Reinterpretation of evolutionary processes as algorithms for Bayesian inference

Jordan W. Suchow, David D. Bourgin, Thomas L. Griffiths, Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference, Trends in Cognitive Sciences, Volume 21, Issue 7, July 2017, Pages 522-530, ISSN 1364-6613, DOI: 10.1016/j.tics.2017.04.005.

Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity.

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