Three case studies in the Bayesian analysis of cognitive models
Three case studies in the Bayesian analysis of cognitive models
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DOI：
10.3758/pbr.15.1.1
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发表时间：
2008-02-01
影响因子：
3.5
通讯作者：
Lee, Michael D.
中科院分区：
文献类型：
--
作者：
Lee, Michael D.
贝叶斯统计推断提供了一种将心理模型与数据相关联的原则性和综合性方法。本文介绍了贝叶斯分析的三个有影响力的心理模型：刺激表征的多维尺度模型，类别学习的广义上下文模型，和信号检测理论模型的决策。在每种情况下，该模型被重铸为概率图形模型，并根据先前考虑的数据集进行评估。在每一种情况下，它表明，贝叶斯推理是能够提供答案的重要理论和经验问题容易和连贯性。贝叶斯方法的一般性和它的潜力，在心理学模型和数据的理解进行了讨论。
Bayesian statistical inference offers a principled and comprehensive approach for relating psychological models to data. This article presents Bayesian analyses of three influential psychological models: multidimensional scaling models of stimulus representation, the generalized context model of category learning, and a signal detection theory model of decision making. In each case, the model is recast as a probabilistic graphical model and is evaluated in relation to a previously considered data set. In each case, it is shown that Bayesian inference is able to provide answers to important theoretical and empirical questions easily and coherently. The generality of the Bayesian approach and its potential for the understanding of models and data in psychology are discussed.