The feeling of another’s knowing: How “mixed messages” in speech are reconciled.
Bibliographic record
Abstract
Listeners often encounter conflicting verbal and vocal cues about the speaker's feeling of knowing; these "mixed messages" can reflect online shifts in one's mental state as they utter a statement, or serve different social-pragmatic goals of the speaker. Using a cross-splicing paradigm, we investigated how conflicting cues about a speaker's feeling of (un)knowing change one's perception. Listeners rated the confidence of speakers of utterances containing an initial verbal phrase congruent or incongruent with vocal cues in a subsequent statement, while their brain potentials were tracked. Different forms of conflicts modulated the perceived confidence of the speaker, the extent to which was stronger for female listeners. A confident phrase followed by an unconfident voice enlarged an anteriorly maximized negativity for female listeners and late positivity for male listeners, suggesting that mental representations of another's feeling of knowing in face of this conflict were hampered by increased demands of integration for females and increased demands on updating for males. An unconfident phrase followed by a confident voice elicited a delayed sustained positivity (from 900 ms) in female participants only, suggesting females generated inferences to moderate the conflicting message about speaker knowledge. We highlight ways that verbal and vocal cues are real-time integrated to access a speaker's feeling of (un)knowing, while arguing that females are more sensitive to the social relevance of conflicting speaker cues. (PsycINFO Database Record
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".