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The Neural Computations Underlying Human Social Interaction Recognition

Friday, December 1, 2023


The Neural Computations Underlying Human Social Interaction Recognition

Dr. Leyla Isik

Friday, Dec. 1, 2023 at 10 a.m. CT

Zoom link or copy and paste link here: https://auburn.zoom.us/j/81706339239

 

Abstract:

Humans perceive the world in rich social detail. We effortlessly recognize not only objects and people in our environment, but also social interactions between people. The ability to perceive and understand social interactions is critical for functioning in our social world. We recently identified a brain region that selectively represents others’ social interactions in the posterior superior temporal sulcus (pSTS) in a manner that is distinct from other visual and social processes, like face recognition and theory of mind. However, it is unclear how social interactions are processed in the real world where they co-vary with many other sensory and social features. In the first part of my talk, I will discuss new work using naturalistic fMRI paradigms and machine learning analyses to understand how humans process social interactions in real-world settings. We find that features of a social interaction are extracted hierarchically along the STS, with strong selectivity for communicative interactions, even after controlling for the effects of other co-varying perceptual and social information. In the second part of my talk, I will discuss the computational implications of social interaction selectivity in the brain, and present a novel graph neural network model, SocialGNN, that instantiates these insights. SocialGNN reproduces human social interaction judgements in both controlled and natural videos using only visual information, without any explicit model of agents’ minds or the physical world, but requires relational, graph structure and processing to do so. Together, this work suggests that social interaction recognition is a core human ability that relies on specialized, structured visual representations.

 

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