Evolution of Social Representation in Neural Networks

By Solvi Fylgja Arnold, Reiji Suzuki, Takaya Arita. 2013

5 citations recorded by the source corpus.

This paper describes an Artificial Life approach to Theory of Mind (ToM), the ability to employ mental representations of other minds in order to understand or anticipate the behaviour of others. We designed a model in which a population of neural network (NN) agents evolve the ability to predict, on basis of observation of past behaviour, others' future behaviour in novel circumstances. As agent behaviour is guided by private mental states, invisible to the predicting agent, this task forces agents to go beyond imitation and repetition of fit responses, requiring them to gain some degree of insight into the partner agent's internal configuration by observation of their externally visible behaviour. As such, this learning ability cannot be captured with conventional learning algorithms based on rewards or examples. We find that NNs equipped with neuromodulation mechanisms can be evolved to perform favourably on this task. The resulting networks are seen to behave as though they have a primitive form of first order ToM.

Subfields: Cultural Studies, Social Psychology, Sociology and Political Science

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In Social Psychology

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “The evolution of social representations in neural networks was shown to be facilitated by neuromodulatory dynamics in Arnold et al. (2013a).”