Synchrony During Online Encounters Affects Social Affiliation and Theory of Mind but Not Empathy
Bibliographic record
Abstract
Moving together in time affects human social affiliation and cognition. However, it is unclear whether these effects hold for on-line video meetings and whether they extend to empathy (understanding or sharing others' emotions) and theory of mind (ToM; attribution of mental states to others). 126 young adult participants met through online video in unacquainted pairs. Participants either performed 3 min of synchronous arm movements paced by sounds ( n = 40), asynchronous movements ( n = 46) or a small talk condition ( n = 40). In a subsequent empathy task, participants engaged in a conversation. A video recording of this conversation was played back, and each participant rated, at predetermined time points, how they felt and how they thought their partner felt. From this we calculated empathic accuracy (accuracy of the estimation of the other's emotions) and emotional congruence (emotion sharing). ToM was measured by showing videos of geometrical shapes interacting and asking the participants to describe what happened, measuring the amount of intentionality. We found that participants in the synchrony condition rated feeling greater closeness and similarity to their partners relative to the asynchronous condition. Further, participants in the synchrony group tended to ascribe more intentionality to the abstract shapes than participants in asynchrony condition, suggesting greater ToM. Synchrony and asynchrony groups did not reliably differ in empathic accuracy nor emotional congruence. These results suggest that moving in synchrony has effects on social affiliation measures even in online encounters. These effects extend to ToM tendencies but not empathic accuracy or emotion sharing. These results highlight the potential of synchronous movement in online encounters to affect a subset of social cognition and affiliation measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".