Prosody guides the rapid mapping of auditory word forms onto visual objects in 6-mo-old infants
Bibliographic record
Abstract
Human infants are predisposed to rapidly acquire their native language. The nature of these predispositions is poorly understood, but is crucial to our understanding of how infants unpack their speech input to recover the fundamental word-like units, assign them referential roles, and acquire the rules that govern their organization. Previous researchers have demonstrated the role of general distributional computations in prelinguistic infants' parsing of continuous speech. We extend these findings to more naturalistic conditions, and find that 6-mo-old infants can simultaneously segment a nonce auditory word form from prosodically organized continuous speech and associate it to a visual referent. Crucially, however, this mapping occurs only when the word form is aligned with a prosodic phrase boundary. Our findings suggest that infants are predisposed very early in life to hypothesize that words are aligned with prosodic phrase boundaries, thus facilitating the word learning process. Further, and somewhat paradoxically, we observed successful learning in a more complex context than previously studied, suggesting that learning is enhanced when the language input is well matched to the learner's expectations.
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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 | 0.001 |
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".