A calm eye is associated with the passive advantage in visual search
Bibliographic record
Abstract
Visual search can be more efficient when one views a display passively, allowing the target to pop into view, than when one actively directs attention around a display in a deliberate effort to locate a target (Smilek et al., 2006). However, little is known about why these different cognitive strategies lead to differences in performance. One possibility is that patterns of eye movements also differ with strategy, such that eye movements associated with the passive strategy allow search items to be registered in a more efficient way. Alternatively, the advantage of a passive strategy may accrue from processes that occur only after the search items have been registered, in which case one would not expect any differences in eye movements between the two strategies. In the experiments reported here, we monitored participants' gaze while they performed visual search tasks of varying difficulty after having been instructed to use either an active or a passive strategy. The passive strategy led to greater search efficiency (speed and accuracy) at all difficulty levels, which suggests that cognitive strategy may have even more influence on search performance than previously observed (Smilek et al., 2006). Furthermore, eye movement data showed that this passive advantage is correlated with fewer saccades per second and longer fixation durations. More detailed analyses examined differences in fixation location in the two conditions, and individual differences in eye movements independent of strategy. These findings are consistent with the hypothesis that the passive advantage in visual search is associated with a calmer eye.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".