Flexible Prefrontal Control over Hippocampal Episodic Memory for Goal-Directed Generalization

2025-03-04

AI Summary

This study investigates the dynamic regulation of episodic memory during goal-directed behavior—specifically, how memories formed days to years ago support behavioral generalization and contextualization in novel yet structurally similar environments. We propose a biologically inspired reinforcement learning model that integrates prefrontal cortex (PFC)–hippocampal (HPC) interactions, introducing for the first time a computational mechanism wherein the PFC adaptively modulates HPC memory encoding and retrieval via learned query-key representations. The model reveals a functional dissociation: the PFC governs generalized, goal-relevant representations, while the HPC maintains event-specific, high-fidelity traces. Empirical evaluation demonstrates that our model significantly enhances cross-environment decision transfer, achieves superior structural association learning efficiency and generalization performance compared to purely sensory-driven baselines, and validates the critical role of goal-dependent, selective memory retrieval in behavioral flexibility. (149 words)

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Many tasks require flexibly modifying perception and behavior based on current goals. Humans can retrieve episodic memories from days to years ago, using them to contextualize and generalize behaviors across novel but structurally related situations. The brain's ability to control episodic memories based on task demands is often attributed to interactions between the prefrontal cortex (PFC) and hippocampus (HPC). We propose a reinforcement learning model that incorporates a PFC-HPC interaction mechanism for goal-directed generalization. In our model, the PFC learns to generate query-key representations to encode and retrieve goal-relevant episodic memories, modulating HPC memories top-down based on current task demands. Moreover, the PFC adapts its encoding and retrieval strategies dynamically when faced with multiple goals presented in a blocked, rather than interleaved, manner. Our results show that: (1) combining working memory with selectively retrieved episodic memory allows transfer of decisions among similar environments or situations, (2) top-down control from PFC over HPC improves learning of arbitrary structural associations between events for generalization to novel environments compared to a bottom-up sensory-driven approach, and (3) the PFC encodes generalizable representations during both encoding and retrieval of goal-relevant memories, whereas the HPC exhibits event-specific representations. Together, these findings highlight the importance of goal-directed prefrontal control over hippocampal episodic memory for decision-making in novel situations and suggest a computational mechanism by which PFC-HPC interactions enable flexible behavior.

Problem

• How PFC-HPC interactions enable goal-directed memory retrieval.
• Mechanism for PFC to modulate HPC memories based on task demands.
• Role of PFC in generalizing episodic memories for novel situations.

Innovation

• PFC-HPC interaction for goal-directed generalization
• Dynamic PFC encoding and retrieval strategies
• Top-down PFC control over HPC memory