Version history

{
  "scope": "clinical population",
  "claim_text": "Drift-diffusion modelling decomposes choice/RT in this paradigm to dissociate evidence accumulation rate, decision threshold, and non-decision time, supporting the cortico-loop interpretation of perceptual or value-based decisions. (Neural Substrates of the Drift-Diffusion Model in Brain Disorders, 2021).",
  "raw_fields": {
    "n": 0,
    "doi": "10.3389/fncom.2021.678232",
    "claim": "Drift-diffusion modelling decomposes choice/RT in this paradigm to dissociate evidence accumulation rate, decision threshold, and non-decision time, supporting the cortico-loop interpretation of perceptual or value-based decisions. (Neural Substrates of the Drift-Diffusion Model in Brain Disorders, 2021).",
    "cite_key": "Gupta2022",
    "evidence": "Frontiers in computational neuroscience (2021); EPMC abstract/fulltext.",
    "effect_size": null,
    "text_access": "fulltext",
    "study_system": "clinical population",
    "source_cluster_id": "cluster_12",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "The drift-diffusion model fits show that patients with schizophrenia favored the accuracy over the speed with impaired learning on negative feedback (Moustafa et al., 2015 ).",
    "replication_evidence_dois": [],
    "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": null,
  "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-f9a1a0a1a709"
  ],
  "source_span": "The drift-diffusion model fits show that patients with schizophrenia favored the accuracy over the speed with impaired learning on negative feedback (Moustafa et al., 2015 ).",
  "study_system": "clinical population",
  "evidence_refs": [
    {
      "ref": "paper:paper-f9a1a0a1a709"
    }
  ],
  "section_title": "Computational Models of Loop Function",
  "source_policy": {
    "mode": "public_source_pointer_with_short_context",
    "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": "Frontiers in computational neuroscience (2021); EPMC abstract/fulltext.",
  "review_bundle_ref": "analysis_bundle:ab-d49e54403ef9",
  "replication_status": "replication_unknown",
  "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"
}