect of character length was vir-tually zero, indicating that only the response button sharesvariance with both pattern dissimilarity and psychologicaldimensions. Because the purely visual character length con-found had no effect, the ambiguous response button effect—which could be mediated visually or socially—is more likelydriven by meaningful social considerations than by spuriousvisual differences. Moreover, visual cortex activity cannotexplain the vast majority of pattern similarity within the socialbrain network, nor can visual confounds explain any of thecross-data set accuracy of the encoding models. Ultimatelymore research is needed to decouple the social properties oftarget people from low-level task features. Adopting more nat-uralistic approaches may also help address this issue andothers by limiting low-level confounds to those found ecolog-ically in typical experience.

Despite these limitations, this study offers useful insightregarding the effectiveness of several theories of person per-ception and demonstrates a new method for directly testingsocial cognitive theories using neuroimaging. This approachholds considerable promise as a way to compare ideas fromdiverse areas of the literature which would otherwise be diffi-cult to integrate. By exploring these domains within a commonframework, we may discover a great deal about the regularity(or lack thereof) of social representations in the brain. Moregenerally, we believe that use of such approaches will helpmove the field in a more cumulative, theory-driven directionand enable more fruitful interaction between psychology andneuroscience. Despite assertions (Newell 1973) that one cannot“play 20 questions with nature and win,” social and personalitypsychologists appear to have assemble a set of remarkablyrobust and generalizable theories regarding the content of per-son perception. By our current estimates, they are indeed morethan half way to “winning” this particular game.

FundingNational Science Foundation Graduate Research Fellowship(DGE1144152) and by The Sackler Scholar Program inPsychobiology (to M.A.T.).

NotesThe authors would like to thank Juan Manuel Contreras,Spencer Dunleavy, Talia Konkle, Abigail Orlando, JoakimNorberg, Franchesca Ramirez, Ryan Song, and Diana Tamir fortheir assistance. Conflict of Interest: None declared.

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