The influence of speaker accent on the neurocognitive processing of politeness
Bibliographic record
Abstract
Does speaking with a foreign accent alter how listeners respond to verbal requests? Cooperative outcomes depend on multiple social factors, such as the politeness of the speaker who makes a request (e.g., their tone of voice). However, little is known about how indexical features derived from the speaker's voice influence neurocognitive operations during politeness communication. In an event-related potential (ERP) study, 31 participants listened to requests ("Please lend me a nickel") that varied in prosodic politeness (polite/rude), imposition level (low/high cost to perform an action), and accent (native/foreign English speaker). In separate rating tasks, participants made a social inference (speaker friendliness) or pragmatic inference (likelihood of request compliance) about each request based on available speech cues. ERPs time-locked to request onset revealed an interaction of speaker accent and prosodic (im)politeness on the P200 component (180-260 ms) and the subsequent late positivity (450-700 ms). Findings pointed to rapid perceptual differentiation of the speaker's linguistic status and stance (P200), after which listeners attended more deeply to attitudinal cues expressed by native ingroup speakers. ERPs evoked by the sentence-final imposition word showed that speaker characteristics influenced how the "cost" of requests was later evaluated, hampering N400 semantic operations (300-500 ms) when speakers acted rudely or had a foreign accent-conditions less frequently associated with cooperative interactions. Selective uptake of particular social cues relevant for drawing social versus pragmatic inferences about requests was also noted. Our data identify a time course and brain mechanisms that integrate speaker and message during politeness 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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".