The Power of a Handshake: Neural Correlates of Evaluative Judgments in Observed Social Interactions
Bibliographic record
Abstract
Effective social interactions require the ability to evaluate other people's actions and intentions, sometimes only on the basis of such subtle factors as body language, and these evaluative judgments may lead to powerful impressions. However, little is known about the impact of affective body language on evaluative responses in social settings and the associated neural correlates. This study investigated the neural correlates of observing social interactions in a business setting, in which whole-body dynamic stimuli displayed approach and avoidance behaviors that were preceded or not by a handshake and were followed by participants' ratings of these behaviors. First, approach was associated with more positive evaluations than avoidance behaviors, and a handshake preceding social interaction enhanced the positive impact of approach and diminished the negative impact of avoidance behavior on the evaluation of social interaction. Second, increased sensitivity to approach than to avoidance behavior in the amygdala and STS was linked to a positive evaluation of approach behavior and a positive impact of handshake. Third, linked to the positive effect of handshake on social evaluation, nucleus accumbens showed greater activity for Handshake than for No-handshake conditions. These findings shed light on the neural correlates of observing and evaluating nonverbal social interactions and on the role of handshake as a way of formal greeting.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".