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Habits often conflict with goal-directed behaviors and this phenomenon has been convincingly demonstrated in instrumental learning experiments. Recent computational models explain habit-goal conflicts as the competitions between two learning systems, arbitrated by a central unit. In this paper, we show that habit-goal conflicts can be more parsimoniously explained by a dynamic integration of habit and goal values in a sequential sampling model, without any arbitration. A computational model was developed by extending the multialternative decision field theory with the assumptions that habits bias starting positions of preference accumulation, and that goal importance and goal relevance determine sampling probabilities of goal-related attributes. Simulation studies demonstrated our approach’s ability to qualitatively reproduce important empirical findings from three paradigms – classic devaluation, devaluation with a concurrent schedule, and reversal learning, and to predict gradual changes in decision times. Implications of our work for a unified theory of instrumental learning and value-based decision-making are discussed.

Mental images of social categories are highly consequential: They can reveal biases and help elucidate the factors that contribute to those biases. One strategy frequently used to evaluate the properties of mental images is reverse correlation, which is a data-driven method that allows researchers to visualize a person’s mental representation of individuals or groups. In social psychology, this technique often employs a unique two-phase structure. This approach, however, has not yet been carefully validated, and its structure may alter the properties of the statistical tests used to evaluate differences between conditions. Using computer simulations to evaluate the Type I error rate in a typical two-phase reverse correlation procedure, we find that it is inflated in a nontrivial set of circumstances.

The principle of compositionality, an important postulation in language and cognition research, posits that the meaning of a complex expression is determined by the meaning of its constituting parts and the operation performed on those parts. Here, we provide strong evidence that this principle plays a significant role also in interpreting facial expressions. In three studies in which perceivers interpreted sequences of two emotional facial expression images, we show that the composite meaning of facial expressions results from the meaning of its constituting expressions and an algebraic operation performed on those expressions. Our study offers a systematic account as to how the meaning of facial expressions (single and sequences) are being formed and perceived. In a broader context, our results raise the possibility that the principle of compositionality may apply to human communication modalities beyond spoken language, whereby a minimal number of components are expanded to a much greater number of meanings.

Judgments we make about others often depend on the standards we use as comparisons. Investigations into the outcomes of these comparisons and potential moderators have often been limited to single dimensions and preselected standards. The current work instead uses multiple evaluative facial dimensions and a multitude of comparisons. A series of 4 experiments ('N' = 665) attempted to detect contrast from extreme (Study 1) and assimilation to moderate standards in within (Studies 2 and 3) and between-subjects designs (Study 4). Results showed inconsistent evidence for both comparison effects and significant heterogeneity across the evaluative dimensions that were sampled. An additional 5 studies ('N' = 861) and a single-paper meta-analysis ('K' = 7) revealed judgment dimension specific dynamics. Facial Extraversion produced both assimilation and contrast effects as expected; Dominance and Competence displayed only contrast; Trustworthiness showed only assimilation effects; and Likability presented no signs of either. The resulting implications for theory and measurement are discussed.

One of the two miscomputations identified in the infoVal metric, namely the omission of the k constant, turns out not to be a miscomputation, since the constant was already taken into account by default in the mad() function from R (see https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/mad ).

Clinical practice still relies heavily on traditional paper-and-pencil testing to assess a patient’s cognitive functions. Digital technology has the potential to be an efficient and powerful alternative, but for many of the existing digital tests and test batteries the psychometric properties have not been properly established. We validated a newly developed digital test battery consisting of digitized versions of conventional neuropsychological tests. Two confirmatory factor analysis models were specified: a model based on traditional neuropsychological theory and expert consensus and one based on the Cattell-Horn-Carroll (CHC) taxonomy. For both models, the outcome measures of the digital tests loaded on the cognitive domains in the same way as established in the neuropsychological literature. Interestingly, no clear distinction could be made between the CHC model and traditional neuropsychological model in terms of model fit. Taken together, these findings provide preliminary evidence for the structural validity of the digital cognitive test battery.

Trustworthiness and dominance impressions summarize trait judgments from faces. Judgments on these key traits are negatively correlated to each other in impressions of female faces, implying less differentiated impressions of female faces. Here we test whether this is true across many trait judgments and whether less differentiated impressions of female faces originate in different facial information used for male and female impressions or different evaluation of the same information. Using multidimensional rating datasets and data-driven modeling, we show that (a) impressions of women are less differentiated and more valence-laden than impressions of men and find that (b) these impressions are based on similar visual information across face genders. Female face impressions were more highly intercorrelated and were better explained by valence (Study 1). These intercorrelations were higher when raters more strongly endorsed gender stereotypes. Despite the gender difference, male and female impression models-derived from separate trustworthiness and dominance ratings of male and female faces-were similar to each other (Study 2). Further, both male and female models could manipulate impressions of faces of both genders (Study 3). The results highlight the high-level, evaluative effect of face gender in impression formation-women are judged negatively to the extent their looks do not conform to expectations, not because people use different facial information across genders but because people evaluate the information differently across genders. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Perceptual conscious experiences result from non-conscious processes that precede them. We document a new characteristic of the cognitive system: the speed with which visual meaningful stimuli are prioritized to consciousness over competing noise in visual masking paradigms. In ten experiments (N = 399) we find that an individual's non-conscious visual prioritization speed (NVPS) is ubiquitous across a wide variety of stimuli, and generalizes across visual masks, suppression tasks, and time. We also find that variation in NVPS is unique, in that it cannot be explained by variation in general speed, perceptual decision thresholds, short-term visual memory, or three networks of attention (alerting, orienting and executive). Finally, we find that NVPS is correlated with subjective measures of sensitivity, as they are measured by the Highly Sensitive Person scale. We conclude by discussing the implications of variance in NVPS for understanding individual variance in behavior and the neural substrates of consciousness.

Most research on emotion recognition focuses on facial expressions. However, people communicate emotional information through bodily cues as well. Prior research on facial expressions has demonstrated that emotion recognition is modulated by top-down processes. Here, we tested whether this top-down modulation generalizes to the recognition of emotions from body postures. We report three studies demonstrating that stereotypes and prejudice about men and women may affect how fast people classify various emotional body postures. Our results suggest that gender cues activate gender associations, which affect the recognition of emotions from body postures in a top-down fashion. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Not so long ago, in the early days of experimental psychology, researchers performed their data analyses by hand (or paid people to do these calculations for them). No wonder, they took ample time beforehand to think through which analyses to perform (and especially which not). Exploring data was not really an option. The t value or F value that resulted from the calculations provided a more or less definitive answer. H0 could be rejected or not. During the past decades, we have quickly moved from punch cards, via expensive mainframes, to relatively cheap but powerful personal computers that are fulltime available to individual researchers and students. Nowadays, SPSS and other user-friendly statistical packages make it easy to run all types of analyses, which encourage analyzing your data in every possible way. It is stunningly easy to (re-)run analyses with or without a specific group of participants, with or without an additional between-subject condition, with or without an extra variable in a scale. Indeed, it may be a waste of resources to leave a dataset partly unexplored when SPSS offers all the tools to do so. In many research labs, analyzing results has shifted from spending days to do confirmatory analyses by hand, to spending days engaging in all kinds of data analyses to see "what’s in the data.” Is this a bad thing? We do not think so, necessarily. Given that you know what you are doing in SPSS, you can learn a lot from exploring a dataset. However, there clearly are risks (see Simmons, Nelson & Simonsohn, 2011), which we will revisit later. This contribution is a commentary on "Playing with Data—Or How to Discourage Questionable Research Practices and Stimulate Researchers to Do Things Right” by Sijtsma (2015). In his paper, Klaas Sijtsma makes the important point that prevention of questionable research practices may be more important than detection. He suggests two policy measures. First, make research data and research materials publicly available. Second, encourage researchers to consult methodologists or statisticians for help and a second opinion. In this commentary, we will not reiterate the points made by Klaas Sijtsma. We support his analysis and his policy recommendations. Making research data and research materials publicly available and consulting methodologists and statisticians will help in increasing the transparency of the data analysis phase of the empirical research cycle within psychology. Currently, in too many cases, this phase is the exclusive domain of (small groups of) individual researchers and their personal computers. However, making the data analysis phase transparent to others will not prevent scientific fraud. A person who willingly aims to fool others always will be able to do so, also when sharing (well-faked) data. Nevertheless, it will help in reducing the errors researchers may make unwittingly due to incorrect methodological decisions and the incorrect use of statistical methods (see Sijtsma). The current contribution focuses on what we think is quickly becoming a misnomer in our field, namely the use of the phrase "questionable research practices” for certain types of practices when analyzing and reporting data. We suggest to replace this with the, in our view, more accurate phrase "questionable reporting practices,” in relation to most potentially problematic data analysis strategies. The abbreviation may remain the same, namely QRPs. However, the meaning and use of the phrase will be more positive and more encouraging to junior and senior researchers, and will do more justice to the important distinction between confirmation and exploration when analyzing data and reporting results (see Wagenmakers, 2012).

Background: The ability to perceive, recognize and process own and others’ emotions is crucial for efficient and effective social communication. Many different tasks have been used to investigate impairments herein in patients with schizophrenia. Evidence suggests that perception, discrimination and recognition of affective facial expressions are impaired in schizophrenia patients (Green, Horan & Lee, 2015; Chan et al 2010). Importantly, not everyone may interpret the same facial expression similarly. People match their internal representation of specific facial expressions to perceived faces and variation in these internal representations may result in distortions of social reality. The impairments in face and/or emotion processing and the bias toward a more negative experience may be causally related to degradation of the internal representation itself or to disturbances in the higher order evaluation of visual input against functionally intact internal representations. Methods: We use a data-driven technique of psychophysical reverse correlation, which makes it possible to visualize internal representations on computer screens. Participants judge noisy images of faces that are created by superimposing random noise on a single constant base face. The random noise distorts the base face at the pixel level, generating facial variation across stimuli that is fully unconstrained and unaffected by researchers’ a priori expectations. The participants’ responses to a large number of faces are then used to model the facial information that was idiosyncratically diagnostic for the judgments. This analysis yields a classification image (CI) for each participant, which visualizes the facial characteristics that drive judgments of emotional expressions (i.e., their internal representation). Specifically, we used reverse correlation image classification (RCIC) to investigate and reconstruct the mental representation of trustworthiness as expressed on the face. Results: Thus far, we have reliable data from 11 patients and 9 controls. While our results are preliminary, they show that patients are capable of performing the task adequately. Both a visual inspection of the group-level visualizations of the 3 constructs probed (i.e., untrustworthy, neutral, and trustworthy) as well as the computed correlations between these visualizations suggest that the internal representation of these constructs is less expressive in patients when compared to controls. Conclusion: Being able to visualize mental representations of trustworthy and untrustworthy faces in patients with schizophrenia opens up the possibility of further use of RCIC tasks in the investigation of emotion-processing deficiencies in schizophrenia.

Objective We developed a cloud-based platform, Philips Intellispace Cognition (ISC), containing tablet-based versions of conventional cognitive tests. We investigated the battery’s sensitivity for subtle cognitive impairment in a mixed clinical sample. Method The sample consisted of 221 healthy participants and 98 traumatic brain injury (TBI) and stroke patients who all scored above the MMSE-2 cutoff of 24, but reported subjective cognitive complaints. All completed the following ISC tests: Phonetic Fluency; Semantic Fluency; Trail-Making; Stroop; O-Cancellation; Star-Cancellation; Digit Span; Rey Auditory Verbal Learning; Rey-Osterrieth Complex Figure. We used ROC analyses to investigate our ability to distinguish the patient and healthy samples. Specifically, we assessed the area under the curve (AUC), where a value of .50 indicates random classification and 1 indicates perfect distinguishability. To this end, we compared sensitivity of cognitive domain scores distilled from the ISC tests (using confirmatory factor analysis) with the sensitivity of MMSE-2 scores. The cognitive domains were executive functioning, processing speed, working memory, memory, and visuospatial ability. Results The AUC of the MMSE-2 was .46, as expected given the fact that all patients scored above the cut-off. The AUCs of the model domain scores ranged from .56 to .73. The differences between these AUCs and the MMSE AUC were all significant (ps ≤ .004), except for visuospatial ability (p = .070). Conclusions The ISC test battery was able to pick up on cognitive impairment in a mixed clinical sample consisting of patients that all scored normally on the MMSE-2. These findings show the usefulness of the digital platform in clinical practice.

Objective To validate a fully automated scoring algorithm for the Rey-Osterrieth Complex Figure Test (ROCFT) by comparing the scoring results of the algorithm to the results of human raters. Method The algorithm consisted of a cascade of deep neural networks which were trained on human rater scores to extract the 18 segments of the figure, and to quantify the patient’s performance. Algorithm results were compared to six expert raters for 303 drawings. We tested whether the average correlation between algorithm scores and scores by all human raters was equivalent to the average inter-rater correlation (with equality bound Δr < .05). The immediate and delayed recall trial were used; the copy trial showed a strong ceiling effect. Results The mean Pearson correlation between raters was .94 (SD = 0.01). The correlation between to algorithm and the raters was .88 (SD = 0.02). A two-one-sided t-tests (TOST) equivalence test showed that these correlations were not strictly equivalent, t(5) = 4.02, p = .995, 95% CI [0.35, 0.52]. Conclusions Although not strictly equivalent to human ratings, the algorithm’s performance is high, approaching a level of reliability found among human raters. We expect that improved individual segment detection will bring the algorithm scoring accuracy on par with that of human raters. Algorithmic scoring of the ROCFT will likely save valuable time and lead to higher levels of standardization in clinical practice.

Objective Following recent (meta-)analyses showing that the Cattell-Horn-Carroll (CHC) model outperforms traditional neuropsychological models of cognition, we investigated the relative performance of the traditional neuropsychological model (TNM) of cognitive functioning and the CHC model using the digital Philips IntelliSpace Cognition (ISC) test battery. Method The ISC battery consists of 11 conventional cognitive tests, which were administered to 221 healthy participants. We used confirmatory factor analysis (CFA) to map outcome measures to cognitive domains. Both models contained processing speed, working memory, memory, and visuospatial ability. TNM included executive functioning consisting of TMT B, Stroop interference, and Phonetic and Semantic Fluency. In the CHC model, these outcome measures were distributed over processing speed and a word fluency domain. The models’ fit was compared through Vuong test for non-nested models. Results Both models were a good fit (TNM: χ2(64) = 91.95, p = .013, CFI = .971, RMSEA = .044; CHC: χ2(64) = 83.66, p = .050, CFI = .979, RMSEA = .037). The Vuong test showed that the models could be significantly distinguished based on the observed data, ω2 = 0.21, p < .001. However, the non-nested likelihood ratio test did not offer evidence that either model was a better fit, z = -0.61, p = .727. The 95% confidence interval of the AIC difference contained zero [-18.60, 35.20]. Conclusions Recent findings showing that the CHC model outperforms the TNM are not corroborated by our findings. Instead, both models captured the data equally well, suggesting that the two theoretical frameworks are not necessarily mutually exclusive.

Contains fulltext : 175036.pdf (Publisher’s version ) (Closed access) Recent findings show that typical faces are judged as more trustworthy than atypical faces. However, it is not clear whether employment of typicality cues in trustworthiness judgment happens across cultures and if these cues are culture specific. In two studies, conducted in Japan and Israel, participants judged trustworthiness and attractiveness of faces. In Study 1, faces varied along a cross-cultural dimension ranging from a Japanese to an Israeli typical face. Own-culture typical faces were perceived as more trustworthy than other-culture typical faces, suggesting that people in both cultures employ typicality cues when judging trustworthiness, but that the cues, indicative of typicality, are culture dependent. Because perceivers may be less familiar with other-culture typicality cues, Study 2 tested the extent to which they rely on available facial information other than typicality, when judging other-culture faces. In Study 2, Japanese and Israeli faces varied from either Japanese or Israeli attractive to unattractive with the respective typical face at the midpoint. For own-culture faces, trustworthiness judgments peaked around own-culture typical face. However, when judging other-culture faces, both cultures also employed attractiveness cues, but this effect was more apparent for Japanese participants. Our findings highlight the importance of culture when considering the effect of typicality on trustworthiness judgments. 15 p.

Item does not contain fulltext Previous research argued that stereotypes differ primarily on the 2 dimensions of warmth/communion and competence/agency. We identify an empirical gap in support for this notion. The theoretical model constrains stereotypes a priori to these 2 dimensions; without this constraint, participants might spontaneously employ other relevant dimensions. We fill this gap by complementing the existing theory-driven approaches with a data-driven approach that allows an estimation of the spontaneously employed dimensions of stereotyping. Seven studies (total N = 4,451) show that people organize social groups primarily based on their agency/socioeconomic success (A), and as a second dimension, based on their conservative–progressive beliefs (B). Communion (C) is not found as a dimension by its own, but rather as an emergent quality in the two-dimensional space of A and B, resulting in a 2D ABC model of stereotype content about social groups. 35 p.

The present study tests the hypothesis that behavioral information diagnostic of an out-group's traits biases the expected facial appearance of out-group members toward having facial features corresponding with the inferred traits. Participants formed a stereotype about a novel group based on random exemplar faces, presented alongside descriptions of their behavior. The behavioral information was manipulated to reflect either trustworthy or criminal traits, whereas the stimulus faces did not reflect any traits. Afterwards, participants' expected facial appearance of group members was assessed using a reverse correlation task. Independent judges rated the resulting visualized expectations as more criminal in the criminal behavioral information condition than in the trustworthy behavioral information condition. The current work establishes a causal link between behavioral information and expected out-group faces where previously only correlations had been observed.

Reverse correlation (RC) techniques provide a data-driven approach to model internal representations in an unconstrained way. Here, we used this approach to model social perception of faces. In the RC task, participants repeatedly selected from two face images—created by superimposing randomly generated noise masks on the same face—the face that looked most trustworthy (or, in other conditions: untrustworthy, dominant, or submissive). We calculated classification images (CIs) by averaging all selected images. Trait judgments of independent participants, as well as objective metrics, showed that the CIs visualized the intended traits well. Furthermore, tests of pixel clusters showed that diagnostic information resided mostly in mouth, eye, eyebrow, and hair regions. The current work shows that RC provides an excellent tool to extract psychologically meaningful images that map onto social perception.

How do we model the complexity of social perception? A major methodological problem is that the space of possible variables driving social perceptions is infinitely large, thus posing an insurmountable hurdle for conventional approaches. Here, we describe a set of data-driven methods whose objective is to identify quantitative relationships between high-dimensional variables (e.g., visual images) and behaviors (e.g., perceptual decisions) with as little bias as possible. We focus on social perception of faces, although the methods could be applied to other visual and nonvisual categories. We review two reverse correlation approaches: (a) psychophysical methods based on judgments of images altered with randomly generated noise, where the analysis relates the random variations of the images to judgments; and (b) methods based on judgments of randomly generated faces from a statistical, multidimensional face space model, where the analysis relates the dimensions of the face model to judgments.

The amygdala is thought to perform a number of social functions, and has received much attention for its role in processing social properties of faces. In particular, it has been shown to respond more to facial expressions than to neutral faces, and more to positively valenced and negatively valenced faces than faces in the middle of the continuum. However, when these findings are viewed in the context of a multidimensional face space, an important question emerges. Face space is a vector space where every face can be represented as a point in the space. The origin of the space represents the average face. In this context, positively valenced and negatively valenced faces are further away from the average face than faces in the middle of the continuum. It is therefore unclear if the amygdala response to positively valenced and negatively valenced faces is due to their social properties or to their general distance from the average face. Here, we compared the amygdala response to a set of faces that varied along two dimensions centered around the average face but differing in social content. In both the amygdala and much of the posterior face network, we observed a similar response to both dimensions, with stronger responses to the extremes of the dimensions than to faces near the average face. These findings suggest that the responses in these regions to socially relevant faces may be partially due to general distance from the average face.

Contains fulltext : 99599.pdf (Publisher’s version ) (Open Access) Coordinated action relies on shared representations between interaction partners: people co-represent actions of others in order to respond appropriately. However, little is known about the social factors that influence shared representations. We investigated whether actions performed by in-group and out-group members are represented differently, and if so, what role perspective-taking plays in this process. White participants performed a joint Simon task with an animated image of a hand with either white or black skin tone. Results of study I demonstrated that actions performed by in-group members were co-represented while actions of out-group members were not. In study II, it was found that participants co-represented actions of out-group members when they had read about an out-group member and to take his perspective prior to the actual experiment. Possible explanations for these findings are discussed.

Contains fulltext : 90643.pdf (Publisher’s version ) (Closed access) The amygdala is thought to perform a number of social functions and has received much attention for its role in processing social properties of faces In particular it has been shown to respond more to facial expressions than to neutral faces and more to positively valenced and negatively valenced faces than faces in the middle of the continuum However when these findings are viewed in the context of a multidimensional face space an important question emerges Face space is a vector space where every face can be represented as a point in the space The origin of the space represents the average face In this context positively valenced and negatively valenced faces are further away from the average face than faces in the middle of the continuum It is therefore unclear if the amygdala response to positively valenced and negatively valenced faces is due to their social properties or to their general distance from the average face Here we compared the amygdala response to a set of faces that varied along two dimensions centered around the average face but differing in social content In both the amygdala and much of the posterior face network we observed a similar response to both dimensions with stronger responses to the extremes of the dimensions than to faces near the average face These findings suggest that the responses in these regions to socially relevant faces may be partially due to general distance from the average face.

Background The ability to perceive, recognize and process own and others’ emotions is crucial for efficient and effective social communication. Many different tasks have been used to investigate impairments herein in patients with schizophrenia. Evidence suggests that perception, discrimination and recognition of affective facial expressions are impaired in schizophrenia patients. Importantly, not everyone may interpret the same facial expression similarly. People match their internal representation of specific facial expressions to perceived faces and variation in these internal representations may result in a distortion of social reality. The impairments in face and/or emotion processing and the bias toward a more negative experience may be causally related to degradation of the internal representation itself or to disturbances in the higher-order evaluation of visual input against functionally intact internal representations. In an attempt to develop ways of visualizing an individuals’ internal representation of an emotional face on a computer screen, we set out to visualize the representation of a male and a female face. Methods We use a data-driven technique, i.e. reverse correlation image classification (RCIC), which makes it possible to visualize internal representations of faces on computer screens. Participants judge noisy images of faces that are created by superimposing random noise on a single constant base face. The random noise distorts the base face at the pixel level, generating facial variation across stimuli that is fully unconstrained and unaffected by researchers’ a priori expectations. The participants’ responses to a large number of faces are used to model the facial information that was idiosyncratically diagnostic for the judgments. This analysis yields a classification image (CI) for each participant, which visualizes the facial characteristics that drive judgments of emotional expressions (i.e., their internal representation). We introduce an objective metric, i.e. infoVal, using gender as proof-of-principle. infoVal quantifies the probability that an observed CI was not generated by a random process and is equivalent to a modified z score. First, we test the association between infoVal and more common markers of data quality, i.e. the subjective recognizability, objective discriminability and test-retest reliability of CIs (convergent validity). Second, we use RCIC to investigate and reconstruct the mental representation of trustworthiness as expressed on the face in 32 patients with schizophrenia and 39 controls. Results Subjective ratings showed that male and female CIs were more strongly associated with masculinity and femininity, respectively, when infoVal scores where high (p

Contains fulltext : 90703.pdf (Publisher’s version ) (Closed access) Many research fields concerned with the processing of information contained in human faces would benefit from face stimulus sets in which specific facial characteristics are systematically varied while other important picture characteristics are kept constant. Specifically, a face database in which displayed expressions, gaze direction, and head orientation are parametrically varied in a complete factorial design would be highly useful in many research domains. Furthermore, these stimuli should be standardised in several important, technical aspects. The present article presents the freely available Radboud Faces Database offering such a stimulus set, containing both Caucasian adult and children images. This face database is described both procedurally and in terms of content, and a validation study concerning its most important characteristics is presented. In the validation study, all frontal images were rated with respect to the shown facial expression, intensity of expression, clarity of expression, genuineness of expression, attractiveness, and valence. The results show very high recognition of the intended facial expressions. 12 p.

Contains fulltext : 73137.pdf (Publisher’s version ) (Closed access) Prejudice biases cognition, affect, and behavior toward ethnic out-groups. We propose that prejudice also biases the way people conceptualize the facial appearance of out-group members. Popular belief holds that people's personality traits are reflected in their facial features. We hypothesized that prejudiced people also have more negatively stereotyped mental representations of faces of people in the out-group. To test this hypothesis, we conducted two studies involving the category of Moroccans, a highly stigmatized immigrant group in The Netherlands. The results of the first study suggested that highly prejudiced people have biased mental representations of Moroccan faces. We ran a second study using more trials in the image construction phase, but with an otherwise identical design, to enhance the quality of individual participants' classification images. This allowed us to replicate the findings of the first study on an individual rather than subgroup level. 3 p.

Contains fulltext : 129276.pdf (Publisher’s version ) (Closed access) More than 40 years of research have shown that people favor members of their ingroup in their impressions, attitudes, and behaviors. Here, we propose that people also form different mental images of minimal ingroup and outgroup members, and we test the hypothesis that differences in these mental images contribute to the well-established biases that arise from minimal group categorization. In Study 1, participants were assigned to 1 of 2 groups using a classic minimal group paradigm. Next, a reverse correlation image classification procedure was used to create visual renderings of ingroup and outgroup face representations. Subsequently, a 2nd sample naive to the face generation stage rated these faces on a series of trait dimensions. The results indicated that the ingroup face was significantly more likely than the outgroup face to elicit favorable impressions (e. g., trusting, caring, intelligent, attractive). Extending this finding, Study 2 revealed that ingroup face representations elicited more favorable implicitly measured attitudes than did outgroup representations, and Study 3 showed that ingroup faces were trusted more than outgroup faces during an economic game. Finally, Study 4 demonstrated that facial physiognomy associated with trustworthiness more closely resembled the facial structure of the average ingroup than outgroup face representation. Together, these studies suggest that minimal group distinctions can elicit different mental representations, and that this visual bias is sufficient to elicit ingroup favoritism in impressions, attitudes and behaviors. 15 p.

Contains fulltext : 121156.pdf (Publisher’s version ) (Open Access) Previous research suggests that stereotypes about a group's warmth bias our visual representation of group members. Based on the stereotype content model (SCM) the current research explored whether the second big dimension of social perception, competence, is also reflected in visual stereotypes. To test this, participants created typical faces for groups either high in warmth and low in competence (male nursery teachers) or vice versa (managers) in a reverse correlation image classification task, which allows for the visualization of stereotypes without any a priori assumptions about relevant dimensions. In support of the independent encoding of both SCM dimensions hypotheses-blind raters judged the resulting visualizations of nursery teachers as warmer but less competent than the resulting image for managers, even when statistically controlling for judgments on one dimension. People thus seem to use facial cues indicating both relevant dimensions to make sense of social groups in a parsimonious, non-verbal and spontaneous manner. 8 p.

Item does not contain fulltext People rapidly form impressions from facial appearance, and these impressions affect social decisions. We argue that data-driven, computational models are the best available tools for identifying the source of such impressions. Here we validate seven computational models of social judgments of faces: attractiveness, competence, dominance, extroversion, likability, threat, and trustworthiness. The models manipulate both face shape and reflectance (i.e., cues such as pigmentation and skin smoothness). We show that human judgments track the models’ predictions (Experiment 1) and that the models differentiate between different judgments, though this differentiation is constrained by the similarity of the models (Experiment 2). We also make the validated stimuli available for academic research: seven databases containing 25 identities manipulated in the respective model to take on seven different dimension values, ranging from −3 SD to +3 SD (175 stimuli in each database). Finally, we show how the computational models can be used to control for shared variance of the models. For example, even for highly correlated dimensions (e.g., dominance and threat), we can identify cues specific to each dimension and, consequently, generate faces that vary only on these cues. 15 p.

Item does not contain fulltext People make rapid and consequential social judgments from minimal (non-emotional) facial cues. There has been rapid progress in identifying the perceptual basis of these judgments using data-driven, computational models. In contrast, our understanding of the neural underpinnings of these judgments is rather limited. Meta-analyses of neuroimaging studies find a wide range of seemingly inconsistent responses in the amygdala that co-vary with social judgments from faces. Guided by computational models of social judgments, these responses can be accounted by positing that the amygdala (and posterior face selective regions) tracks face typicality. Atypical faces, whether positively or negatively evaluated, elicit stronger responses in the amygdala. We conclude with the promise of data-driven methods for modeling neural responses to social judgments from faces.

Contains fulltext : 103146.pdf (Publisher’s version ) (Closed access) Individuals perceive their own group to be more typical of a shared superordinate identity than other groups are. This in-group projection process has been demonstrated with both self-report and indirect measures. The two studies reported here extend this research to the visual level, specifically, within the domain of faces. Using an innovative reverse-correlation approach, we found that German and Portuguese participants’ visual representations of European faces resembled the appearance typical for their own national identity. This effect was found even among participants who explicitly denied that one nation was more typical of Europe than the other (Study 1). Moreover, Study 2 provides experimental evidence that in-group projection is restricted to inclusive superordinate groups, as the effect was not observed for visual representations of a category (“Australian”) that did not include participants’ in-group. Implications for the in-group projection model, as well as for the applicability of reversecorrelation paradigms, are discussed. 8 p.

Item does not contain fulltext Previous research has demonstrated that, presumably as a way to protect one’s current romantic relationship, individuals involved in a heterosexual romantic relationship tend to give lower attractiveness ratings to attractive opposite-sex others as compared to uninvolved individuals (i.e., the derogation effect). The present study importantly extends this research by examining whether romantic relationship status actually biases memory for the facial appearance of attractive (vs. unattractive) mates. To address this issue, we used a reverse-correlation technique (Mangini and Biederman, 2004 M.C. Mangini and I. Biederman, Making the ineffable explicit: Estimating the information employed for face classification. Cognitive Science, 28 (2004), pp. 209–226. Mangini & Biederman, 2004), originally developed to get a visual approximation of an individual’s internal representation of a target category or person. In line with the derogation effect, results demonstrated that romantically involved (vs. uninvolved) individuals indeed held a less attractive memory of a previously encountered attractive mate’s face. Interestingly, they also held a more attractive memory of an unattractive mate’s face as compared to uninvolved individuals. This latter finding may suggest that romantically involved (as compared to uninvolved) individuals differentiate opposite-sex others along the attractiveness dimension less.

Contains fulltext : 99894.pdf (Publisher’s version ) (Closed access) Three studies show that social categorization is biased at the level of category allocation. In all studies, participants categorized faces. In Studies 1 and 2, participants overallocated faces with criminal features-a stereotypical negative trait-to the stigmatized Moroccan category, especially if they were prejudiced. On the contrary, the stereotype-irrelevant negative trait stupid did not lead to overallocation to the Moroccan category. In Study 3, using the stigmatized category homosexual, the previously used negative trait criminal-irrelevant to the homosexual stereotype-did not lead to overallocation, but the stereotype-relevant positive trait femininity did. These results demonstrate that normative fit is higher for faces with stereotype-relevant features regardless of valence. Moreover, individual differences in implicit prejudice predicted the extent to which stereotype-relevant traits elicited overallocation: Whereas more negatively prejudiced people showed greater overallocation of faces associated with negative stereotype-relevant traits, they showed less overallocation of faces associated with positive stereotype-relevant traits. These results support our normative fit hypothesis: In general, normative fit is better for faces with stereotypical features. Moreover, normative fit is enhanced for prejudiced individuals when these features are evaluatively congruent. Social categorization thus may be biased in itself. 16 p.