Social contingency in embodied neural networks relies on co-constructed dynamical mechanisms.

What are the mechanisms that enable organisms to detect and respond to the actions of others? Social contingency, or the degree to which one's actions reliably elicit timely and relevant responses from another, underlies adaptive behaviour and social interaction across species. In order to investigate general principles underlying this phenomenon, we trained and analysed populations of embodied recurrent neural networks engaged in the perceptual crossing task, a minimal social interaction experiment in humans. Through extensive robustness and performance testing, we isolated a subset of 111 circuits. Analysis revealed several shared principles among the robust subset. First, despite uniform performance, we found four distinct behavioural strategies that agents would switch to depending on state history and the strategy of their partner. Next, we found that social contingency does not depend on a single feature of feedback but rather on a scaled relationship between feedback parameters. Finally, using dynamical systems analysis, we identified a shared mechanism for social contingency across all successful circuits. Specifically, it was necessary for the nervous system to couple a contingency cue, a specific temporal pattern in the sensor's activation that distinguishes social from non-social interactions, with a method of conditional stability, a way of structuring the nervous system such that interactions are stable only if the appropriate temporal cue is present. This article is part of the theme issue 'Mechanisms of learning from social interaction'.

Duke Scholars

Author Sasha Winkler