Attentional control settings affect attention but not perception: A study of gaze cues and pupilometry
Bibliographic record
Abstract
The human visual system is calibrated by an observer's goal state such that attention is only allocated to stimuli possessing task relevant properties. In the present research we investigated whether such attentional control settings extend to perceptual processing. To answer this question we made use of gaze cues - centrally presented schematic faces, with eyes either gazing left or right, which have been shown to generate reflexive shifts of attention to peripheral locations. Our study produced two novel findings. First, the shifts of attention generated by gaze cues are contingent on the schematic face being presented in a task relevant color. In other words, gaze cues are sensitive to attentional control settings. Second, when a face is presented upright at fixation, it causes a contraction in pupil size relative to when the same face is presented upside-down. Importantly, this contraction of pupil size does not depend on the face being presented in a task relevant color. That is, gaze cues that were outside the attentional control setting did not generate shifts of attention even though they were perceived as faces. These results demonstrate that processing within the visual system can be calibrated to prevent task irrelevant stimuli from capturing attention, but not from being perceived.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".