Speaking and Listening with the Eyes: Gaze Signaling during Dyadic Interactions
Bibliographic record
Abstract
Cognitive scientists have long been interested in the role that eye gaze plays in social interactions. Previous research suggests that gaze acts as a signaling mechanism and can be used to control turn-taking behaviour. However, early research on this topic employed methods of analysis that aggregated gaze information across an entire trial (or trials), which masks any temporal dynamics that may exist in social interactions. More recently, attempts have been made to understand the temporal characteristics of social gaze but little research has been conducted in a natural setting with two interacting participants. The present study combines a temporally sensitive analysis technique with modern eye tracking technology to 1) validate the overall results from earlier aggregated analyses and 2) provide insight into the specific moment-to-moment temporal characteristics of turn-taking behaviour in a natural setting. Dyads played two social guessing games (20 Questions and Heads Up) while their eyes were tracked. Our general results are in line with past aggregated data, and using cross-correlational analysis on the specific gaze and speech signals of both participants we found that 1) speakers end their turn with direct gaze at the listener and 2) the listener in turn begins to speak with averted gaze. Convergent with theoretical models of social interaction, our data suggest that eye gaze can be used to signal both the end and the beginning of a speaking turn during a social interaction. The present study offers insight into the temporal dynamics of live dyadic interactions and also provides a new method of analysis for eye gaze data when temporal relationships are of interest.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".