Explaining effects of eye gaze on mediated group conversations:
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it