Evidence for top‐down metre perception in infancy as shown by primed neural responses to an ambiguous rhythm
Bibliographic record
Abstract
From auditory rhythm patterns, listeners extract the underlying steady beat and perceptually group beats to form metres. While previous studies show infants discriminate different auditory metres, it remains unknown whether they can maintain (imagine) a metrical interpretation of an ambiguous rhythm through top-down processes. We investigated this via electroencephalographic mismatch responses. We primed 6-month-old infants (N = 24) to hear a 6-beat ambiguous rhythm either in duple metre (n = 13) or in triple metre (n = 11) through loudness accents either on every second or every third beat. Periods of priming were inserted before sequences of the ambiguous unaccented rhythm. To elicit mismatch responses, occasional pitch deviants occurred on either beat 4 (strong beat in triple metre; weak in duple) or beat 5 (strong in duple; weak in triple) of the unaccented trials. At frontal left sites, we found a significant interaction between beat and priming group in the predicted direction. Post-hoc analyses showed that mismatch response amplitudes were significantly larger for beat 5 in the duple-primed than triple-primed group (p = .047) and were non-significantly larger for beat 4 in the triple-primed than duple-primed group. Further, amplitudes were generally larger in infants with musically experienced parents. At frontal right sites, mismatch responses were generally larger for those in the duple compared with triple group, which may reflect a processing advantage for duple metre. These results indicate that infants can impose a top-down, internally generated metre on ambiguous auditory rhythms, an ability that would aid early language and music learning.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".