Attentional blink during simulated driving
Bibliographic record
Abstract
The attentional blink (AB) is the finding that identification of the second of two sequentially presented targets (T1, T2) is impaired if they occur within about 500ms of each other. Although hundreds of AB studies have been conducted over the last 25 years, one criticism that has been leveled at AB research is that it has no real-world applicability. In the present work, we examine the AB within the context of driving a car. Three experiments were conducted in a simulator and, in all three, impairments consistent with the AB were found. In Experiment 1, participants followed a lead vehicle and a rapid serial visual presentation (RSVP) of digits, with two letters inserted in the stream, were presented on the back of that lead vehicle. Accuracy of identification of the T2 letter showed the typical AB pattern (i.e., impairment when presented 300ms after T1, and good performance when the separation was 700ms). In Experiment 2, the RSVP contained only a single target for identification, and T2 consisted of a response to the lead vehicle braking. RTs to press the break again showed a typical AB pattern, with improved (faster) responses as the separation between the two targets increased. In Experiment 3, drivers followed a lead car on a busy expressway and were told to drive as normally as possible while following that vehicle. At random intervals the lead vehicle would apply its brakes, and again RT to initiate a braking response was measured. Critically, cars in the adjacent lanes would occasionally engage their turn signal to indicate that they wanted to make a lane change. When that turn indicator was presented shortly before the lead vehicle applied its brakes, the participants' RT was impaired. Therefore, the results of the present work suggest that the AB does have real-world applications. Meeting abstract presented at VSS 2017
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".