{
  "abstract": "Current clinical assessment methods are insufficiently sensitive for detection of subtle post-concussion impairments in perceptual-cognitive function. The purpose of this study was to search for any perceptual response metrics associated with concussion history that might increase understanding of altered brain-behavior relationships. Immersive virtual reality test data were aggregated for 202 healthy adolescents and young adults (116 males, 86 females) who participated in different studies over a 2-year period. Left- versus right-directed neck rotation, arm reach, and step-lunge responses to sequential presentations of 2 types of horizontally moving visual stimuli were measured in terms of time to initiation of body segment movement (perceptual latency [PL]), as well as response completion (response time [RT]). Speed-accuracy tradeoff was represented by rate correct per second for PL (RCS-PL) and RT (RCS-RT) of neck and arm movements, and across-trial inconsistency was represented by PL variability (PLV) and RT variability (RTV). Both supervised machine learning and theory-based statistical regression methods were used to identify metrics that best discriminated between participants who reported a history of no concussion (NC), single concussion (SC), NC + SC, or multiple concussions (MC). Additionally, statistical regression was used to assess a theoretical relationship between metrics believed to align with components of the drift-diffusion computational model of decision-making. The best metric for discrimination between NC + SC and MC was Neck RCS-PL. Neck PLV values demonstrated a strong inverse logarithmic correlation with Neck RCS-PL (r = –0.796, P < 0.001). The Neck RCS-PL and Neck PLV behavioral metrics may have relevance to the two components of the drift-diffusion computational model of perceptual decision-making, and their combination may be associated with a cumulative and persisting deficiency after having sustained more than one lifetime concussion.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Health & Human Performance, University of Tennessee at Chattanooga, Chattanooga, Tennessee, United States of America",
    "Department of Business Analytics & Information Systems, Harbert College of Business, Auburn University, Auburn, Alabama, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
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      "name": "Gary B. Wilkerson"
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      "name": "Mohammad Joghataee"
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    {
      "name": "Ashish Gupta"
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  "doi": "10.1371/journal.pone.0358042",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358042&type=printable",
  "pmcid": null,
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  "published": "2026-09-21",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/aqen9qyecnped.ris",
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      "origin": "plos:anatomy",
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  "title": "Virtual reality, machine learning, and statistical modeling reveal an association between multiple concussion history and impaired perceptual decision-making",
  "title_text": "Virtual reality, machine learning, and statistical modeling reveal an association between multiple concussion history and impaired perceptual decision-making",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/aqen9qyecnped",
  "venue": "PLOS One",
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