Piray Payam, Daw Nathaniel D
Department of Psychology, University of Southern California, Los Angeles, CA, USA.
Department of Psychology, and Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Nat Commun. 2025 Aug 12;16(1):7444. doi: 10.1038/s41467-025-62733-7.
The influential concept of cognitive maps envisions that the brain builds mental representations of objects, barriers, and goals. Computational models show how these representations guide goal-directed behavior, such as planning novel routes to maximize rewards. One key feature of flexible cognitive representations is compositionality, the ability to build complex structures by recombining simpler parts. However, how this applies to neural representations of cognitive maps and map-based planning remains unclear. Compositionality can be difficult to reconcile with efficient planning, as reusing components may limit efficiency. Here, we propose a novel computational model for efficiently creating and planning with compositional predictive maps, which successfully reproduces response fields in the medial entorhinal cortex, particularly object vector cells and grid cells. The model treats each object as an alteration to a baseline map linked to open space, creating predictive maps by combining object-related representations compositionally, providing insights into brain processes supporting efficient, flexible planning.
认知地图这一颇具影响力的概念设想，大脑构建关于物体、障碍和目标的心理表征。计算模型展示了这些表征如何指导目标导向行为，比如规划新颖路线以最大化奖励。灵活认知表征的一个关键特征是组合性，即通过重新组合更简单部分来构建复杂结构的能力。然而，这如何应用于认知地图的神经表征以及基于地图的规划仍不清楚。组合性可能难以与高效规划相协调，因为重复使用组件可能会限制效率。在此，我们提出一种新颖的计算模型，用于高效创建和规划组合性预测地图，该模型成功再现了内嗅皮层中的反应场，特别是物体向量细胞和网格细胞。该模型将每个物体视为与开放空间相关联的基线地图的一种改变，通过组合与物体相关的表征来创建预测地图，为支持高效、灵活规划的大脑过程提供了见解。
