A Bayesian hierarchical mixture approach to individual differences: Case studies in selective attention and representation in category learning
A Bayesian hierarchical mixture approach to individual differences: Case studies in selective attention and representation in category learning
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DOI：
10.1016/j.jmp.2013.12.002
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发表时间：
2014-04-01
影响因子：
1.8
通讯作者：
Vanpaemel, Wolf
中科院分区：
文献类型：
--
作者：
Bartlema, Annelies;Lee, Michael;Vanpaemel, Wolf
我们展示了使用贝叶斯分层混合方法来模拟个体认知差异的潜力。混合成分可以用来识别使用不同认知过程的受试者的潜在群体，而分层分布可以用来捕捉每个群体中更多的微小差异。我们将贝叶斯分层混合方法应用于两个涉及类别学习的说明性应用中。一种侧重于通常被认为是参数估计问题的问题，而另一种侧重于传统上从模型选择的角度来解决的问题。使用以前发表的和新收集的数据，我们展示了分层混合方法在模拟个体差异方面的灵活性和广泛的适用性。(C)2013 Elsevier Inc.保留所有权利。
We demonstrate the potential of using a Bayesian hierarchical mixture approach to model individual differences in cognition. Mixture components can be used to identify latent groups of subjects who use different cognitive processes, while hierarchical distributions can be used to capture more minor variation within each group. We apply Bayesian hierarchical mixture methods in two illustrative applications involving category learning. One focuses on a problem that is typically conceived of as a problem of parameter estimation, while the other focuses on a problem that is traditionally tackled from a model selection perspective. Using both previously published and newly collected data, we demonstrate the flexibility and wide applicability of the hierarchical mixture approach to modeling individual differences. (C) 2013 Elsevier Inc. All rights reserved.