Version history

{
  "scope": "theoretical Bayesian model + human probabilistic learning",
  "claim_text": "A three-level hierarchical Gaussian filter (HGF) yields closed-form variational update rules in which the weight on prediction errors at each level scales inversely with precision, producing individual-difference markers of learning style.",
  "raw_fields": {
    "n": 0,
    "doi": "10.3389/fnhum.2011.00039",
    "claim": "A three-level hierarchical Gaussian filter (HGF) yields closed-form variational update rules in which the weight on prediction errors at each level scales inversely with precision, producing individual-difference markers of learning style.",
    "cite_key": "Mathys2011",
    "evidence": "Variational mean-field derivation produces analytic update equations; fit to behavior in probabilistic learning tasks.",
    "effect_size": "closed-form precision-weighted PE updates; individual subject parameters fitted",
    "text_access": "fulltext",
    "study_system": "theoretical Bayesian model + human probabilistic learning",
    "source_cluster_id": "cluster_12",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "Finally, by introducing parameters that determine the nature of the coupling between the levels of the hierarchical model, the optimality of an update is made conditional upon parameter values that may vary from agent to agent.",
    "replication_evidence_dois": [
      "10.1162/neco_a_00524",
      "10.3389/fnhum.2014.00825"
    ],
    "effect_size_source_sentence": null
  },
  "section_id": "section_13",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_13_evidence_package.json",
  "effect_size": "closed-form precision-weighted PE updates; individual subject parameters fitted",
  "review_repo": "ComputationalReviewLoops",
  "section_ref": "wiki_page:computationalreviewloops-13",
  "source_kind": "review_finding",
  "source_path": "evidence/section_13_evidence_package.json",
  "source_refs": [
    "paper:paper-03f80bc1208b"
  ],
  "source_span": "Finally, by introducing parameters that determine the nature of the coupling between the levels of the hierarchical model, the optimality of an update is made conditional upon parameter values that may vary from agent to agent.",
  "study_system": "theoretical Bayesian model + human probabilistic learning",
  "evidence_refs": [
    {
      "ref": "paper:paper-03f80bc1208b"
    }
  ],
  "section_title": "Computational Models of Loop Function",
  "source_policy": {
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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."
    ],
    "source_commit_sha": "0632aae8abc141909207fe91f6349b9e36489c3b",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops"
  },
  "evidence_summary": "Variational mean-field derivation produces analytic update equations; fit to behavior in probabilistic learning tasks.",
  "review_bundle_ref": "analysis_bundle:ab-d49e54403ef9",
  "replication_status": "independently_replicated",
  "review_package_ref": "analysis_bundle:ab-d49e54403ef9",
  "source_artifact_ref": "wiki_page:computationalreviewloops-13",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_13_evidence_package.json",
  "commit_sha": "0632aae8abc141909207fe91f6349b9e36489c3b",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops"
}