Repetitive Gamma-tACS Improves the Reaction Times of Healthy Young Adults in a Visuospatial Working Memory Task: A Randomized Study
Bibliographic record
Abstract
Objective: The aims of the study were to test the short-term and long-term efficacy of repetitive γ-tACS over the left DLPFC to improve visuospatial working memory performance in the spatial capacity delayed response task (SCDRT). Methods: In a single blind placebo-controlled study, 35 healthy young adults were randomly assigned to three sessions of either active γ-tACS (n = 18) or passive sham γ-tACS (n = 17) The design allowed us to evaluate the influence of the stimulation protocol (active vs. sham), the stimulation session number (day 1 to 3), the session block (before stimulation, during stimulation and after stimulation) and the VSWM retention load (1, 3, 5 or 7 stimuli) on the response speed and accuracy. Results: Active γ-tACS selectively improved VSWM performance on day 2 and 3, and the effect was greater following stimulation rather than during stimulation. Significant effects were seen concerning response speed but not accuracy. The VSWM performance gains of the active γ-tACS were no longer present in the long-term at a follow-up session after two weeks. Conclusions: The present study provides novel evidence for a selective improvement in VSWM performance with three repeated sessions of γ-tACS in young adults through the entrainment of gamma rhythms in the left DLPFC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".