Familiarity modulates neural tracking of sung and spoken utterances
Bibliographic record
Abstract
Music is often described in the laboratory and in the classroom as a beneficial tool for memory encoding and retention, with a particularly strong effect when words are sung to familiar compared to unfamiliar melodies. However, the neural mechanisms underlying this memory benefit, especially for benefits related to familiar music are not well understood. The current study examined whether neural tracking of the slow syllable rhythms of speech and song is modulated by melody familiarity. Participants became familiar with twelve novel melodies over four days prior to MEG testing. Neural tracking of the same utterances spoken and sung revealed greater cerebro-acoustic phase coherence for sung compared to spoken utterances, but did not show an effect of familiar melody when stimuli were grouped by their assigned (trained) familiarity. However, when participant's subjective ratings of perceived familiarity were used to group stimuli, a large effect of familiarity was observed. This effect was not specific to song, as it was observed in both sung and spoken utterances. Exploratory analyses revealed some in-session learning of unfamiliar and spoken utterances, with increased neural tracking for untrained stimuli by the end of the MEG testing session. Our results indicate that top-down factors like familiarity are strong modulators of neural tracking for music and language. Participants' neural tracking was related to their perception of familiarity, which was likely driven by a combination of effects from repeated listening, stimulus-specific melodic simplicity, and individual differences. Beyond simply the acoustic features of music, top-down factors built into the music listening experience, like repetition and familiarity, play a large role in the way we attend to and encode information presented in a musical context.
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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 | 0.000 |
| 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".