Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America.
PLoS Comput Biol. 2021 Dec 31;17(12):e1009688. doi: 10.1371/journal.pcbi.1009688. eCollection 2021 Dec.
From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target's location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants' behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.
从烹饪一顿饭到找到去目的地的路线，许多现实生活中的决策可以分解为层次化的子决策。在层次结构中，选择要考虑的决策需要在潜在的广泛决策序列空间上进行规划。为了深入了解人们如何决定决定什么，我们研究了一项将感知决策、主动感知以及分层和反事实推理相结合的新任务。人类参与者必须在决策树的最底层找到目标。他们可以从决策树的不同节点收集信息，以收集有关目标位置的嘈杂证据。只有在叶节点出现错误后，才会提供反馈，并提供关于错误原因的模糊证据。尽管任务很复杂（有 107 个潜在状态），但参与者能够在任务中有效地进行规划。该过程的计算模型确定了少数几种计算复杂度较低的启发式方法，可以解释人类的行为。这些启发式方法包括在决策树的分支点上做出分类决策，而不是传递整个概率分布，丢弃被认为不可靠的感官证据以做出选择，并在初始计划失败后使用选择置信度来推断错误的原因。基于概率推理或短视采样规范的计划无法捕捉到参与者的行为。我们的结果表明，有可能在人类行为中识别出具有传感功能的启发式规划特征，并且使用中等复杂程度的任务有助于确定人类在决策层次结构上推理的规则。
