Measuring Neural Entrainment to Beat and Meter in Infants: Effects of Music Background
Bibliographic record
Abstract
Caregivers often engage in musical interactions with their infants. For example, parents across cultures sing lullabies and playsongs to their infants from birth. Behavioral studies indicate that infants not only extract beat information, but also group these beats into metrical hierarchies by as early as 6 months of age. However, it is not known how this is accomplished in the infant brain. An EEG frequency-tagging approach has been used successfully with adults to measure neural entrainment to auditory rhythms. The current study is the first to use this technique with infants in order to investigate how infants' brains encode rhythms. Furthermore, we examine how infant and parent music background is associated with individual differences in rhythm encoding. In Experiment 1, EEG was recorded while 7-month-old infants listened to an ambiguous rhythmic pattern that could be perceived to be in two different meters. In Experiment 2, EEG was recorded while 15-month-old infants listened to a rhythmic pattern with an unambiguous meter. In both age groups, information about music background (parent music training, infant music classes, hours of music listening) was collected. Both age groups showed clear EEG responses frequency-locked to the rhythms, at frequencies corresponding to both beat and meter. For the younger infants (Experiment 1), the amplitudes at duple meter frequencies were selectively enhanced for infants enrolled in music classes compared to those who had not engaged in such classes. For the older infants (Experiment 2), amplitudes at beat and meter frequencies were larger for infants with musically-trained compared to musically-untrained parents. These results suggest that the frequency-tagging method is sensitive to individual differences in beat and meter processing in infancy and could be used to track developmental changes.
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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".