To believe or not to believe? How voice and accent information in speech alter listener impressions of trust
Bibliographic record
Abstract
Our decision to believe what another person says can be influenced by vocally expressed confidence in speech and by whether the speaker–listener are members of the same social group. The dynamic effects of these two information sources on neurocognitive processes that promote believability impressions from vocal cues are unclear. Here, English Canadian listeners were presented personal statements ( She has access to the building) produced in a confident or doubtful voice by speakers of their own dialect (in-group) or speakers from two different “out-groups” (regional or foreign-accented English). Participants rated how believable the speaker is for each statement and event-related potentials (ERPs) were analysed from utterance onset. Believability decisions were modulated by both the speaker’s vocal confidence level and their perceived in-group status. For in-group speakers, ERP effects revealed an early differentiation of vocally expressed confidence (i.e., N100, P200), highlighting the motivational significance of doubtful voices for drawing believability inferences. These early effects on vocal confidence perception were qualitatively different or absent when speakers had an accent; evaluating out-group voices was associated with increased demands on contextual integration and re-analysis of a non-native representation of believability (i.e., increased N400, late negativity response). Accent intelligibility and experience with particular out-group accents each influenced how vocal confidence was processed for out-group speakers. The N100 amplitude was sensitive to out-group attitudes and predicted actual believability decisions for certain out-group speakers. We propose a neurocognitive model in which vocal identity information (social categorization) dynamically influences how vocal expressions are decoded and used to derive social inferences during person perception.
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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".