Version history

{
  "scope": "monkey prefrontal cortex recordings + recurrent neural network models",
  "claim_text": "In monkey PFC during probabilistic reversal learning, a subspace encoding reversal probability shows reward-outcome integration consistent with a line-attractor model, with trial-start activity acting as a stable initial condition for non-stationary trial dynamics.",
  "raw_fields": {
    "n": 0,
    "doi": "10.7554/elife.103660",
    "claim": "In monkey PFC during probabilistic reversal learning, a subspace encoding reversal probability shows reward-outcome integration consistent with a line-attractor model, with trial-start activity acting as a stable initial condition for non-stationary trial dynamics.",
    "cite_key": "Kim2025b",
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    "effect_size": "qualitative",
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    "study_system": "monkey prefrontal cortex recordings + recurrent neural network models",
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    "replication_status": "replication_unknown",
    "claim_source_sentence": "We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": null
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  "section_id": "section_13",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "effect_size": "qualitative",
  "review_repo": "ComputationalReviewRecurrence",
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  "source_span": "We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model.",
  "study_system": "monkey prefrontal cortex recordings + recurrent neural network models",
  "evidence_refs": [
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      "ref": "paper:paper-bd6ab3ad8754"
    }
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  "section_title": "13. Attractor-network models — Hopfield, ring, line, bump; what each model requires of the cortical E→E matrix and what the mouse empirical record provides",
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    "notes": [
      "Local review repositories are read-only inputs.",
      "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
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    "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
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  "evidence_summary": "Analysis of monkey PFC recordings and trained RNNs on a probabilistic reversal-learning task; subspace dynamics fit a line attractor at trial start, with predictive modeling of non-stationary dynamics.",
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  "replication_status": "replication_unknown",
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}