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.

翻译：社会群体行为的计算基础是什么？传统研究方法侧重于口头推理或简单（低维）定量模型。然而在真实世界中，当高维学习系统（大脑与身体）在动物的具身交互过程中与高维感官输入相互作用时，社会偏好才得以涌现。因此，要深入理解社会群体行为，需要采用直接通过高维学习机制从感官输入中学习的具身模型。为此，我们构建了人工神经网络（ANNs），将这些网络嵌入虚拟鱼体中，并在模拟真实鱼类饲养环境的虚拟鱼缸中培育人工鱼，进而比较真实鱼类与人工鱼涌现的社会偏好。研究发现，当人工鱼具备两种核心学习机制（强化学习与好奇心驱动学习）时，它们会发展出类似鱼类社会偏好。与真实鱼类相同，人工鱼自发学会偏好同群成员而非异群成员，并自发形成群内分离行为——这与自然界中观察到的自我隔离现象相似。我们的研究结果表明，社会群体行为可由三种要素衍生：（1）强化学习，（2）内在动机，（3）早期与群内成员的社会互动经验。该方法通过图像可计算模型为逆向工程构建类动物社会行为奠定基础，弥合了高维感官输入与社会偏好之间的鸿沟。