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 data sets that we investigate.
翻译:许多心理实验的课题是重复一项任务,以获得测试心理表现定量理论所需的统计精确度。在这样的实验中,时间在任务上可以对表现产生巨大的影响,改变正在调查的心理过程。大多数研究要么忽视这些变化,将基本过程视为静态,或者牺牲统计简单化模型的某些心理内容。我们使用粒子Markov链Monte-Carlo的方法研究模型参数在心理上可信的时间变化。我们从三个高调实验中找到有力的证据,以隐蔽的Markov转换过程作为时间变化效应的解释。这体现了“制度转换”的心理假设,主体在不同的认知状态之间交替,代表不同的决策模式。转换模型解释了数据中的关键长期和短期动态效应。我们方法的中心思想可以非常普遍地应用于定量的心理理论,超出我们所调查的模型和数据集。