Liu Shuze, Gershman Samuel Joseph
PhD Program in Neuroscience, Harvard University, Cambridge, Massachusetts, United States of America.
Department of Psychology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.
PLoS Comput Biol. 2025 Sep 5;21(9):e1013444. doi: 10.1371/journal.pcbi.1013444. eCollection 2025 Sep.
Real-world decision-making often involves navigating large action spaces with state-dependent action values, taxing the limited cognitive resources at our disposal. While previous studies have explored cognitive constraints on generating action consideration sets or refining state-action mappings (policy complexity), their interplay remains underexplored. In this work, we present a resource-rational framework for policy compression that integrates both constraints, offering a unified perspective on decision-making under cognitive limitations. Through simulations, we characterize the suboptimality arising from reduced action consideration sets and reveal the complex interaction between policy complexity and action consideration set size in mitigating this suboptimality. We then use such normative insight to explain empirically observed phenomena in option generation, including the preferential sampling of generally valuable options and increased correlation in responses across contexts under cognitive load. We further validate the framework's predictions through a contextual multi-armed bandit experiment, showing how humans flexibly adapt their action consideration sets and policy complexity to maintain near-optimality in a task-dependent manner. Our study demonstrates the importance of accounting for fine-grained resource constraints in understanding human cognition, and highlights the presence of adaptive metacognitive strategies even in simple tasks.
现实世界中的决策通常涉及在具有状态依赖动作值的大型动作空间中进行导航，这会消耗我们有限的认知资源。虽然先前的研究已经探讨了认知对生成动作考虑集或完善状态-动作映射（策略复杂性）的限制，但它们之间的相互作用仍未得到充分探索。在这项工作中，我们提出了一个用于策略压缩的资源合理框架，该框架整合了这两种限制，为认知限制下的决策提供了统一的视角。通过模拟，我们刻画了因动作考虑集减少而产生的次优性，并揭示了策略复杂性和动作考虑集大小在减轻这种次优性方面的复杂相互作用。然后，我们利用这种规范性见解来解释在选项生成中实证观察到的现象，包括对一般有价值选项的优先采样以及在认知负荷下不同情境中反应之间增加的相关性。我们通过一个情境多臂老虎机实验进一步验证了该框架的预测，展示了人类如何灵活地调整他们的动作考虑集和策略复杂性，以任务依赖的方式保持接近最优性。我们的研究证明了在理解人类认知时考虑细粒度资源限制的重要性，并强调了即使在简单任务中也存在适应性元认知策略。
