Virtually simulated interpersonal touch negatively affects perceived closeness and social affiliation to an avatar partner
Bibliographic record
Abstract
Interpersonal touch is an essential component of human non-verbal communication, facilitating social affiliation and bonding. With the widespread use of digital interfaces and online platforms in all realms of human interactions, there are fewer opportunities for communicating through touch. Popular online platforms that virtually simulate human interactions rely primarily on visual and auditory modalities, providing limited or no capacity for the exchange of tactile cues. Previous studies of virtual interactions have explored the simulation of social touch using haptic devices, but little is known about how the visual representation of interpersonal touch is perceived and integrated into a virtual social experience. In two studies we examined how the exchange of virtual touch mediated by simulated 3-dimensional human characters, or avatars, within an online virtual environment influenced affiliation towards an unfamiliar interaction partner. Surprisingly, the exchange of virtual touch negatively affected the perceived closeness and affiliation to the partner and the social evaluation of the interaction but did not affect the level of physiological arousal during the interaction. These results indicate that the visual representation of social touch is sufficient to virtually communicate touch-related cues that impact social affiliation, but the influence of touch may be dependent on the interaction context.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".