The Impact of Video-Mediated Communication on Social Predictions and Theory of Mind Activation
Bibliographic record
Abstract
Humans have a remarkable ability to predict strangers’ willingness to cooperate, an essential evolutionary trait to reduce free riding and foster social cooperation. This ability has been linked to the Theory of Mind (ToM), a cognitive mechanism that enables individuals to infer others’ intentions, beliefs, and behaviors. However, with the increasing prevalence of video-mediated communication tools such as Zoom and Google Meet, it is unclear whether this ability—evolved in face-to-face (FtF) settings—remains intact. This study addresses a critical gap in understanding how video-mediated communication affects ToM activation. Specifically, we explore whether video interactions impair cooperativeness predictions compared to FtF interactions, and the extent to which egocentric biases arise in mediated settings. We propose three hypotheses: cooperativeness prediction is more accurate than chance in FtF settings, no better than chance in video settings, and highly egocentric in video settings. Across three studies (n1=98, n2=120, n3=91), we confirmed these hypotheses with post hoc analyses ruling out language, non-verbal cues, and extended interaction time as explanations. A fourth study (n4=83) supported theoretical explanations and highlighted the role of eye gaze in prediction accuracy. Our findings suggest limitations in digital communication, impacting virtual team dynamics and trust formation, and indicate the need for technological advancements to enhance non-verbal cue perception in video conferencing.
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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 | 0.002 | 0.041 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".