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Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference

delete2017-07-01
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OA
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J
Jordan W. Suchow *
D
David Bourgin
T
Thomas L. Griffiths
DOI:10.1016/j.tics.2017.04.005delete
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摘要

摘要

En 中文
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.
Keyword:
VISUAL WORKING-MEMORY
BLIND VARIATION
MODEL
SELECTION
LANGUAGE
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期刊

Trends in Cognitive Sciences 封面图
Trends in Cognitive Sciences
IF:
17.2
论文数:
3.6K
被引数:
3.5W

机构

University of California System 封面图
University of California System
学者数:
37.7W
论文数: 33.8W
被引数: 6.6K
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