Superficial auditory (dis)fluency biases higher-level social judgment
Bibliographic record
Abstract
When talking to other people, we naturally form impressions based not only on what they say but also on how they say it-e.g., how confident they sound. In modern life, however, the sounds of voices are often determined not only by intrinsic qualities (such as vocal anatomy) but also by extrinsic properties (such as videoconferencing microphone quality). Here, we show that such superficial auditory properties can have surprisingly deep consequences for higher-level social judgments. Listeners heard short narrated passages (e.g., from job application essays) and then made various judgments about the speakers. Critically, the recordings were modified to simulate different microphone qualities, while carefully equating listeners' comprehension of the words. Though the manipulations carried no implications about the speakers themselves, common disfluent auditory signals (as in "tinny" speech) led to decreased judgments of intelligence, hireability, credibility, and romantic desirability. These effects were robust across speaker gender and accent, and they occurred for both human and clearly artificial (computer-synthesized) speech. Thus, just as judgments from written text are influenced by factors such as font fluency, judgments from speech are not only based on its content but also biased by the superficial vehicle through which it is delivered. Such effects may become more relevant as daily communication via videoconferencing becomes increasingly widespread.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".