Attenuated electroencephalographic activity following risk-taking in high risk drivers
Bibliographic record
Abstract
Background: High-risk drivers (HRDs) contribute disproportionately to road traffic crashes by repeatedly engaging in behavious such as speeding and driving while impaired (DWI). Therefore, early detection and injury prevention strategies would benefit from identifying the underlying neurobiology of HRD behaviours. Previous work has identified abnormal electroencephalographic (EEG) activity as being associated with impaired decision-making, a characteristic displayed by many high-risk drivers. Specifically, an attenuated EEG signal following impaired decision-making distinguished between participants who continued to engage in risky behaviors and a reference group. Objectives: Investigate whether this pattern of abnormal EEG activity could be a neurobiological marker for high-risk drivers, and be used to distinguish high-risk drivers from the heterogeneous driving population. Methods: Two groups are re-recruited from the lab’s database: HRDs and non-offenders (n=20, N=40). HRDs include licensed drivers convicted of 3+ HRD events within a 2-year period (road or criminal conviction, first DWI conviction, refusal to provide a breath sample, DWI recidivism). Non-offenders do not fulfill HRD criteria. Participants will undergo electroencephalography (EEG) while being submitted to the Game of Dice Task, a neuropsychological task that assesses aversion/attraction to risky decision. Expected Results: HRDs will exhibit more impaired decision-making, linked to behavioral risk-taking, than non-offenders. HRDs exhibiting impaired decision-making will also display an attenuated EEG response following risk-taking in the neuropsychological task compared to non-offenders. Anticipated Conclusions: Analysis will establish whether an attenuated EEG response distinguishes HRDs from non-offenders. Future studies will investigate the explanatory potential of EEG response to impaired decision-making and risk-taking behaviours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".