Investigating Generalizability of Top–Down Neural Representation of Meter in Infancy
Bibliographic record
Abstract
Music and speech rhythms are hierarchically organized, including grouping beats to create metrical structures. Previously, we showed that infants can be primed via loudness accents to interpret a metrically ambiguous (unaccented) rhythm either in duple meter (groupings of 2 beats) or in triple meter (groupings of 3 beats), as measured by larger mismatch responses (MMRs) in electroencephalographic recordings for the perceptually strong compared with weak beat in the unaccented rhythm [Flaten, E., Marshall, S. A., Dittrich, A., & Trainor, L. J. Evidence for top-down meter perception in infancy as shown by primed neural responses to an ambiguous rhythm. European Journal of Neuroscience, 55, 2003-2023, 2022]. Given that infants primed with a duple or triple metrical interpretation heard the same ambiguous stimulus at test, this indicated top-down meter perception. The effects were stronger in the duple-primed infants, although this may have reflected that the stimulus was also slightly biased toward the duple meter. Here, we investigated the generalizability of 6-month-old infants' top-down meter processing by varying the tempo of the rhythm from priming to test. We also used an isochronous test rhythm to ensure there was no duple or triple bias in the stimulus. Results showed that infants' MMRs were not enhanced for deviants on primed strong versus weak beat positions; however, infants taking regular music classes who were primed with triple meter showed a larger MMR for beat 5 (strong beat for duple) than beat 4. Furthermore, duple-primed infants tracked the rhythm more strongly than triple-primed infants, as shown by steady-state evoked potentials. These results suggest that, although infants did not show evidence of generalizing metrical priming across varying tempi, a bias for duple metrical interpretation develops early and may be accelerated by participation in music classes.
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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".