Activation of the motor system following gaze cues is determined by hand access, not hand proximity
Bibliographic record
Abstract
The influence of gaze cues on target prioritisation (reaction times [RTs]) and movement execution (movement trajectories) differs based on the ability of the human gaze cue model to manually interact with the targets. Whereas gaze cues consistently impacted RTs, movement trajectories may only be affected when the hands of the human model had the potential to interact with the target. However, the perceived ability to interact with the targets was confounded by the proximity between the model's hands and the targets. The current study explored if the influence of gaze cues on movement trajectories is shaped by the model's potential to access and interact with the targets using their hands or simply the proximity of the hands. A centrally presented human model randomly gazed towards one of two peripheral target locations. Participants executed aiming movements to targets that non-predictively appeared at one location at a stimulus onset asynchrony of 100, 350, or 850 ms. In Experiment 1, the model's hands could not directly access the targets as each was holding a tray. In Experiment 2, the hands had direct access to the targets, but their palms-downwards orientation and wrist-flexed posture rendered efficiently interacting with the targets unlikely. Although RTs showed a facilitation effect of the gaze cue in both experiments, changes in movement trajectories were only observed when the model had direct access to the target (Experiment 2). The results of the current study suggest that the gaze model's direct hand access is necessary for the social gaze cues to influence movement execution.
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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 | 0.001 |
| 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".