The neonate brain detects speech structure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it