Selection for Reinforcement-Free Learning Ability as an Organizing Factor in the Evolution of Cognition

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

5 citations recorded by the source corpus.

This research explores the relation between environmental structure and neurocognitive structure. We hypothesize that selection pressure on abilities for efficient learning (especially in settings with limited or no reward information) translates into selection pressure on correspondence relations between neurocognitive and environmental structure, since such correspondence allows for simple changes in the environment to be handled with simple learning updates in neurocognitive structure. We present a model in which a simple formof reinforcement-free learning is evolved in neural networks using neuromodulation and analyze the effect this selection for learning ability has on the virtual species' neural organization. We find a higher degree of organization than in a control population evolved without learning ability and discuss the relation between the observed neural structure and the environmental structure. We discuss our findings in the context of the environmental complexity thesis, the Baldwin effect, and other interactions between adaptation processes.

Subfields: Economics and Econometrics, Genetics, Sociology and Political Science

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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).”
Evolution of Social Representation in Neural Networks (2013, methodology): “Agents are implemented as neural networks (NNs).”