{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Robby Ralston"
    },
    {
      "name": "Brandon M. Turner"
    },
    {
      "name": "Vladimir Sloutsky"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5axkxpwk9dvr3.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/rsq7g_v2",
  "fulltext_url": null,
  "id": "5axkxpwk9dvr3",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/rsq7g_v2/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Cognitive Neuroscience"
    ],
    [
      "bepress",
      "Cognitive Psychology"
    ],
    [
      "bepress",
      "Computational Neuroscience"
    ],
    [
      "bepress",
      "Memory"
    ],
    [
      "bepress",
      "Neuroscience"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "2805|Memory and Neural Mechanisms"
    ]
  ],
  "openalex": "W7221506662",
  "pdf_url": "https://osf.io/rsq7g/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5axkxpwk9dvr3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:55Z",
      "native_id": "rsq7g",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/rsq7g_v1/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Cognitive Neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "The Same Memory Retrieved Differently: Competition Enables Flexible Generalization in Hippocampus-like Networks",
  "title_text": "The Same Memory Retrieved Differently: Competition Enables Flexible Generalization in Hippocampus-like Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:55Z",
  "url": "https://research.draconitic.com/paper/5axkxpwk9dvr3",
  "venue": "PsyArXiv",
  "versions": []
}