How Robust Are Probabilistic Models of Higher-Level Cognition?
How Robust Are Probabilistic Models of Higher-Level Cognition?
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DOI：
10.1177/0956797613495418
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发表时间：
2013-12-01
影响因子：
8.2
通讯作者：
Davis, Ernest
中科院分区：
文献类型：
--
作者：
Marcus, Gary F.;Davis, Ernest
一种越来越流行的理论认为，在词汇学习、语用学、朴素物理学和对未来的预测等不同领域，大脑应该被视为概率推理的一个接近最优或理性的引擎。我们认为，这种观点，往往与贝叶斯模型的推断，是明显不如人们普遍认为的有前途，并破坏了事后的做法，值得批发重新评估。我们还表明，概率和理性或最优之间的共同方程是不合理的。
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.