Learning to differentiate individuals by their voices: Infants' individuation of native‐ and foreign‐species voices
Bibliographic record
Abstract
The ability to discriminate and identify people by their voice is important for social interaction in humans. In early development, learning to discriminate important differences in a number of socially relevant stimuli, such as phonemes and faces, has been shown to follow a common pattern of experience-driven perceptual narrowing, where the discrimination of native stimuli improves, while the discrimination of foreign stimuli worsens. The aim of the present study was to investigate whether similar perceptual narrowing occurs for discriminating individuals by voice. We tested the ability of English-speaking adults and English-learning 6- and 12-month-olds to discriminate either native-species (human) or foreign-species (rhesus monkey [Macaca mulatta]) individuals by their vocalizations. Between 6 and 12 months of age, the ability to discriminate monkey voices decreased significantly and there was a non-significant trend for improved ability to discriminate human voices. The results support the hypothesis of experience-driven perceptual narrowing for voice individuation during the first year after birth.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".