Assessing the Impact of Transcranial Magnetic Stimulation on Speech Perception in Noise
Bibliographic record
Abstract
Healthy aging is associated with reduced speech perception in noise (SPiN) abilities. The etiology of these difficulties remains elusive, which prevents the development of new strategies to optimize the speech processing network and reduce these difficulties. The objective of this study was to determine if sublexical SPiN performance can be enhanced by applying TMS to three regions involved in processing speech: the left posterior temporal sulcus, the left superior temporal gyrus, and the left ventral premotor cortex. The second objective was to assess the impact of several factors (age, baseline performance, target, brain structure, and activity) on post-TMS SPiN improvement. The results revealed that participants with lower baseline performance were more likely to improve. Moreover, in older adults, cortical thickness within the target areas was negatively associated with performance improvement, whereas this association was null in younger individuals. No differences between the targets were found. This study suggests that TMS can modulate sublexical SPiN performance, but that the strength and direction of the effects depend on a complex combination of contextual and individual factors.
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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".