Comparison of Decision Learning Models Using the Generalization Criterion Method
Comparison of Decision Learning Models Using the Generalization Criterion Method
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DOI：
10.1080/03640210802352992
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发表时间：
2008-01-01
影响因子：
2.5
通讯作者：
Stout, Julie C.
中科院分区：
文献类型：
--
作者：
Ahn, Woo-Young;Busemeyer, Jerome R.;Stout, Julie C.
对新条件做出准确的先验预测是一个好的模型的标志（Busemeyer Wang, 2000）。本研究比较了8种决策学习模型的通用性。参与者执行2个任务（爱荷华州赌博任务和东吴赌博任务），每个模型通过估计每个参与者在一个任务中的参数并使用相同的参数预测另一个任务来进行先验预测。在个体分析水平上，采用三种方法对模型进行评价。第一种方法使用事后拟合标准，第二种方法使用短期预测的一般化标准，第三种方法再次使用长期预测的一般化标准。结果表明，具有前景效用函数的模型可以对新条件进行泛化预测，而对简单的赌博任务进行短期预测和长期预测需要不同的学习模型。
It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.