The Same Memory Retrieved Differently: Competition Enables Flexible Generalization in Hippocampus-like Networks
Abstract
Hippocampus supports the retrieval of richly detailed episodic memories from sparse cues. However, an event can be detailed and also lead to generalization. To explain both phenomena, researchers have proposed multiple memory systems or distinct memory traces for specific and general content. In contrast, we argue that a single system can support both specificity and generalization. To do this, we present ATHENA, a formal framework that reinterprets cognitive memory models as recurrent neural networks. We show that many episodic memory models are single-step approximations of dynamic networks with competitive retrieval mechanisms, similar to Modern Hopfield Networks. Notably, under a cognitive interpretation, the intensity of the competitive process is a psychologically-meaningful parameter that shapes generalization in the network. Through simulations, we demonstrate that modulating this parameter during retrieval leads the same traces to produce qualitatively different forms of generalization over the same memory traces. We then extend our analysis to sequential memory, and show how the same competitive retrieval process can be combined with known hippocampal circuits to achieve more sophisticated forms of generalization. These results demonstrate the flexibility of single-system memory retrieval and highlight the interplay of specific and general mnemonic content.
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BibTeX
@misc{ralston2026same,
title = {{The Same Memory Retrieved Differently: Competition Enables Flexible Generalization in Hippocampus-like Networks}},
author = {Robby Ralston and Brandon M. Turner and Vladimir Sloutsky},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/rsq7g_v2},
url = {https://osf.io/preprints/psyarxiv/rsq7g_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Same Memory Retrieved Differently: Competition Enables Flexible Generalization in Hippocampus-like Networks
AU - Robby Ralston
AU - Brandon M. Turner
AU - Vladimir Sloutsky
PY - 2026
DA - 2026/10/11
PB - PsyArXiv
DO - 10.31234/osf.io/rsq7g_v2
UR - https://osf.io/preprints/psyarxiv/rsq7g_v2/
AB - Hippocampus supports the retrieval of richly detailed episodic memories from sparse cues. However, an event can be detailed and also lead to generalization. To explain both phenomena, researchers have proposed multiple memory systems or distinct memory traces for specific and general content. In contrast, we argue that a single system can support both specificity and generalization. To do this, we present ATHENA, a formal framework that reinterprets cognitive memory models as recurrent neural networks. We show that many episodic memory models are single-step approximations of dynamic networks with competitive retrieval mechanisms, similar to Modern Hopfield Networks. Notably, under a cognitive interpretation, the intensity of the competitive process is a psychologically-meaningful parameter that shapes generalization in the network. Through simulations, we demonstrate that modulating this parameter during retrieval leads the same traces to produce qualitatively different forms of generalization over the same memory traces. We then extend our analysis to sequential memory, and show how the same competitive retrieval process can be combined with known hippocampal circuits to achieve more sophisticated forms of generalization. These results demonstrate the flexibility of single-system memory retrieval and highlight the interplay of specific and general mnemonic content.
ER -