Testing the Role of Dorsal Premotor Cortex in Auditory-Motor Association Learning Using Transcranical Magnetic Stimulation (TMS)
Bibliographic record
Abstract
Interactions between the auditory and the motor systems are critical in music as well as in other domains, such as speech. The premotor cortex, specifically the dorsal premotor cortex (dPMC), seems to play a key role in auditory-motor integration, and in mapping the association between a sound and the movement used to produce it. In the present studies we tested the causal role of the dPMC in learning and applying auditory-motor associations using 1 Hz repetitive Transcranical Magnetic Stimulation (rTMS). In this paradigm, non-musicians learn a set of auditory-motor associations through melody training in two contexts: first when the sound to key-press mapping was in a conventional sequential order (low to high tones mapped onto keys from left to right), and then when it was in a novel scrambled order. Participant's ability to match the four pitches to four computer keys was tested before and after the training. In both experiments, the group that received 1 Hz rTMS over the dPMC showed no significant improvement on the pitch-matching task following training, whereas the control group (who received rTMS to visual cortex) did. Moreover, in Experiment 2 where the pitch-key mapping was novel, rTMS over the dPMC also interfered with learning. These findings suggest that rTMS over dPMC disturbs the formation of auditory-motor associations, especially when the association is novel and must be learned rather explicitly. The present results contribute to a better understanding of the role of dPMC in auditory-motor integration, suggesting a critical role of dPMC in learning the link between an action and its associated sound.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".