Perceiving Others Through a Screen: Are First Impressions of Personality Accurate and Normative via Videoconferencing?
Bibliographic record
Abstract
The use of videoconferencing platforms has globally risen to facilitate face-to-face communication since the onset of COVID-19. But how do our first impressions of people we meet on Zoom compare to in-person interactions? Specifically, do we view others’ personalities as accurately (in line with their unique personality) and normatively (in line with the average, desirable personality) as in-person? Across two Zoom first-impression round-robin studies (exploratory study: N = 567, Dyads = 3,053; preregistered replication: N = 371, Dyads = 1,961), which we compared to an in-person round-robin study ( N = 306; Dyads = 1,682), people viewed others’ personalities as accurately and as normatively on Zoom as in-person. Moreover, people better liked interaction partners they viewed more accurately and normatively. However, in interactions of poorer audio-video quality, people viewed others less accurately, less normatively, and liked them less. Overall, through a screen, our impressions of others are as accurate and normative as face-to-face, but it depends on the quality of that screen.
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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 | 0.001 | 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 | 0.001 | 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) | 0.006 | 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".