{
  "abstract": "Anticipatory neurophysiological activity before unpredictable emotional events has been reported for decades, but the effects are small, difficult to replicate, and obtained almost exclusively in stripped-down laboratory paradigms. Whether they reflect genuine predictive processes or methodological artifacts remains debated; one rarely tested possibility is that such settings mute it. We examined anticipation in an immersive virtual reality (VR) driving simulation whose collision outcomes were assigned by a quantum random number generator, while EEG, photoplethysmography (PPG), EMG, EOG and inertial (IMU) signals were recorded from a single wireless dry-electrode headset built into the VR display. Sixteen participants passively observed 120 driving sequences ending in a collision or no collision. Length-matched pre- and post-stimulus windows were analysed identically with mass-univariate hierarchical general linear models under permutation cluster correction, plus machine-learning classification across held-out participants. Two post-stimulus clusters differentiated the conditions, the largest peaking at 451.6 ms (d = -1.35), with broadband power modulation robust to baseline modelling, decoded from held-out participants with up to 93.8% accuracy. No time-domain effect or decodable information preceded stimulus onset, but a broadband spectral difference did, surviving preregistered controls for expectation bias (run length), time-on-task build-up and ocular or facial-muscle artifact: directionally consistent in 13 of 15 participants, with pre- and post-stimulus power coupled in all 16. Heart rate differentiated the conditions neither alone nor when added to the classifier. The study was preregistered; the BIDS-format data, analysis code and an open-source EEGLAB plugin are publicly available.",
  "abstract_shown": true,
  "affiliations": [
    "The Brain and Cognition research center (CerCo), CNRS"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "C. Cannard"
    },
    {
      "name": "D. Demet Yesilbas"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/00h5zq0hzzt6r.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.24.754154",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754154v1.full",
  "id": "00h5zq0hzzt6r",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754154v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7214906358",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754154v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/00h5zq0hzzt6r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:19Z",
      "native_id": "10.64898/2026.09.24.754154",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754154v3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Reactive and predictive processes during unpredictable driving hazards in virtual reality: an exploratory brain and body study with multimodal neurophysiological monitoring",
  "title_text": "Reactive and predictive processes during unpredictable driving hazards in virtual reality: an exploratory brain and body study with multimodal neurophysiological monitoring",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:43Z",
  "url": "https://research.draconitic.com/paper/00h5zq0hzzt6r",
  "venue": "bioRxiv",
  "versions": []
}