Facilitation of choice reaction time following transcranial direct current stimulation (tDCS) applied over contralateral motor cortex
Bibliographic record
Abstract
Greater excitability in primary motor cortex (M1) is associated with increased preparatory activity in simple reaction time (RT) tasks. While evidence suggests that up-regulating cortical excitability using tDCS can facilitate simple RT, it remains unclear if similar RT facilitation can occur in choice RT as limited preparation occurs when the response is unknown in advance. The current experiment investigated whether increasing M1 excitability would lead to a decrease in choice RT for the limb contralateral to the stimulation. Additionally, it was hypothesized that if lateralized M1 excitability is related to response selection probability, the limb contralateral to the stimulation would be chosen more often during a free-choice task. Participants performed a choice RT task requiring either a 20? right or left wrist extension upon illumination of an associated stimulus (right or left box, respectively). In 20% of trials the central fixation illuminated, corresponding to a free-choice of either movement. Participants completed pre- and post-tDCS RT blocks of 100 trials. Between blocks, tDCS (1mA*10 min) was delivered over either the right or left motor representation for the wrist. Results showed that post-tDCS RTs were faster for only the contralateral hand in both forced-choice and free-choice trials (p’s<.05). Moreover, no change was observed in the proportion of contralateral hand responses on free-choice trials following tDCS. Together, these results suggest that M1 excitability is related to response initiation speed even when a choice is required. Furthermore, a larger facilitation of free-choice RT may also indicate that response selection processing was affected by tDCS.Acknowledgments: Supported by NSERC
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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) | 0.002 | 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".