Department of Psychology, Princeton University, Princeton, NJ, USA.
Department of Psychology, University of California, Berkeley, CA, USA.
Nat Hum Behav. 2022 Aug;6(8):1112-1125. doi: 10.1038/s41562-022-01332-8. Epub 2022 Apr 28.
Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near optimal under some circumstances but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.
做出明智的决策需要提前思考，但对于计算能力受限的主体（例如人类）而言，需要考虑的行动和结果数量巨大，因此详尽的规划是不可行的。那么，当人们的行为具有深远影响时，他们如何能够解决新问题，这是认知科学中长期存在的一个问题。为了解决这个问题，我们提出了一种资源受限规划模型，该模型允许我们推导出最佳规划策略。我们发现，以前提出的启发式算法（例如最佳优先搜索）在某些情况下接近最优，但在其他情况下则不然。在鼠标跟踪范式中，我们表明人们会相应地调整其规划策略，以与最佳模型大致一致但与任何单个启发式模型都不一致的方式进行规划。我们还发现与最优模型存在系统偏差，这可能是由于尚未发现的其他认知限制所致。
