Bibliographic record
Abstract
Previous studies have obtained contradictory conclusions regarding the effect of stress on visual attention. Some have reported that stress narrows attentional focus (Callaway and Dembo, 1958); others have reported that stress causes a broadening of attention (Braunstein-Bercovitz, 2003). To help resolve this situation, this study assessed the effect of mild stress on visual search. In a first experiment, two different conditions were used: short line among long lines, and long line among short lines. Prior to each task participants performed either easy (low stress) or difficult (high stress) math tasks (and were told that a debriefing (low stress) or a videotaped interview (high stress) would follow the experiment. The Short Stress State Questionnaire (Helton, 2004) measured stress induction effectiveness. Results show no difference in accuracy for different stress levels, but significantly faster response times and lower search slopes for the high-stress condition. In a second experiment using the same method we compared the effect of stress on two different tasks: conjunction search and feature search (similar to experiment 1). For the feature search task results show no difference in accuracy for different stress levels but significantly lower search slopes for the high-stress condition; For the conjunction search task, accuracy, response times and search slopes do not differ accross stress levels. The findings support the hypothesis of a broadening effect of stress on visual attention. Meeting abstract presented at VSS 2012
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".