Stimulus Predictability and Liking Enhance Auditory–Motor Encoding and Memory for Melodies
Bibliographic record
Abstract
The ability to predict or anticipate musical events contributes to music-related pleasure and memory; however, their contributions to learning to play a melody have been less well-explored. In this study, we investigated how musical predictability and pleasure influenced how nonmusicians learned to play short melodies, as well as post-learning recall. Participants listened to and rated perceived pleasure for melodies that varied in predictability while we measured pupil dilation as an index of attention and arousal. Participants then learned to play the ending of each melody. We found that pupil dilation during listening was sensitive to musical predictability and liking ratings, with pupil size increasing for more liked and more predictable melodies. During the motor learning task, participants' asynchrony scores were modulated by liking and predictability: More predictable melodies resulted in lower asynchrony overall, whereas more liked melodies elicited steeper learning slopes. During recall, more predictable melodies were better recognized. Finally, individuals with better recognition performance also showed greater pupil dilation during the initial listening, along with steeper motor learning slopes. Altogether, these findings indicate that arousal is linked to predictability and pleasure, and that all three factors are related to auditory encoding, motor learning, and explicit recognition of musical stimuli.
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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) | 0.002 | 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".