Researching Hemispheric Specialization in the Control of Final Position Accuracy Using the Transcranial Direct Current Stimulation
Bibliographic record
Abstract
Transcranial Direct Current Simulation (tDCS) can improve or disrupt brain functions and can therefore be used to investigate hemispheric specialization. Accordingly, this study was designed to research hemispheric specialization in the control of final position accuracy by comparing the effects of tDCS on dorsolateral prefrontal cortex (DLPFC) in the right and left hemispheres. Forty-three right-handed male university students (aged 21.34±1.61) volunteered to participate in this study. They were divided into the right DLPFC, left DLPFC, sham, and practice groups, including 12, 11, 9, and 11 participants, respectively. After learning motor skills in two days, the participants practiced final position accuracy in one day. They were asked to move the cursor toward the centers of targets appearing randomly at the top, middle, and bottom on the right side of a monitor as accurately and quickly as they could. At the time of practice, the participants received anodic stimulation in one hemisphere and cathodic stimulation in the other. The results indicated that the left anodic/right cathodic group (left DLPFC) showed the worst performance, which may be caused by the inhibitory effects of cathodic stimulations in the right DLPFC. Therefore, it is predicted that the right hemisphere may have greater specialization in final position accuracy of movement.
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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 | 0.000 |
| 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".