Measuring the separate effects of practice and fatigue on eye movements during visual search
Bibliographic record
Abstract
Two experiments were conducted to examine how time-ontask (i.e., practice and fatigue) influences eye movements during visual search.In Experiment 1, we examined how practice influences eye movements during an extended visual search task.Results replicate the findings that over the course of a visual search task, performance improves and fixation duration increases.Yet changes in fixation duration did not correlate with changes in search performance.In Experiment 2, we examined how fatigue influences eye movements during an extended visual search task.To manipulate fatigue, participants either did or did not receive breaks.Those who did not receive breaks replicated the findings in Experiment 1. Critically, participants who did receive breaks showed no increase in fixation duration over the course of the visual search task.These results indicate that the increase in fixation duration with time-on-task reflects fatigue, and that this measure of fatigue can be derived independent of measures of performance improvements, such as shorter response times.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".