Predicting strangers’ cooperativeness in Face-to-Face vs. Video-to-Video interactions: A case of inaccurate social prediction in mediated communication
Bibliographic record
Abstract
In modern organizations, video conferencing platforms are frequently used for key interactions, such as job interviews and virtual team collaborations. However, the accuracy of social predictions during brief virtual encounters remains uncertain. This study investigates the impact of different communication media on the accuracy of predicting cooperative or generous behavior among strangers after brief interactions. Grounded in Media Naturalness Theory (MNT) and evolutionary psychology , we explore whether video-to-video (VtV) interactions affect the accuracy of predicting cooperativeness compared to face-to-face (FtF) interactions. Across two behavioral studies, participants engaged in FtF and VtV interactions, with the second study introducing a condition where eye contact was enabled using gaze correction technology (VtV g ). Our findings consistently demonstrate that FtF interactions lead to significantly higher prediction accuracy than VtV, where accuracy levels did not surpass chance. Interestingly, enabling eye contact in VtV g did not significantly improve predictive accuracy. These results underscore the limitations of current video communication technologies in replicating the social cognitive and perceptual capabilities present in FtF interactions, with implications for remote interactions in organizational settings.
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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 | 0.007 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".