Perceptual Fluency and Judgments of Vocal Aesthetics and Stereotypicality
Bibliographic record
Abstract
Research has shown that processing dynamics on the perceiver's end determine aesthetic pleasure. Specifically, typical objects, which are processed more fluently, are perceived as more attractive. We extend this notion of perceptual fluency to judgments of vocal aesthetics. Vocal attractiveness has traditionally been examined with respect to sexual dimorphism and the apparent size of a talker, as reconstructed from the acoustic signal, despite evidence that gender-specific speech patterns are learned social behaviors. In this study, we report on a series of three experiments using 60 voices (30 females) to compare the relationship between judgments of vocal attractiveness, stereotypicality, and gender categorization fluency. Our results indicate that attractiveness and stereotypicality are highly correlated for female and male voices. Stereotypicality and categorization fluency were also correlated for male voices, but not female voices. Crucially, stereotypicality and categorization fluency interacted to predict attractiveness, suggesting the role of perceptual fluency is present, but nuanced, in judgments of human voices.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".