Looking, pointing, and talking together: How dyads of differential expertise coordinate attention during conversation
Bibliographic record
Abstract
When people discuss something that they can both see, their attention becomes increasingly coupled. Previous studies have found that this coupling is temporally asymmetric (e.g., one person leads and one follows) when dyads are assigned conversational roles (e.g., speaker and listener). And while such studies have focused on the coupling of gaze, there is also evidence that people use their hands to coordinate attention. The present study uses a visual task to expand on this past work in two respects. First, rather than assigning conversational roles, participants' background knowledge was manipulated (e.g., expert and novice) to elicit differential roles inherent to the conversation. Second, participants were permitted to gesture freely while interacting. Cross Recurrence Quantification Analysis with data from mobile eye trackers and manually coded pointing gestures revealed that although more knowledgeable participants dominated the dialogue by talking and pointing more, the symmetry of coupled behaviors (gaze and pointing) between participants remained fixed. Asymmetric attentional coupling emerged, although this was dependent on conversational turn taking. Specifically, regardless of background knowledge, the currently speaking participant led attention, both with the eyes and with the hands. These findings suggest stable, turn-dependent interpersonal coupling dynamics, and highlight the role of pointing gestures and conversational turn-taking in multimodal attention coordination.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".