Dataset Information

A Neural Mechanism of Social Categorization.

ABSTRACT: Humans readily sort one another into multiple social categories from mere facial features. However, the facial features used to do so are not always clear-cut because they can be associated with opponent categories (e.g., feminine male face). Recently, computational models and behavioral studies have provided indirect evidence that categorizing such faces is accomplished through dynamic competition between parallel, coactivated social categories that resolve into a stable categorical percept. Using a novel paradigm combining fMRI with real-time hand tracking, the present study examined how the brain translates diverse social cues into categorical percepts. Participants (male and female) categorized faces varying in gender and racial typicality. When categorizing atypical faces, participants' hand movements were simultaneously attracted toward the unselected category response, indexing the degree to which such faces activated the opposite category in parallel. Multivoxel pattern analyses (MVPAs) provided evidence that such social category coactivation manifested in neural patterns of the right fusiform cortex. The extent to which the hand was simultaneously attracted to the opposite gender or race category response option corresponded to increased neural pattern similarity with the average pattern associated with that category, which in turn associated with stronger engagement of the dorsal anterior cingulate cortex. The findings point to a model of social categorization in which occasionally conflicting facial features are resolved through competition between coactivated ventral-temporal cortical representations with the assistance of conflict-monitoring regions. More broadly, the results offer a promising multimodal paradigm to investigate the neural basis of "hidden", temporarily active representations in the service of a broad range of cognitive processes.SIGNIFICANCE STATEMENT Individuals readily sort one another into social categories (e.g., sex, race), which have important consequences for a variety of interpersonal behaviors. However, individuals routinely encounter faces that contain diverse features associated with multiple categories (e.g., feminine male face). Using a novel paradigm combining neuroimaging with hand tracking, the present research sought to address how the brain comes to arrive at stable social categorizations from multiple social cues. The results provide evidence that opponent social categories coactivate in face-processing regions, which compete and may resolve into an eventual stable categorization with the assistance of conflict-monitoring regions. Therefore, the findings provide a neural mechanism through which the brain may translate inherently diverse social cues into coherent categorizations of other people.

SUBMITTER: Stolier RM

PROVIDER: S-EPMC6596469 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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A Neural Mechanism of Social Categorization.

Stolier Ryan M RM Freeman Jonathan B JB

The Journal of neuroscience : the official journal of the Society for Neuroscience 20170508 23

Humans readily sort one another into multiple social categories from mere facial features. However, the facial features used to do so are not always clear-cut because they can be associated with opponent categories (e.g., feminine male face). Recently, computational models and behavioral studies have provided indirect evidence that categorizing such faces is accomplished through dynamic competition between parallel, coactivated social categories that resolve into a stable categorical percept. Us ...[more]

PMID: 28483974

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Project description:Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare brain responses to both a behavioral model and a deep neural network and test if they give similar results.

Dataset Information

A Neural Mechanism of Social Categorization.

Publications

A Neural Mechanism of Social Categorization.

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