Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of “regime switching,” with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate.

中文翻译：

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反复决策的时间演变心理过程。

许多心理实验让受试者重复一项任务，以获得测试心理表现定量理论所需的统计精度。在这样的实验中，任务时间会对表现产生相当大的影响，从而改变正在研究的心理过程。大多数研究要么忽略了这些变化，将基本过程视为静态，要么为了统计简单而牺牲了模型的一些心理内容。我们使用粒子马尔可夫链蒙特卡罗方法来研究模型参数在心理上似是而非的时变变化。使用来自三个被高度引用的实验的数据，我们发现强有力的证据支持隐马尔可夫转换过程作为对时变效应的解释。这体现了“政权转换，”，受试者在代表不同决策模式的不同认知状态之间交替。转换模型解释了数据中关键的长期和短期动态影响。除了我们研究的模型和数据集之外，我们方法的中心思想可以非常普遍地应用于定量心理学理论。