Sensitivity to cue consistency in multi-agent contexts: Effects of cue type and group size
Bibliographic record
Abstract
Humans spontaneously follow where others are looking, with this process in groups modulated by the number of consistent gaze cues. In groups of three, a minority of consistent gaze cues facilitates target responses; in groups of five, a majority of consistent cues is needed for similar response facilitation. Are such evaluative processes unique to gaze cues? We investigated this question in two preregistered experiments in which participants responded to targets cued by a group of three (E1, N=156) or five (E2, N=154) faces or visually matched directional arrows. Participants saw a group of cues at fixation and identified a peripheral target appearing on the left or right of the group. The target location could be cued by 0, 1, 2 or 3 cues in Experiment 1 or by 0, 1, 2, 3, 4 or 5 cues in Experiment 2. In Experiment 1, faster overall responses occurred in response to gaze relative to arrow cues, while reliable target facilitation tracked with increasing consistent cue numerosity for both cue types. In Experiment 2, target facilitation also tracked with increasing cue numerosity for both cue types with no overall differences in speed of responding for gaze and arrow cues. Interestingly, while for gaze cues, targets were reliably facilitated by one, two, and three consistent cues, there were no differences in responses between three and four consistent gaze cues. In contrast, for arrow cues, target facilitation followed a linear trend of reliable facilitation with increasing cue numerosity. These results show that while target responses can be facilitated by the minority of consistent biological and directional cues, responses are also modulated by group size and show nuanced effects across biological cue consistency increases. As such, these results highlight the sensitivity of human perceptual and attentional processes to the complexity of visual information in multi-agent contexts.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".