Responsibility in the Global Information Society
Bibliographic record
Abstract
In speech communication, listeners must accurately decode vocal cues that refer to the speaker's mental state, such as their confidence or 'feeling of knowing'. However, the time course and neural mechanisms associated with online inferences about speaker confidence are unclear. Here, we used event-related potentials (ERPs) to examine the temporal neural dynamics underlying a listener's ability to infer speaker confidence from vocal cues during speech processing. We recorded listeners' real-time brain responses while they evaluated statements wherein the speaker's tone of voice conveyed one of three levels of confidence (confident, close-to-confident, unconfident) or were spoken in a neutral manner. Neural responses time-locked to event onset show that the perceived level of speaker confidence could be differentiated at distinct time points during speech processing: unconfident expressions elicited a weaker P2 than all other expressions of confidence (or neutral-intending utterances), whereas close-to-confident expressions elicited a reduced negative response in the 330-500 msec and 550-740 msec time window. Neutral-intending expressions, which were also perceived as relatively confident, elicited a more delayed, larger sustained positivity than all other expressions in the 980-1270 msec window for this task. These findings provide the first piece of evidence of how quickly the brain responds to vocal cues signifying the extent of a speaker's confidence during online speech comprehension; first, a rough dissociation between unconfident and confident voices occurs as early as 200 msec after speech onset. At a later stage, further differentiation of the exact level of speaker confidence (i.e., close-to-confident, very confident) is evaluated via an inferential system to determine the speaker's meaning under current task settings. These findings extend three-stage models of how vocal emotion cues are processed in speech comprehension (e.g., Schirmer & Kotz, 2006) by revealing how a speaker's mental state (i.e., feeling of knowing) is simultaneously inferred from vocal expressions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".