From dyads to crowds: Perceptual unity of group interactions.
Bibliographic record
Abstract
The human visual system is well-tuned to detect not just the presence of other people, but also the social relationships between them. For example, recent work shows that facing dyads (or groups of two) are detected faster than non-facing dyads in visual search tasks. Everyday life however often involves social groups larger than two, and so here we examined if this perceptual advantage also occurs for groups of three or more individuals. Participants searched either for groups of facing individuals (in arrays of non-facing individuals) or groups of non-facing individuals (in arrays of facing individuals). In Experiment 1, facing groups of three (triads) were detected faster relative to non-facing triads, suggesting a perceptual advantage for interacting groups. Experiment 2 further indicated that this search advantage was not driven by perceptual grouping of dyads within triads, as it vanished when triad group unity was reduced to dyads by singling out one individual. Experiment 3 showed that search advantage for facing triads held when the triads were inverted, suggesting that perception of body orientation may be one of the principles behind social perceptual grouping. Finally, in Experiment 4 we manipulated group size from three to seven individuals and found that the magnitude of the social search advantage was modulated by this factor. Thus, human perception appears to be well tuned to extract and represent not just simple social relationships but also more complex group social structures as well.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".