Computational approaches to the neuroscience of social perception

A neurocomputational account of the link between social perception and social action

People selectively help others based on perceptions of their merit or need. Here, we develop a neurocomputational account of how these social perceptions translate into social choice. Using a novel fMRI social perception task, we show that both merit and need perceptions recruited the brain's social inference network. A behavioral computational model identified two non-exclusive mechanisms underlying variance in social perceptions: a consistent tendency to perceive others as meritorious/needy (bias) and a propensity to sample and integrate normative evidence distinguishing high from low merit/need in other people (sensitivity). Variance in people's merit (but not need) bias and sensitivity independently predicted distinct aspects of altruism in a social choice task completed months later. An individual's merit bias predicted context-independent variance in people's overall other-regard during altruistic choice, biasing people towards prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity towards high and low merit recipients by influencing other-regard and self-regard during altruistic decision-making. This context-sensitive perception-action link was associated with activation in the right temporoparietal junction. Together, these findings point towards stable, biologically based individual differences in perceptual processes related to abstract social concepts like merit, and suggest that these differences may have important behavioral implications for an individual's tendency toward favoritism or discrimination in social settings.

Exploring the relationships between first impressions and MMI ratings: a pilot study

The phenomenon of first impression is well researched in social psychology, but less so in the study of OSCEs and the multiple mini interview (MMI). To explore its bearing on the MMI method we included a rating of first impression in the MMI for student selection executed 2012 at the University Medical Center Hamburg-Eppendorf, Germany (196 applicants, 26 pairs of raters) and analyzed how it was related to MMI performance ratings made by (a) the same rater, and (b) a different rater. First impression was assessed immediately after an applicant entered the test room. Each MMI-task took 5 min and was rated subsequently. Internal consistency was α = .71 for first impression and α = .69 for MMI performance. First impression and MMI performance correlated by r = .49. Both measures weakly predicted performance in two OSCEs for communication skills, assessed 18 months later. MMI performance did not increment prediction above the contribution of first impression and vice versa. Prediction was independent of whether or not the rater who rated first impression also rated MMI performance. The correlation between first impression and MMI-performance is in line with the results of corresponding social psychological studies, showing that judgements based on minimal information moderately predict behavioral measures. It is also in accordance with the notion that raters often blend their specific assessment task outlined in MMI-instructions with the self-imposed question of whether a candidate would fit the role of a medical doctor.

Functional organization of social perception in the human brain

Humans rapidly extract diverse and complex information from ongoing social interactions, but the perceptual and neural organization of the different aspects of social perception remains unresolved. We showed short movie clips with rich social content to 97 healthy participants while their haemodynamic brain activity was measured with fMRI. The clips were annotated moment-to-moment for a large set of social features and 45 of the features were evaluated reliably between annotators. Cluster analysis of the social features revealed that 13 dimensions were sufficient for describing the social perceptual space. Three different analysis methods were used to map the social perceptual processes in the human brain. Regression analysis mapped regional neural response profiles for different social dimensions. Multivariate pattern analysis then established the spatial specificity of the responses and intersubject correlation analysis connected social perceptual processing with neural synchronization. The results revealed a gradient in the processing of social information in the brain. Posterior temporal and occipital regions were broadly tuned to most social dimensions and the classifier revealed that these responses showed spatial specificity for social dimensions; in contrast Heschl gyri and parietal areas were also broadly associated with different social signals, yet the spatial patterns of responses did not differentiate social dimensions. Frontal and subcortical regions responded only to a limited number of social dimensions and the spatial response patterns did not differentiate social dimension. Altogether these results highlight the distributed nature of social processing in the brain.

Reflections on Neurofeminism and Intersectionality Using Insights From Psychology

Intersectionality contends that sex/gender is constituted of and with other social categories, and that the social structures giving rise to inequality should be addressed in research. This is a powerful and important perspective from which to investigate the processes and consequences of social group memberships, one which has been overlooked by most neuroscientific research. In particular, neurofeminism, a field of critical neuroscience that challenges neuroscientific assumptions, methods and interpretations of data that reinforce sexism, has ignored intersectionality to date. In contrast, research in the field of psychology has been engaging with intersectionality for more than a decade. In reflecting on how intersectionality has advanced feminist research in psychology, this paper provides a critical analysis of potential novel research avenues for neurofeminism. We identify three main research themes guided by intersectionality. The first theme involves research centered on understanding the socio-structural causes of health inequalities experienced by individuals with intersecting marginalized social identities; the second concerns research addressing the psychological processing of social group memberships that underlies the enactment of systemic discriminatory practices; and the third theme comprises intersectionality research that aims to challenge psychological epistemology. Drawing parallels between the fields of psychology and neuroscience, we explore the potential benefits and risks of advancing an intersectionality-informed neurofeminism.

Computational methods in social neuroscience: recent advances, new tools and future directions

Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized—many of which contain instructive materials (e.g. tutorials and code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments—at the levels of stimuli, paradigms and the webs of social relationships that surround people—with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand and navigate their complex social worlds.