How Robust Are Probabilistic Models of Higher-Level Cognition?

被引：86

作者：

Marcus, Gary F. ^{[1
]}

Davis, Ernest ^{[1
]}

机构：

[1] NYU, New York, NY 10003 USA

来源：

PSYCHOLOGICAL SCIENCE | 2013年 / 24卷 / 12期

关键词：

cognition(s); Bayesian models; optimality; BAYESIAN-INFERENCE; CHILDRENS USE; PSYCHOLOGY; STATISTICS; INFANTS; RATIONALITY; INFORMATION; SIMILARITY; PREDICTION; JUDGMENT;

D O I：

10.1177/0956797613495418

中图分类号：

B84 [心理学];

学科分类号：

04 ; 0402 ;

摘要：

An increasingly popular theory holds that the mind should be viewed as a near-optimal or rational engine of probabilistic inference, in domains as diverse as word learning, pragmatics, naive physics, and predictions of the future. We argue that this view, often identified with Bayesian models of inference, is markedly less promising than widely believed, and is undermined by post hoc practices that merit wholesale reevaluation. We also show that the common equation between probabilistic and rational or optimal is not justified.

引用

页码：2351 / 2360

页数：10

共 46 条

[1]

American Academy of Poets, 1997, LIF LIN

[2] REFLECTIONS OF THE ENVIRONMENT IN MEMORY [J].