Five-month-old infants' identification of the sources of vocalizations
Bibliographic record
Abstract
Humans speak, monkeys grunt, and ducks quack. How do we come to know which vocalizations animals produce? Here we explore this question by asking whether young infants expect humans, but not other animals, to produce speech, and further, whether infants have similarly restricted expectations about the sources of vocalizations produced by other species. Five-month-old infants matched speech, but not human nonspeech vocalizations, specifically to humans, looking longer at static human faces when human speech was played than when either rhesus monkey or duck calls were played. They also matched monkey calls to monkey faces, looking longer at static rhesus monkey faces when rhesus monkey calls were played than when either human speech or duck calls were played. However, infants failed to match duck vocalizations to duck faces, even though infants likely have more experience with ducks than monkeys. Results show that by 5 months of age, human infants generate expectations about the sources of some vocalizations, mapping human faces to speech and rhesus faces to rhesus calls. Infants' matching capacity does not appear to be based on a simple associative mechanism or restricted to their specific experiences. We discuss these findings in terms of how infants may achieve such competence, as well as its specificity and relevance to acquiring language.
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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 | 0.000 |
| 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".