Malhotra Gaurav, Leslie David S, Ludwig Casimir J H, Bogacz Rafal
School of Experimental Psychology, University of Bristol.
Department of Mathematics and Statistics, Lancaster University.
J Exp Psychol Gen. 2017 Jun;146(6):776-805. doi: 10.1037/xge0000286. Epub 2017 Apr 13.
The dominant theoretical framework for decision making asserts that people make decisions by integrating noisy evidence to a threshold. It has recently been shown that in many ecologically realistic situations, decreasing the decision boundary maximizes the reward available from decisions. However, empirical support for decreasing boundaries in humans is scant. To investigate this problem, we used an ideal observer model to identify the conditions under which participants should change their decision boundaries with time to maximize reward rate. We conducted 6 expanded-judgment experiments that precisely matched the assumptions of this theoretical model. In this paradigm, participants could sample noisy, binary evidence presented sequentially. Blocks of trials were fixed in duration, and each trial was an independent reward opportunity. Participants therefore had to trade off speed (getting as many rewards as possible) against accuracy (sampling more evidence). Having access to the actual evidence samples experienced by participants enabled us to infer the slope of the decision boundary. We found that participants indeed modulated the slope of the decision boundary in the direction predicted by the ideal observer model, although we also observed systematic deviations from optimality. Participants using suboptimal boundaries do so in a robust manner, so that any error in their boundary setting is relatively inexpensive. The use of a normative model provides insight into what variable(s) human decision makers are trying to optimize. Furthermore, this normative model allowed us to choose diagnostic experiments and in doing so we present clear evidence for time-varying boundaries. (PsycINFO Database Record
决策的主导理论框架认为，人们通过将有噪声的证据整合到一个阈值来做出决策。最近有研究表明，在许多生态现实情境中，降低决策边界能使决策可获得的奖励最大化。然而，关于人类降低边界的实证支持却很少。为了研究这个问题，我们使用了一个理想观察者模型来确定参与者应该在哪些条件下随时间改变他们的决策边界以最大化奖励率。我们进行了6个扩展判断实验，这些实验精确匹配了这个理论模型的假设。在这个范式中，参与者可以依次对呈现的有噪声的二元证据进行采样。试验块的持续时间是固定的，并且每个试验都是一个独立的奖励机会。因此，参与者必须在速度（在速度（尽可能多地获得奖励）和准确性（采样更多证据）之间进行权衡。能够获取参与者实际经历的证据样本使我们能够推断出决策边界的斜率。我们发现，参与者确实按照理想观察者模型预测的方向调整了决策边界的斜率，尽管我们也观察到了与最优性的系统性偏差。使用次优边界的参与者以一种稳健的方式这样做，以至于他们边界设置中的任何错误成本相对较低。使用规范模型可以深入了解人类决策者试图优化的变量。此外，这个规范模型使我们能够选择诊断性实验，并且在此过程中我们为随时间变化的边界提供了明确的证据。（《心理学文摘数据库记录》 ）
