Flexible Prefrontal Control over Hippocampal Episodic Memory for Goal-Directed Generalization
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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.
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In realistic interactions, appropriate responses often depend on implicit constraints such as user state, goals, or values that are not explicitly queried later.
“In realistic interactions, appropriate responses often depend on implicit constraints such as user state, goals, or values that are not explicitly queried later.”
From LoCoMo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026) · cites this paper as Zheng et al., 2025
The ‘Asymmetrical Environment with Multiple Goals’ section (page unavailable) shows that the agent must retrieve memories relevant to its current goal to navigate appropriately. But the paper studies simulated mazes, not realistic user interactions or user values, and it explicitly provides a goal indicator during later exploit trials rather than leaving the goal implicit. (LLM, conf=0.82)
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Research works printed but not linked: 11
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Cited by
Sorted by citations
LoCoMo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents
Li, Y., Guo, W., Zhang, L., Xu, R., Huang, M., Liu, H., Xu, L., Xu, Y., & Liu, J. (2026) · arXiv · doi:10.48550/arxiv.2602.10715
“( Zheng et al., 2025) Information is frequently conveyed implicitly, and later responses depend on inferred goals or constraints, a view consistent with findings from cognitive science and neuroscience (Amodio, 2019; Hoskins, 2024).”
Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents
Li, Y., Guo, W., Zhang, L., Xu, R., Huang, M., Liu, H., Xu, L., Xu, Y., & Liu, J. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1150
Relación de la memoria episódica con lasfunciones ejecutivas en jóvenes universitarios
Bermúdez, S. Q., & de Mejía, F. R. (2025) · Revista Latinoamericana de Ciencias Sociales Niñez y Juventud · doi:10.11600/rlcsnj.23.3.6980
- Authors
- Zheng, Y., Wolf, N., Ranganath, C., O'Reilly, R. C., & McKee, K. L.
- Affiliations
- University of California, Davis (US)
- University of California, Davis
- Astera Institute (US)
- Venue
- Proceedings of Cognitive Computational Neuroscience 2025
- Publisher
- Cognitive Computational Neuroscience
- Published
- 2025
- OpenAlex
- W4416507610
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Cognitive Computational Neuroscience (CCN) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- prefrontal cortex, hippocampus, episodic memory, top-down control, reinforcement learning models, working memory, memory retrieval, decision-making, novel environment
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