Exploring infant talker bias: Insights from remote speech perception testing
Bibliographic record
Abstract
Lab studies show that infants (4- to 7-month-olds) prefer to listen to vowels with infant-like f0 and formant frequencies over those of an adult female (Masapollo et al., 2015; Polka et al., 2021). This Infant Talker Bias may facilitate infants’ mapping of articulatory gestures to acoustic correlates. In this study, 4- to 12-month-olds completed a listening preference task on the Lookit online testing platform. Across eight trials, we presented synthesized infant and adult vowel sounds (/i/ and /a/) paired with a simple animation and recorded the infant’s response via the webcam. Infant looking time and vocalization to each vowel type were coded offline. Preliminary analyses (n = 91) show that listening time increased with age (p < 0.05), and all infants listened longer to infant vowels than to adult vowels (p < 0.01). Preliminary analyses (n = 62) also show an increase in infant vocalizations with age (p = 0.00) and a trend towards more and longer vocalizations in response to the adult vowels. These findings replicate and extend the infant talker bias to new vowel stimuli and to older infants, and support the use of remote testing in infant speech perception studies.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".