Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS Comput Biol. 2024 Nov 12;20(11):e1012582. doi: 10.1371/journal.pcbi.1012582. eCollection 2024 Nov.
How people plan is an active area of research in cognitive science, neuroscience, and artificial intelligence. However, tasks traditionally used to study planning in the laboratory tend to be constrained to artificial environments, such as Chess and bandit problems. To date there is still no agreed-on model of how people plan in realistic contexts, such as navigation and search, where values intuitively derive from interactions between perception and cognition. To address this gap and move towards a more naturalistic study of planning, we present a novel spatial Maze Search Task (MST) where the costs and rewards are physically situated as distances and locations. We used this task in two behavioral experiments to evaluate and contrast multiple distinct computational models of planning, including optimal expected utility planning, several one-step heuristics inspired by studies of information search, and a family of planners that deviate from optimal planning, in which action values are estimated by the interactions between perception and cognition. We found that people's deviations from optimal expected utility are best explained by planners with a limited horizon, however our results do not exclude the possibility that in human planning action values may be also affected by cognitive mechanisms of numerosity and probability perception. This result makes a novel theoretical contribution in showing that limited planning horizon generalizes to spatial planning, and demonstrates the value of our multi-model approach for understanding cognition.
人们如何进行计划是认知科学、神经科学和人工智能的一个活跃研究领域。然而，传统上用于在实验室中研究规划的任务往往受到人工环境的限制，例如国际象棋和强盗问题。迄今为止，对于人们如何在导航和搜索等现实情境中进行规划，仍然没有达成共识的模型，在这些情境中，价值直观地源于感知和认知之间的相互作用。为了解决这一差距，并朝着更自然的规划研究方向前进，我们提出了一种新颖的空间迷宫搜索任务 (Maze Search Task, MST)，其中成本和奖励以距离和位置的形式实际存在。我们在两项行为实验中使用了这个任务，以评估和对比规划的多个不同计算模型，包括最优期望效用规划、受信息搜索研究启发的几种单步启发式方法，以及一系列偏离最优规划的规划器，其中动作值由感知和认知之间的相互作用来估计。我们发现，人们对最优期望效用的偏离最好用具有有限视野的规划器来解释，然而，我们的结果并不排除在人类规划中，动作值可能也受到数量和概率感知的认知机制的影响。这一结果在表明有限的规划视野推广到空间规划方面做出了新颖的理论贡献，并展示了我们的多模型方法对于理解认知的价值。
