Does the prolonged attentional blink to emotional stimuli affect driving performance?
Bibliographic record
Abstract
The attentional blink is a temporary delay in responding to a second stimulus in a stream after attending to the first. Recent investigations suggest the duration of the attentional blink is longer after viewing pictures with a negative emotional valence (Most, Chen, & Widders, 2005) though this effect has never been demonstrated in a day-to-day task. This study investigated whether this extended attentional blink would have an impact on driving performance. Participants were tested in a DriveSafety DS-600c driving simulator (a full car body surrounded by six viewing screens that immersed drivers in a 300 degree wrap-around virtual driving environment). They were required to drive down a virtual highway, following a lead vehicle. During the 75 minute drive they were exposed to high and low arousal pictures from the International Affective Picture System (IAPS: Lang, Bradley, & Cuthbert, 2001), pictures that had either a positive or negative emotional valence. In most cases, participants would simply be required to indicate whether the picture was positive or negative while lane keeping performance was assessed. However, occasionally the lead-vehicle would brake unexpectedly, forcing the participant to brake. Braking response times after positive and negative pictures were compared to those in a baseline (no picture) control condition. Results have practical implications as they relate to the enhanced collision risk that may occur as the result of upsetting images displayed on billboards or on in-vehicle entertainment devices (e.g., onboard DVD players).
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".