Resolving the (Apparent) Talker Recognition Paradox in Developmental Speech Perception
Bibliographic record
Abstract
The infant literature suggests that humans enter the world with impressive built-in talker processing abilities. For example, newborns prefer the sound of their mother's voice over the sound of another woman's voice, and well before their first birthday, infants tune in to language-specific speech cues for distinguishing between unfamiliar talkers. The early childhood literature, however, suggests that preschoolers are unable to learn to identify the voices of two unfamiliar talkers unless these voices are highly distinct from one another, and that adult-level talker recognition does not emerge until children near adolescence. How can we reconcile these apparently paradoxical messages conveyed by the infant and early childhood literatures? Here, we address this question by testing 16.5-month-old infants (N = 80) in three talker recognition experiments. Our results demonstrate that infants at this age have difficulty recognizing unfamiliar talkers, suggesting that talker recognition (associating voices with people) is mastered later in life than talker discrimination (telling voices apart). We conclude that methodological differences across the infant and early childhood literatures-rather than a true developmental discontinuity-account for the performance differences in talker processing between these two age groups. Related findings in other areas of developmental psychology are discussed.
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 | 0.006 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".