Environment-dependent increase of integrated information in evolving animats

Talk

Natural selection favors the evolution of brains that are able to capture features of the causal structure of the environment relevant for fitness. Here we investigate the evolution of small, adaptive logic-gate networks (‘animats’) in task environments that require the integration of current sensory inputs and memory. We evaluate the evolved networks using measures of information integration that include the number of evolved concepts and the total amount of integrated conceptual information. We find that, over the course of the animats’ adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks with many concepts, thereby increasing their internal complexity.

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