  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.
