Auditory‐Somatosensory Integration and Cortical Plasticity in Musical Training
Bibliographic record
Abstract
Learning to play a musical instrument requires complex multimodal skills involving simultaneous perception of several sensory modalities: auditory, visual, and somatosensory as well as the motor system. Musical training thus provides an adequate neuroscientific model to study multimodal integration and plasticity in musical training. The aim of this study was to investigate the impact of short-term uni- and multimodal musical training on auditory-somatosensory integration and plasticity. Two groups of nonmusicians were musically trained. The first group (sensorimotor-auditory group, SA) learned to play a musical sequence on the piano, whereas the second one (auditory group, A) actively listened to and made judgments about the correctness of the music. The training-induced cortical plasticity effect was assessed by recording musically elicited mismatch negativity (MMN) from magnetoencephalographic (MEG) measurements before and after training. The SA group showed significant enlargement of MMN after training compared to the A group, reflecting greater enhancement of musical representations in auditory cortex after sensorimotor-auditory training compared to mere auditory training. This study demonstrates that the sensorimotor and auditory systems integrate and that this multimodal training causes cortical reorganizational changes in the auditory cortex over and above the changes introduced by auditory training alone.
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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".