Sensorimotor Learning Enhances Expectations During Auditory Perception
Bibliographic record
Abstract
Sounds that have been produced with one's own motor system tend to be remembered better than sounds that have only been perceived, suggesting a role of motor information in memory for auditory stimuli. To address potential contributions of the motor network to the recognition of previously produced sounds, we used event-related potential, electric current density, and behavioral measures to investigate memory for produced and perceived melodies. Musicians performed or listened to novel melodies, and then heard the melodies either in their original version or with single pitch alterations. Production learning enhanced subsequent recognition accuracy and increased amplitudes of N200, P300, and N400 responses to pitch alterations. Premotor and supplementary motor regions showed greater current density during the initial detection of alterations in previously produced melodies than in previously perceived melodies, associated with the N200. Primary motor cortex was more strongly engaged by alterations in previously produced melodies within the P300 and N400 timeframes. Motor memory traces may therefore interface with auditory pitch percepts in premotor regions as early as 200 ms following perceived pitch onsets. Outcomes suggest that auditory-motor interactions contribute to memory benefits conferred by production experience, and support a role of motor prediction mechanisms in the production effect.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".