TY - JOUR
T1 - Evolution in Mind
T2 - Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference
AU - Suchow, Jordan W.
AU - Bourgin, David D.
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
KW - Bayesian inference
KW - cognitive processes
KW - creativity
KW - evolution
KW - learning
KW - memory
UR - http://www.scopus.com/inward/record.url?scp=85019602781&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019602781&partnerID=8YFLogxK
U2 - 10.1016/j.tics.2017.04.005
DO - 10.1016/j.tics.2017.04.005
M3 - Review article
C2 - 28551106
AN - SCOPUS:85019602781
SN - 1364-6613
VL - 21
SP - 522
EP - 530
JO - Trends in Cognitive Sciences
JF - Trends in Cognitive Sciences
IS - 7
ER -