"Where you look, I will follow": Probing the time-course of facilitation and inhibition in gaze cueing of attention
Bibliographic record
Abstract
Although we can attend to objects and locations that we are not looking at, we generally look at the objects and locations that are the focus of our attention. Because of this tight coupling between attention and eye gaze location, determining where another person is looking is an important cue we use to understand the focus of another person's attention. As such, gaze cueing has been investigated because it is a powerful biological cue involved in social interaction and development. The results of this work indicate that orienting of attention to gaze cues may have a unique neural mechanism because it has been demonstrated to be associated with behavioural effects of both bottom-up and top-down control of attention. The majority of this work, however, has focused on reaction time (RT) in discrete button pressing tasks. The current study employed an aiming movement paradigm because the analysis of both temporal (RT) and spatial (movement angle) measures of aiming movements can provide a greater resolution of the time-course of gaze cueing effects. Analysis of RTs revealed facilitation effects for cued targets at SOAs of 100 ms, 250 ms and 400 ms, suggesting an early but prolonged orientation to gaze cued targets. Interestingly, there was also a facilitation effect observed in the movement angle measure at the 250 ms SOA, corresponding to the timing of the maximal facilitation effect observed in the RT measure. These results suggest that there are overlapping attentional and motoric contributions to attention orienting to gaze cues.Acknowledgments: This research was supported by grants from the Natural Sciences and Engineering Research Council.
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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 | 0.002 |
| 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 | 0.001 |
| 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".