Parallel development of social preferences in fish and machines

TL;DR

This study demonstrates that artificial neural networks embedded in virtual fish can develop social preferences similar to real fish, highlighting the roles of reinforcement learning, curiosity, and early social experiences in social grouping behaviors.

Contribution

The paper introduces embodied neural network models that learn social preferences from high-dimensional sensory inputs, bridging the gap between biological and artificial social behavior development.

Findings

Artificial fish learned to prefer their own group over others.

Artificial fish spontaneously self-segregated with their group.

Social preferences emerged from reinforcement learning, curiosity, and early social experiences.

Abstract

What are the computational foundations of social grouping? Traditional approaches to this question have focused on verbal reasoning or simple (low-dimensional) quantitative models. In the real world, however, social preferences emerge when high-dimensional learning systems (brains and bodies) interact with high-dimensional sensory inputs during an animal's embodied interactions with the world. A deep understanding of social grouping will therefore require embodied models that learn directly from sensory inputs using high-dimensional learning mechanisms. To this end, we built artificial neural networks (ANNs), embodied those ANNs in virtual fish bodies, and raised the artificial fish in virtual fish tanks that mimicked the rearing conditions of real fish. We then compared the social preferences that emerged in real fish versus artificial fish. We found that when artificial fish had two core learning mechanisms (reinforcement learning and curiosity-driven learning), artificial fish developed fish-like social preferences. Like real fish, the artificial fish spontaneously learned to prefer members of their own group over members of other groups. The artificial fish also spontaneously learned to self-segregate with their in-group, akin to self-segregation behavior seen in nature. Our results suggest that social grouping can emerge from three ingredients: (1) reinforcement learning, (2) intrinsic motivation, and (3) early social experiences with in-group members. This approach lays a foundation for reverse engineering animal-like social behavior with image-computable models, bridging the divide between high-dimensional sensory inputs and social preferences.