Does the face say it all? Examining face and body integration in whole-person perception.
Bibliographic record
Abstract
Every day, we perceive the people around us as visually integrated “persons” with faces and bodies. Previous research on this whole-person perception has focused on face and body recognition in isolation, overlooking how they combine in person-level recognition. In the current study, we examine the integration of faces and bodies in three experiments, characterizing the influence of body information on face perception and the influence of face information on body perception. In each experiment, participants made “same-different” decisions about two sequentially presented face-body composite images categorized as either congruent (e.g., faces and bodies were identical) or incongruent (e.g., different faces or different bodies). Holistic processing was probed by presenting face and body components as either spatially aligned or misaligned. In Experiment 1 (n=37), participants were instructed to make judgements strictly on faces and ignore the bodies. Participants’ evaluations of faces were influenced by body congruency in both the aligned and misaligned conditions, indicating there was integration of the face-body composites, but it was not holistic. In Experiment 2 (n=35), participants were instructed to make judgements on the bodies and ignore the faces. In contrast to Experiment 1, where performance was comparable between aligned and misaligned conditions, body-only judgements in Experiment 2 were more accurate for aligned conditions. In Experiment 3 (n=40), participants were asked to make body-only judgements in an intact whole-person condition, inverted head condition, or isolated body (e.g., no face) condition. Responses were most accurate in the intact face-body condition. Experiments 2 and 3 indicate body judgements benefit when the face and body are aligned as a unified person. Together, our results indicate that whole-person perception has distinct face-body components. Critically, there is an asymmetry where the body more strongly influences face judgements, and even the mere presence of misaligned body information will impact face perception.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".