Inhibitory effect of tDCS on auditory evoked response: Simultaneous MEG-tDCS reveals causal role of right auditory cortex in pitch learning
Bibliographic record
Abstract
A body of literature has demonstrated that the right auditory cortex (AC) plays a dominant role in fine pitch processing. However, our understanding is relatively limited about whether this asymmetry extends to perceptual learning of pitch. There is also a lack of causal evidence regarding the role of the right AC in pitch learning. We addressed these points with anodal transcranial direct current stimulation (tDCS), adapting a previous behavioral study in which anodal tDCS over the right AC was shown to block improvement of a microtonal pitch pattern learning task over 3 days. To address the physiological changes associated with tDCS, we recorded MEG data simultaneously with tDCS on the first day, and measured behavioral thresholds on the following two consecutive days. We tested three groups of participants who received anodal tDCS over their right or left AC, or sham tDCS, and measured the N1m auditory evoked response before, during, and after tDCS. Our data show that anodal tDCS of the right AC disrupted pitch discrimination learning up to two days after its application, whereas learning was unaffected by left-AC or sham tDCS. Although tDCS reduced the amplitude of the N1m ipsilaterally to the stimulated hemisphere on both left and right, only right AC N1m amplitude reductions were associated with the degree to which pitch learning was disrupted. This brain-behavior relationship confirms a causal link between right AC physiological responses and fine pitch processing, and provides neurophysiological insight concerning the mechanisms of action of tDCS on the auditory system.
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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) | 0.001 | 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".