Audio-Visual Perception of Gender by Infants Emerges Earlier for Adult-Directed Speech
Bibliographic record
Abstract
Early multisensory perceptual experiences shape the abilities of infants to perform socially-relevant visual categorization, such as the extraction of gender, age, and emotion from faces. Here, we investigated whether multisensory perception of gender is influenced by infant-directed (IDS) or adult-directed (ADS) speech. Six-, 9-, and 12-month-old infants saw side-by-side silent video-clips of talking faces (a male and a female) and heard either a soundtrack of a female or a male voice telling a story in IDS or ADS. Infants participated in only one condition, either IDS or ADS. Consistent with earlier work, infants displayed advantages in matching female relative to male faces and voices. Moreover, the new finding that emerged in the current study was that extraction of gender from face and voice was stronger at 6 months with ADS than with IDS, whereas at 9 and 12 months, matching did not differ for IDS versus ADS. The results indicate that the ability to perceive gender in audiovisual speech is influenced by speech manner. Our data suggest that infants may extract multisensory gender information developmentally earlier when looking at adults engaged in conversation with other adults (i.e., ADS) than when adults are directly talking to them (i.e., IDS). Overall, our findings imply that the circumstances of social interaction may shape early multisensory abilities to perceive gender.
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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 | 0.001 |
| 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) | 0.002 | 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".