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What are the computational foundations of social grouping? Traditionalapproaches to this question have focused on verbal reasoning or simple(low-dimensional) quantitative models. In the real world, however, socialpreferences emerge when high-dimensional learning systems (brains and bodies)interact with high-dimensional sensory inputs during an animal's embodiedinteractions with the world. A deep understanding of social grouping willtherefore require embodied models that learn directly from sensory inputs usinghigh-dimensional learning mechanisms. To this end, we built artificial neuralnetworks (ANNs), embodied those ANNs in virtual fish bodies, and raised theartificial fish in virtual fish tanks that mimicked the rearing conditions ofreal fish. We then compared the social preferences that emerged in real fishversus artificial fish. We found that when artificial fish had two corelearning mechanisms (reinforcement learning and curiosity-driven learning),artificial fish developed fish-like social preferences. Like real fish, theartificial fish spontaneously learned to prefer members of their own group overmembers of other groups. The artificial fish also spontaneously learned toself-segregate with their in-group, akin to self-segregation behavior seen innature. Our results suggest that social grouping can emerge from threeingredients: (1) reinforcement learning, (2) intrinsic motivation, and (3)early social experiences with in-group members. This approach lays a foundationfor reverse engineering animal-like social behavior with image-computablemodels, bridging the divide between high-dimensional sensory inputs and socialpreferences.

Parallel development of social preferences in fish and machines