{
  "abstract": "Feedback-locked electroencephalography (EEG) responses are often interpreted using model-derived prediction errors (PEs), although signed PE can be strongly coupled to feedback valence in binary-outcome tasks. We revisited a published anxiety-related EEG association using two open probabilistic-learning datasets to examine measurement reliability, predictor separability, and participant-level recovery. In the primary dataset, greater trait anxiety was associated with a larger negative-minus-positive FCz theta response among 46 participants with elevated depressive symptoms after adjustment for depression severity. The association also differed between the sampled depression-symptom groups. The feedback contrast showed useful within-session reliability, but matched delta and P3-minus-N2 analyses did not establish theta specificity. Within participants, feedback valence and signed PE were highly correlated (r = -0.964). Exact-design simulations showed that population-level model discrimination could succeed despite poor recovery of individual neural slopes. Participant-specific learning-model refits recovered overall signed PE more consistently than the within-valence variation needed to distinguish it from feedback valence. An independent dataset reproduced the FCz feedback contrast in untreated and placebo cohorts but lacked trait-anxiety scores. Adding signed PE to valence yielded a small held-out predictive improvement under one reinforcement-learning specification, with limited support across sessions and no comparable support under the dual-rate model or in placebo. Together, these findings support a symptom-related feedback EEG association while constraining its theta-specific and participant-level computational interpretation.",
  "abstract_shown": true,
  "affiliations": [
    "University of Cincinnati"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "V. Sinha"
    },
    {
      "name": "V. Ravindra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/25zgt4cvpkry7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.04.762513",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.762513v1.full",
  "id": "25zgt4cvpkry7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.762513v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.762513v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/25zgt4cvpkry7.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T00:22:13Z",
      "native_id": "10.64898/2026.11.04.762513",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.04.762513v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Feedback Valence, Prediction Error, and the Limits of Computational Attribution in Symptom-Related EEG",
  "title_text": "Feedback Valence, Prediction Error, and the Limits of Computational Attribution in Symptom-Related EEG",
  "type": "preprint",
  "updated_at": "2026-11-07T00:20:58Z",
  "url": "https://research.draconitic.com/paper/25zgt4cvpkry7",
  "venue": "bioRxiv",
  "versions": []
}