The neonate brain's sensitivity to repetition‐based structure: Specific to speech?
Bibliographic record
Abstract
Newborns are able to extract and learn repetition-based regularities from the speech input, that is, they show greater brain activation in the bilateral temporal and left inferior frontal regions to trisyllabic pseudowords of the form AAB (e.g., "babamu") than to random ABC sequences (e.g., "bamuge"). Whether this ability is specific to speech or also applies to other auditory stimuli remains unexplored. To investigate this, we tested whether newborns are sensitive to regularities in musical tones. Neonates listened to AAB and ABC tones sequences, while their brain activity was recorded using functional Near-Infrared Spectroscopy (fNIRS). The paradigm, the frequency of occurrence and the distribution of the tones were identical to those of the syllables used in previous studies with speech. We observed a greater inverted (negative) hemodynamic response to AAB than to ABC sequences in the bilateral temporal and fronto-parietal areas. This inverted response was caused by a decrease in response amplitude, attributed to habituation, over the course of the experiment in the left fronto-temporal region for the ABC condition and in the right fronto-temporal region for both conditions. These findings show that newborns' ability to discriminate AAB from ABC sequences is not specific to speech. However, the neural response to musical tones and spoken language is markedly different. Tones gave rise to habituation, whereas speech was shown to trigger increasing responses over the time course of the study. Relatedly, the repetition regularity gave rise to an inverted hemodynamic response when carried by tones, while it was canonical for speech. Thus, newborns' ability to detect repetition is not speech-specific, but it engages distinct brain mechanisms for speech and music. RESEARCH HIGHLIGHTS: The ability of newborns' to detect repetition-based regularities is not specific to speech, but also extends to other auditory modalities. The brain mechanisms underlying speech and music processing are markedly different.
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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 | 0.001 |
| 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".