An improved cognitive model of the Iowa and Soochow Gambling Tasks with regard to model fitting performance and tests of parameter consistency
An improved cognitive model of the Iowa and Soochow Gambling Tasks with regard to model fitting performance and tests of parameter consistency
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DOI：
10.3389/fpg.2015.00229
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发表时间：
2015-03-12
影响因子：
3.8
通讯作者：
Stout, Julie C.
中科院分区：
文献类型：
--
作者：
Dai, Junyi;Kerestes, Rebecca;Stout, Julie C.
爱荷华州赌博任务（IGT）和苏州赌博任务（SGT）是两个基于经验的风险决策任务，用于检查临床人群的决策缺陷。一些认知模型，包括期望-效价学习（EVL）模型和前景效价学习（PVL）模型，已经被开发出来，以解开动机，认知和反应过程的显式选择在这些任务。本研究的目的是开发一种改进的模型，可以更好地拟合经验数据比EVL和PVL模型，此外，产生更一致的参数估计在IGT和SGT.26阿片类药物使用者（平均年龄34.23; SD 8.79）和27名对照参与者（平均年龄35; SD 10.44）完成这两项任务。18个认知模型的评价，更新和选择规则不同，适合个人数据和他们的表现进行了比较，以找到一个最佳的拟合模型的统计基线模型。结果表明，将损益分开处理的前景效用函数、衰减-强化更新规则和试验-独立选择规则相结合的模型在两个任务中均表现最好。此外，获胜的模型在两个任务中产生的单个参数估计比其他任何模型都更一致。
The Iowa Gambling Task (IGT) and the Soochow Gambling Task (SGT) are two experience based risky decision making tasks for examining decision making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning (EVL) model and the prospect valence learning (PVL) model, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79) and 27 control participants (mean age 35; SD 10.44) completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models.