Predicting the Allocation of Attention: Using contextual guidance of eye movements to examine the distribution of attention
Bibliographic record
Abstract
Eye movements are often taken as a marker of where attention is allocated, but it is possible that the attentional window can be either tightly or broadly focused around the fixation point. Using target objects whose location could either be strongly predicted by scene context (High Certainty) or not (Low Certainty), we examined how attention was initially distributed across a scene image during search. To do so, an unexpected distractor object suddenly appeared either in the relevant or irrelevant scene region for each target type. Distractors will be more disruptive where attention is allocated. We found that for High Certainty targets, the distractors were fixated significantly more often when they appeared in relevant than irrelevant regions, but there was no such difference for Low Certainty targets. This finding demonstrated differential patterns of attentional distribution around the fixation point based on the predicted location of target objects within a scene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".