Face and body recognition in dancers and non-dancers
Bibliographic record
Abstract
The ability to recognize others is critical to our everyday social interactions. Although extensive research has explored the role of the face for person recognition, little has explored the role of the body, which may be used for recognition at a distance. Because bodies may be processed similarly to faces (Rhodes, Jeffery, Boeing, & Calder, 2013; Robbins, Coltheart, 2010; Robbins, Coltheart, 2012), we explored whether body recognition abilities are influenced by visual experience, as are face recognition abilities. We tested two groups with different types of visual experience with bodies: dancers (n=29), who spend much of their time observing and comparing bodies in form fitting clothing to achieve a physical aesthetic, and non-dancers (n=37), who tend to see bodies in more obstructive clothing and spend less of their time viewing bodies. Participants viewed images of bodies wearing identical clothing, and after a short break, selected which body from a pair of bodies they had seen before. Participants completed the same task with faces in a separate, counterbalanced block. We hypothesized that dancers would have better accuracy at recognizing bodies, but perform similarly to non-dancers at recognizing faces. First, we found that participants recognized faces better than bodies (p< 0.001), consistent with previous research (Burton, Wilson, Cowan, & Bruce, 1999). Additionally, we found that dancers recognized faces and bodies better than non-dancers (main effect of participant group, p=0.043). These results suggest that dancers are more accurate at recognizing identity using the body than non-dancers, possibly because of their extensive visual experience with bodies. More accurate face recognition abilities among dancers could result from facilitation effects across brain networks, as bodies and faces are typically seen together. Meeting abstract presented at VSS 2016
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".