Title: Data-driven Analysis of the Dynamics of Information-acquisition Over time during Social judgement
Funder: Economic and Social Research Council

Abstract: At the heart of human society, social interactions shape and maintain the complex network of relationships between individuals and groups. Yet, whereas some social exchanges foster healthy relations with considerable benefits for society, some actively jeopardize the social connections on which harmonious societies are based. A powerful driver of such social degeneration is prejudice, reflected by the stereotypes harbored by individuals. Specifically, stereotypes bias thought and action via top-down processes, including imposing unsubstantiated inferences about competence [4-6], trustworthiness [12, 13] and criminality [34] based on the recognition of out-group identifiers. However, little is known about how stereotypes and prejudice exert a top-down control to shape the dynamics of information extraction and integration, thereby influencing critical social judgments. To address this, we propose a state-of-the-art data-driven and modeling analysis of the dynamics of information acquisition over the time course of social judgments. Our proposal comprises a main theoretical hypothesis, studied under three complementary strands, each of which constitutes one main research objective. Theoretical hypothesis. Complex social judgments (e.g. of facial attraction, dominance and trustworthiness) depend on the conjunction of visual information acquisition and the influence of top-down factors (e.g. prejudice, stereotypes and context) on information processing. We will document the role of top-down factors on the extraction dynamics and use of information for social judgments. Strand 1. We will combine eye movements and state-of-the-art reverse correlation methods to understand how top-down social categories (prejudice and stereotypes) modulate the sequences of eye fixations on static faces to extract social information for social judgments. Strand 2. Real-world social interactions typically happen with dynamic faces. Extending Stand 1, we will investigate social information extraction from photo-realistic, three-dimensional dynamic faces. Strand 3. We will integrate the data-driven analyses of Strands 1-3 in a Bayesian model of information acquisition via eye movements that merges the top-down factors of social priors (prejudice and stereotyping) with the bottom-up preferences for facial features. We also plan to adapt this model to the development of communicative dynamic avatars. Benefits. Academia. The Social Sciences and Neuroscience, Information and Communication Technology (ICT). Determining precisely the integration of information causing social judgments of attractiveness, trustworthiness and dominance is centrally important for social science. Normative models of social signals derived from our research will also inform how populations with social deficits fail (e.g. Autism Spectrum Disorder and Asperger Syndrome). Industrial stakeholders. Gaming industry, robotics, social networking industry, including the exploding field of social computing, with higher-level parameterisation of face and better understanding of how the face communicates important social dimensions. The gaming and 3D-movie industries rely on detailed understandings of social signals. Their automated recognition ranges from homeland security to remote communication via avatars and companion robots, with automated expression applying primarily to the latter two. So far, the 3D-movie/animation industries use point-light displays to capture social signals. Yet, our 4D platform provides models with a far greater power of generalisation. User community. Intuitive, socially intelligent interfaces for mediated communication. Older population, companion robots. Wider impact of social networking on society. Ultimately, the user community will benefit from ecologically improved (i.e., more human), more intelligent communication interfaces for mediated communication based on socially-relevant traits.


Research activity codes: 1.2 PSYCHOLOGICAL AND SOCIOECONOMIC PROCESSES
Health categories: MENTAL HEALTH