Boehm Udo, van Maanen Leendert, Evans Nathan J, Brown Scott D, Wagenmakers Eric-Jan
Department of Experimental Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Department of Psychology, University of Amsterdam, 1018 XA, Amsterdam, The Netherlands.
Atten Percept Psychophys. 2020 Jun;82(3):1520-1534. doi: 10.3758/s13414-019-01806-4.
A standard assumption of most sequential sampling models is that decision-makers rely on a decision criterion that remains constant throughout the decision process. However, several authors have recently suggested that, in order to maximize reward rates in dynamic environments, decision-makers need to rely on a decision criterion that changes over the course of the decision process. We used dynamic programming and simulation methods to quantify the reward rates obtained by constant and dynamic decision criteria in different environments. We further investigated what influence a decision-maker's uncertainty about the stochastic structure of the environment has on reward rates. Our results show that in most dynamic environments, both types of decision criteria yield similar reward rates, across different levels of uncertainty. This suggests that a static decision criterion might provide a robust default setting.
大多数序贯抽样模型的一个标准假设是，决策者依赖于在整个决策过程中保持不变的决策标准。然而，最近有几位作者提出，为了在动态环境中最大化奖励率，决策者需要依赖于在决策过程中不断变化的决策标准。我们使用动态规划和模拟方法来量化在不同环境中恒定和动态决策标准所获得的奖励率。我们进一步研究了决策者对环境随机结构的不确定性对奖励率有何影响。我们的结果表明，在大多数动态环境中，在不同的不确定性水平下，这两种决策标准产生的奖励率相似。这表明静态决策标准可能提供一个稳健的默认设置。
