Cortical processing of speaker politeness: Tracking the dynamic effects of voice tone and politeness markers
Bibliographic record
Abstract
Information in the tone of voice alters social impressions and underlying brain activity as listeners evaluate the interpersonal relevance of utterances. Here, we presented requests that expressed politeness distinctions through the voice (polite/rude) and explicit linguistic markers (half of the requests began with Please). Thirty participants performed a social perception task (rating friendliness) while their electroencephalogram was recorded. Behaviorally, vocal politeness strategies had a much stronger influence on the perceived friendliness than the linguistic marker. Event-related potentials revealed rapid effects of (im)polite voices on cortical activity prior to ~300 ms; P200 amplitudes increased for polite versus rude voices, suggesting that the speaker’s polite stance was registered as more salient in our task. At later stages, politeness distinctions encoded by the speaker’s voice and their use of Please interacted, modulating activity in the N400 (300–500 ms) and late positivity (600–800 ms) time windows. Patterns of results suggest that initial attention deployment to politeness cues is rapidly influenced by the motivational significance of a speaker’s voice. At later stages, processes for integrating vocal and lexical information resulted in increased cognitive effort to reevaluate utterances with ambiguous/contradictory cues. The potential influence of social anxiety on the P200 effect is also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".