二次学習に基づく心的表象の進化的理論とその検討

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In this research we investigated a fundamental question about the origins of cognition: How did representational cognition evolve? The project consisted of theoretical work (formulation and refinement of the central hypothesis) and computational verification (w.r.t. spatial cognition and social cognition). At the heart of this research project is a hypothesis that representational cognition evolves under selection for 2nd order learning ability (i.e. the ability to "learn to learn"). Applied to the topic of social cognition, this implies that evolution social abilities that qualify as 2nd order learning will produce forms of social cognition that operate by forming representations of other individuals’ minds ("Theory of Mind"). We investigated this idea by letting a simple form of social cognition evolve in a population of AI systems (neural networks).Results of computational work: 1) We showed that in simulation, evolution under selection for 2nd order learning ability leads to a representational form of social cognition, whereas evolution under selection for 1st order learning ability does not. This result supports the hypothesis that representational cognition is a product of evolutionary selection for second order learning ability. 2) In doing so, we showed how representational cognition can be evolved in neural networks. This technique could prove applicable in e.g. social robotics.Results of theoretical work: A theoretical paper detailing the theory (illustrated with our computational work) was completed and published in Minds and Machines journal.