Functional changes in brain organization after <i>de novo</i> audiomotor learning: An fMRI investigation
Bibliographic record
Abstract
To learn to talk or play a musical instrument, the brain must acquire a “mapping” of the relationship between movements and their acoustical consequences. However, it remains unclear how this type of audiomotor learning alters the functional organization of the brain, and what neural networks support consolidation of that learning. This study used functional magnetic resonance imaging (fMRI) to measure functional changes in brain organization as a result of audiomotor learning from scratch. We used a novel paradigm in which subjects learned to move a joystick in different directions in a 2D workspace to achieve speech-like acoustical targets. At the end of each movement, subjects received auditory feedback corresponding to the sound associated with the direction that they moved in. Before and after training, we used resting-state fMRI to assess learning-related changes in functional connectivity. Behavioral results show that, over the course of practice with feedback, subjects gradually produced joystick movements with fewer errors, indicative of learning. Furthermore, some of this learning was maintained to the second day, indicating subjects started forming durable performance gains. Analyses of the fMRI data are currently underway and aimed at localizing changes in neural activity that scale with behavioral indices of audiomotor learning.
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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 | 0.000 |
| 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".