Seeing the Way: the Role of Vision in Conversation Turn Exchange Perception
Bibliographic record
Abstract
During conversations, we engage in turn-taking behaviour that proceeds back and forth effortlessly as we communicate. In any given day, we participate in numerous face-to-face interactions that contain social cues from our partner and we interpret these cues to rapidly identify whether it is appropriate to speak. Although the benefit provided by visual cues has been well established in several areas of communication, the use of visual information to make turn-taking decisions during conversation is unclear. Here we conducted two experiments to investigate the role of visual information in identifying conversational turn exchanges. We presented clips containing single utterances spoken by single individuals engaged in a natural conversation with another. These utterances were from either right before a turn exchange (i.e., when the current talker would finish and the other would begin) or were utterances where the same talker would continue speaking. In Experiment 1, participants were presented audiovisual, auditory-only and visual-only versions of our stimuli and identified whether a turn exchange would occur or not. We demonstrated that although participants could identify turn exchanges with unimodal information alone, they performed best in the audiovisual modality. In Experiment 2, we presented participants audiovisual turn exchanges where the talker, the listener or both were visible. We showed that participants suffered a cost at identifying turns exchanges when visual cues from the listener were not available. Overall, we demonstrate that although auditory information is sufficient for successful conversation, visual information plays an important role in the overall efficiency of communication.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".