Explaining effects of eye gaze on mediated group conversations:
Bibliographic record
Abstract
We present an experiment examining effects of gaze on speech during three-person conversations. Understanding such effects is crucial for the design of teleconferencing systems and Collaborative Virtual Environments (CVEs). Previous findings suggest subjects take more turns when they experience more gaze. We evaluated whether this is because more gaze allowed them to better observe whether they were being addressed. We compared speaking behavior between two conditions: (1) in which subjects experienced gaze synchronized with conversational attention, and (2) in which subjects experienced random gaze. The amount of gaze experienced by subjects was a covariate. Results show subjects were 22% more likely to speak when gaze behavior was synchronized with conversational attention. However, covariance analysis showed these results were due to differences in amount of gaze rather than synchronization of gaze, with correlations of .62 between amount of gaze and amount of subject speech. Task performance was 46% higher when gaze was synchronized. We conclude it is commendable to use synchronized gaze models when designing CVEs, but depending on task situation, random models generating sufficient amounts of gaze may suffice.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".