The effect of context on oculomotor capture: It's better not to think about it
Bibliographic record
Abstract
An abundance of research has demonstrated that attention is captured by the appearance of abrupt onsets in visual displays. Abrupt onsets capture both covert and overt attentional systems and are thought to do so largely, if not entirely, in a bottom-up stimulus driven fashion, independent of any top-down attentional factors. However, Kramer et al. (2000) demonstrated that the magnitude of oculomotor capture could be influenced by participants' awareness of the abrupt onset. By making the abrupt onset more salient, younger individuals demonstrated less oculomotor capture than when the abrupt onset was less salient. This finding highlights the possibility that the magnitude of oculomotor capture may be influenced by contextual factors. In the present study, awareness was manipulated via task instruction while stimulus properties were kept consistent across all conditions. Prior to completing a traditional oculomotor capture task, participants were either a) informed that an onset would appear on some trials, b) informed of the abrupt onset and instructed to actively avoid looking toward it, or c) were not informed of the presence of the abrupt onset. Results provide evidence that the instructions influenced the magnitude of oculomotor capture. Those instructed to actively avoid the abrupt onset were worse at doing so than those simply informed of its presence. Those not told of the abrupt onset fell between these two extremes. Consistent with the view that abrupt onsets capture attention and the eyes in a bottom-up manner, oculomotor capture was observed in all conditions; however, the present findings highlight the potential importance of context in modulating this effect.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".