Cheng Chung-Ping, Sheu Ching-Fan, Yen Nai-Shing
Department of Psychology, National Chengchi University, Taipei, Taiwan.
Behav Res Methods. 2009 Aug;41(3):657-63. doi: 10.3758/BRM.41.3.657.
The Iowa gambling task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994) was developed to simulate real-life decision making under uncertainty. The task has been widely used to examine possible neurocognitive deficits in normal and clinical populations. Busemeyer and Stout (2002) proposed the expectancy-valence (EV) model to explicitly account for individual participants' repeated choices in the IGT. Parameters of the EV model presumably measure different psychological processes that underlie performance on the task, and their values may be used to differentiate individuals across different populations. In the present article, the EV model is extended to include both fixed effects and subject-specific random effects. The mixed-effects EV model fits the nested structure of observations in the IGT naturally and provides a unified procedure for parameter estimation and comparisons among groups of populations. We illustrate the utility of the mixed-effects approach with an analysis of gender differences using a real data set. A simulation study was conducted to verify the advantages of this approach.
爱荷华赌博任务（IGT；贝沙拉、达马西奥、达马西奥和安德森，1994年）旨在模拟现实生活中在不确定性下的决策。该任务已被广泛用于检查正常人群和临床人群中可能存在的神经认知缺陷。布塞迈耶和斯托特（2002年）提出了期望-效价（EV）模型，以明确解释个体参与者在IGT中的重复选择。EV模型的参数大概测量了任务表现背后的不同心理过程，其值可用于区分不同人群中的个体。在本文中，EV模型被扩展为包括固定效应和特定于个体的随机效应。混合效应EV模型自然地拟合了IGT中观测值的嵌套结构，并为参数估计和不同人群组之间的比较提供了一个统一的程序。我们通过使用真实数据集分析性别差异来说明混合效应方法的实用性。进行了一项模拟研究以验证该方法的优势。
