Failure to replicate enhancement of speech adaptation using tDCS over motor cortex and cerebellum
Bibliographic record
Abstract
Transcranial direct current stimulation (tDCS) modulates cortical excitability and when applied in combination with a cognitive task has potential to enhance performance. In the speech domain, previous work indicated that anodal tDCS over left motor cortex and right cerebellum increased the magnitude of speech adaptation during sensorimotor learning. Here, we aimed to replicate these findings in a pre-registered, double-blind, randomised, sham-controlled study of a large sample (three groups of N = 30). Participants read words out loud. Speech was recorded and fed back to them either normally or with a 110-Mel increase in the frequency of the first vowel formant. Participants responded to altered feedback by changing their speech production (adaptation). Participants were randomly allocated to receive 2-mA anodal tDCS over either left speech motor cortex, or right cerebellum, or sham stimulation. We tested for differences in speech adaptation among the three groups using one-way analyses of variance. We also explored the relationship between speech adaptation and measures of speech perception. All groups showed significant adaptation while receiving altered auditory feedback. Contrary to the previous study, we found no impact of anodal tDCS on the magnitude of the speech adaptation. In conclusion, speech adaptation was unaffected by anodal tDCS over speech motor cortex or cerebellum. This study is another example of the inconsistent effects of tDCS on task performance particularly when participants are young and healthy. Even larger samples may be needed to detect small effects and to avoid spurious results arising from individual differences in task performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".