Default Feature Representations of the Cognitive Map
Updating a predictive cognitive map when the environment changes is a central problem for both biological agents and reinforcement learning, yet existing approaches either depend on explicit model knowledge or learn the full state-indexed map from samples. We propose Default Feature Representations (DFR), a featurized parameterization of predictive cognitive maps in which a fixed feature basis is composed with an operator that encodes the current environment. We provide two forms for the operator: a model-based closed form when the structural change between environments is known, and a model-free temporal-difference learning rule that recovers the operator from sampled transitions, with provable convergence to the model-based solution. The model-free DFR reconstructs the perturbed map from samples alone, achieves planning performance comparable to the model-based solution, and substantially outperforms successor-representation baselines on replanning tasks. We also show that DFR captures the local remapping of grid cells observed under local environmental change. By separating the cognitive map into a stable feature basis and a fast-adapting operator, DFR offers a sample-based account of how a predictive map can be updated from local experience, mirroring the stability of entorhinal grid fields across environments.