Covert and overt orienting to gaze direction cues and the effects of fixation offset
Bibliographic record
Abstract
We examined covert and overt orienting in response to non-predictive gaze direction cues and investigated whether the subcortical superior colliculus (SC) plays a role in this type of orienting. Participants viewed a centrally presented gazing schematic face and responded to targets appearing at gazed-at or non-gazed-at locations either by making a keypress response while maintaining central fixation or by making an eye movement to the target. For both response conditions, the fixation stimulus (the gazing face) either remained on the screen or was extinguished at the time of target presentation, a manipulation known to engage and disengage the SC. Results revealed that participants making manual responses oriented covertly to the gazed-at location regardless of the fixation condition, and that participants making eye movements oriented overtly only if the fixation stimulus remained on the screen. Overt gaze-triggered orienting was not enhanced relative to covert orienting, and the fixation offset effect was not reduced for averted gaze cues relative to straight gaze cues. These findings suggest that gaze direction cues do not activate or predisengage the oculomotor system, and thus that orienting to gaze direction does not engage the SC. This is consistent with the view that gaze-triggered orienting is a unique form of reflexive orienting that depends crucially on cortical processes.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".