Diminishing Risk-Taking Behavior by Modulating Activity in the Prefrontal Cortex: A Direct Current Stimulation Study
Bibliographic record
Abstract
Studies have shown increased risk taking in healthy individuals after low-frequency repetitive transcranial magnetic stimulation, known to transiently suppress cortical excitability, over the right dorsolateral prefrontal cortex (DLPFC). It appears, therefore, plausible that differential modulation of DLPFC activity, increasing the right while decreasing the left, might lead to decreased risk taking, which could hold clinical relevance as excessively risky decision making is observed in clinical populations leading to deleterious consequences. The goal of the present study was to investigate whether risk-taking behaviors could be decreased using concurrent anodal transcranial direct current stimulation (tDCS) of the right DLPFC, which allows upregulation of brain activity, with cathodal tDCS of the left DLPCF, which downregulates activity. Thirty-six healthy volunteers performed the risk task while they received either anodal over the right with cathodal over the left DLPFC, anodal over the left with cathodal over the right DLPFC, or sham stimulation. We hypothesized that right anodal/left cathodal would decrease risk-taking behavior compared with left anodal/right cathodal or sham stimulation. As predicted, during right anodal/left cathodal stimulation over the DLPFC, participants chose more often the safe prospect compared with the other groups. Moreover, these participants appeared to be insensitive to the reward associated with the prospects. These findings support the notion that the interhemispheric balance of activity across the DLPFCs is critical in decision-making behaviors. Most importantly, the observed suppression of risky behaviors suggests that populations with boundless risk-taking behaviors leading to negative real-life consequences, such as individuals with addiction, might benefit from such neuromodulation-based approaches.
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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".