The power of pausing in collaborative conversations
Bibliographic record
Abstract
• Long pauses while speaking tend to elicit negative impressions of communicators. • Brief pauses elicit assents (e.g., “yeah” or “uh-huh”) in collaborative dialogue. • Assents foster positive perceptions of speakers. • Pausing in collaborative conversations can make communicators seem more helpful. Communicators benefit from being perceived as helpful in collaborative conversations. While research has found that actions preceding such conversations can impact how communicators are perceived, less is known about how speaking style shapes such perceptions. Might how communicators talk (i.e., how often they pause) influence how helpful they seem? Though speakers who spend more time in silence while talking are often perceived negatively, we suggest that brief pauses while speaking can be beneficial. Specifically, we argue that pausing encourages verbal assents from conversation partners (e.g., “yeah” or “uh-huh”), which leads them to perceive speakers more positively. A multi-method study of collaborative conversations, including an analysis of customer service calls and two experiments manipulating pause frequency, supports this account. Although long silences can have impression management drawbacks, our findings indicate that, in collaborative conversations, brief pauses while speaking can make a person seem more helpful because they encourage conversation partners to assent.
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".