The contribution of movement correlation in perceptual judgments of affiliation during social interaction
Bibliographic record
Abstract
Research in scene perception has demonstrated that humans are able to accurately recognize static visual stimuli with very brief exposures to a scene (Oliva, 2005). This ability is also true of the perception of social information. Very brief exposures or ‘thin slices’ of behavioral information are sufficient for accurately perceiving properties of social situations. Studies of this phenomena examine how the correlation of movement between two people vary as a result of their affiliation, and how this variation results in accurate perception of affiliation while observing conversation. Coordination of movement is ubiquitously present in social interactions, and this is more prominent when individuals are familiar with each other (Ambady & Rosenthal, 1992; Ng & Dunne). Experiment 1 quantified the variation in how individuals move during conversation based on their affiliation. New methodology using optical flow analysis to quantify motion was used. Results demonstrated that the correlation of movements between friends is significantly greater than the correlation during stranger interaction. Experiment 2 investigated how the perception of this coordination may contribute to accurate judgments of affiliation while observing interaction. We used the previous analysis of movement to examine how correlation serves as a cue for the accurate perception of affiliation by observers. Results demonstrated that although correlation was not a significant cue in affiliation perception, participants could indeed do the perceptual task. We suggest that the perception of social information is multi-faceted and cues may be differentially prioritized based on their availability for perception (i.e. viewing full-body versus facial correlation). These studies also highlight how the visual system may be examined using more complex yet ecologically valid stimuli. Further investigation is being conducted to determine how these visual cues may be perceived and prioritized in specific ways when making accurate social judgments. Meeting abstract presented at VSS 2013
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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 | 0.009 |
| 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 | 0.001 |
| 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".