From dyads to triads: Perceptual unity of social groups
Bibliographic record
Abstract
While much existing work in social perception has focused on how we detect and recognize people and their individual social cues, most encounters in life involve more than one person. Indeed, recent work has demonstrated that visual perception is sensitive to social interactions, with facing dyads located more efficiently than non-facing ones, and individuals within facing dyads found less efficiently than individuals in non-facing ones. This suggests that interacting dyads are processed as visual perceptual units. Here we assessed if perception may be similarly specialized for larger interacting groups, such as triads or groups of three. To test this, we used a visual search task in which participants located either a facing triad (among non-facing triads) or a non-facing triad (among facing triads). The triads were either comprised of all individuals depicted in neutral poses (uniform triads) or of two individuals depicted in neutral poses and one individual depicted performing a pointing gesture (non-uniform triads). Participants were faster to find facing triads, but only when they were uniform. This search advantage for uniform facing triads suggests that, similar to dyads, our perceptual system is well-tuned to perceive larger interacting groups as well. Thus, human visual perception appears to be sensitive to sophisticated information about the relationship between multiple interacting people.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".