Modulating mental state recognition by anodal tDCS over the cerebellum
Bibliographic record
Abstract
Increasing evidence from neuroimaging and clinical studies has demonstrated cerebellar involvement in social cognition components, including the mentalizing process. The aim of this study was to apply transcranial direct current stimulation (tDCS) to modulate cerebellar excitability to investigate the role the cerebellum plays in mental state recognition. Forty-eight healthy subjects were randomly assigned to different groups in which anodal, cathodal, or sham tDCS (2 mA for 20 min) was delivered centering the electrode on the vermis to stimulate the posterior portion of the cerebellum. The ability to attribute mental states to others was tested before and after tDCS using a digital version of the 'Reading the Mind in the Eyes test', which includes visual perceptive and motor stimuli as control conditions. Correct response and reaction times (RTs) were recorded. The results revealed a significant reduction in RTs between the baseline and post-stimulation sessions after cerebellar anodal tDCS only for mental state stimuli (Wilcoxon test p = 0.00055), whereas no significant effect was found in the cathodal or sham conditions or for visual perceptive and motor stimuli. Overall, our study suggests that cerebellar anodal tDCS might selectively improve mental state recognition and constitute an effective strategy to positively modulate the mentalizing process.
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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".