Model generalization and parameter consistency for cognitive models of decision m

决策认知模型的模型泛化和参数一致性

• 批准号: 8021916
• 负责人: JEROME R BUSEMEYER
• 金额: $ 15.4万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8021916
• 关键词: Bayesian Method; Brain Injuries; Characteristics; Chi-Square Tests; Choice Behavior; Clinical; Cognitive; Complex; Data; Data Set; Decision Making; Decision Modeling; Drug abuse; Exhibits; Gambling; Individual; Individual Differences; Iowa; Laboratories; Learning; Measures; Methodology; Methods; Modeling; Performance; Pharmaceutical Preparations; Population; Procedures; Process; Reaction Time; Risk; Sorting - Cell Movement; Source; Testing; Wisconsin; Work; base; discounting; drug abuser; improved; success

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. PUBLIC HEALTH RELEVANCE: hisrsarchinvstiats th undrly in causesfor decision main dficits in brai daaed,drug abuse, and psychopathological populations his information will eventually b usdtod sign and prdicttratetutcmes o theseciica sychoopgroyblms.

描述（由申请人提供）：本提案的目的是扩展我们过去的工作，比较脑损伤，药物滥用或精神病理个体与非滥用或正常个体在标准实验室决策任务中的表现。在这些任务上的表现是三个不同的基本组成部分的相互作用和综合，包括动机、学习和选择过程。这些复杂决策任务的认知模型用于将性能分解为这三个部分。然后使用与这些组成部分相关的参数来了解这些临床人群表现出的决策缺陷的来源。过去工作的两个关键假设是模型泛化和参数一致性的假设。如果可以将模型的参数拟合到个人的一个任务上，那么模型就可以泛化，然后使用这些相同的参数来预测同一个人在其他密切相关的任务上的表现。如果从一个个体的任务中估计的参数与从另一个密切相关的任务中估计的参数相关联，则参数是一致的。如果我们想把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无关紧要的特征，那么这些假设是至关重要的。到目前为止，我们在模型泛化和参数一致性方面取得了初步的成功。但至少有两个原因限制了成功：一是需要通过模型比较找到更好的模型，二是需要更好的估计模型参数的方法。我们计划使用新的层次贝叶斯分析来改进我们的方法。这种新方法允许人们为个体差异建立模型，而不是单独拟合个体。这样，通过一个由所有个体的数据提供信息的模型来估计单个个体的参数。这为模型比较提供了更稳定的参数估计和更有力的方法。我们还计划扩展层次贝叶斯方法来比较模型泛化。