Model generalization and parameter consistency for cognitive models of decision m

描述(由申请人提供)：这项建议的目的是扩展我们过去的工作，比较大脑受损、滥用药物或精神病理个体的表现，以及在标准实验室决策任务中与非滥用或正常个体的表现。在这些任务上的表现是三个不同的基本组成部分的交互和综合，包括动机、学习和选择过程。这些复杂决策任务的认知模型被用来将绩效分解为这三个组成部分。与这些成分相关的参数随后被用来理解这些临床人群表现出的决策缺陷的来源。支持这项过去工作的两个关键假设是模型泛化和参数一致性假设。如果一个人可以将模型的参数适合于一个人的一项任务，然后使用这些相同的参数来预测同一人在其他密切相关任务中的表现，那么模型就是泛化的。如果从一项任务为个人估计的参数与从另一项密切相关的任务为同一个人估计的参数相关，则参数是一致的。如果我们想要将这些参数解释为测量个人的稳定特征，而不是实验室任务的一些不必要的特征，这些假设是至关重要的。到目前为止，我们取得了一些初步的成功，取得了模型泛化和参数一致性。但成功受到限制，至少有两个原因：一是需要通过模型比较找到更好的模型，二是需要更好的方法来估计模型参数。我们计划使用新的分层贝叶斯分析来改进我们的方法。这种新的方法允许人们建立一个针对个体差异的模型，而不是单独对个体进行匹配。这样，通过一个由所有个人的数据提供信息的模型来估计单个个人的参数。这为模型比较提供了更稳定的参数估计和更强大的方法。我们还计划扩展用于比较模型泛化的分层贝叶斯方法。

DESCRIPTION (provided by applicant): The purpose of this proposal is to extend our past work comparing performance of brain damaged, drug abusing, or psychopathological individuals and with non abusing or normal individuals on standard laboratory decision making tasks. Performance on these tasks is an interaction and synthesis of three different underlying components, including motivational, learning, and choice processes. Cognitive models of these complex decision tasks are used to break performance down into these three components. The parameters associated with these components are then used to understand the source of the decision making deficits exhibited by these clinical populations. Two critical assumptions underlying this past work are the assumptions of model generalization and parameter consistency. A model generalizes if one can fit the parameters of the model to one task for an individual, and then use these same parameters to predict performance on other closely related tasks for the same individual. Parameters are consistent if the parameters estimated from one task for an individual correlate with the parameters estimated from another closely related task for the same individual. These assumptions are crucial if we want to interpret the parameters as measuring stable characteristics of an individual, rather than some inessential characteristics of a laboratory task. So far, we achieved some initial success obtaining model generalization and parameter consistency. But success has been limited for at least two reasons: one is the need to find better models through model comparison, and the other is the need for better methods of estimating model parameters. We plan to improve our methods using new hierarchical Bayesian analyses. This new methodology allows one to build a model for individual differences rather than fitting individuals separately. This way the parameters for a single individual are estimated through a model which is informed by data from all individuals. This provides more stable parameter estimates and more powerful methods for model comparison. We also plan to extend the hierarchical Bayesian method for comparing model generalization.