The neonate brain detects speech structure
Bibliographic record
Abstract
What are the origins of the efficient language learning abilities that allow humans to acquire their mother tongue in just a few years very early in life? Although previous studies have identified different mechanisms underlying the acquisition of auditory and speech patterns in older infants and adults, the earliest sensitivities remain unexplored. To address this issue, we investigated the ability of newborns to learn simple repetition-based structures in two optical brain-imaging experiments. In the first experiment, 22 neonates listened to syllable sequences containing immediate repetitions (ABB; e.g., "mubaba," "penana"), intermixed with random control sequences (ABC; e.g., "mubage," "penaku"). We found increased responses to the repetition sequences in the temporal and left frontal areas, indicating that the newborn brain differentiated the two patterns. The repetition sequences evoked greater activation than the random sequences during the first few trials, suggesting the presence of an automatic perceptual mechanism to detect repetitions. In addition, over the subsequent trials, activation increased further in response to the repetition sequences but not in response to the random sequences, indicating that recognition of the ABB pattern was enhanced by repeated exposure. In the second experiment, in which nonadjacent repetitions (ABA; e.g., "bamuba," "napena") were contrasted with the same random controls, no discrimination was observed. These findings suggest that newborns are sensitive to certain input configurations in the auditory domain, a perceptual ability that might facilitate later language development.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".