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DOI: http://dx.doi.org/10.7551/978-0-262-31709-2-ch061
Pages 425-430
First published 2 September 2013

Evolution of Social Representation in Neural Networks

Solvi F. Arnold, Reiji Suzuki, Takaya Arita

Abstract

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.