Activation of Prefrontal Cortex by Transcranial Direct Current Stimulation Reduces Appetite for Risk during Ambiguous Decision Making
Bibliographic record
Abstract
As adult humans, we are continuously faced with decisions in which proper weighing of the risk involved is critical. Excessively risky or overly cautious decision making can both have disastrous real-world consequences. Weighing of risks and benefits toward decision making involves a complex neural network that includes the dorsolateral prefrontal cortex (DLPFC), but its role remains unclear. Repetitive transcranial magnetic stimulation studies have shown that disruption of the DLPFC increases risk-taking behavior. Transcranial direct current stimulation (tDCS) allows upregulation of activity in the DLPFC, and we predicted that it might promote more cautious decision making. Healthy participants received one of the following treatments while they performed the Balloon Analog Risk Task: (1) right anodal/left cathodal DLPFC tDCS, (2) left anodal/right cathodal DLPFC tDCS, or (3) sham tDCS. This experiment revealed that participants receiving either one of the bilateral DLPFC tDCS strategies adopted a risk-averse response style. In a control experiment, we tested whether unilateral DLPFC stimulation (anodal tDCS over the right or left DLPFC with the cathodal electrode over the contralateral supraorbital area) was sufficient to decrease risk-taking behaviors. This experiment showed no difference in decision-making behaviors between the groups of unilateral DLPFC stimulation and sham stimulation. These findings extend the notion that DLPFC activity is critical for adaptive decision making, possibly by suppressing riskier responses. Anodal tDCS over DLPFC by itself did not significantly change risk-taking behaviors; however, when the contralateral DLPFC was modulated with cathodal tCDS, an important decrease in risk taking was observed. Also, the induced cautious decision-making behavior was observed only when activity of both DLPFCs was modulated. The ability to modify risk-taking behavior may be translated into therapeutic interventions for disorders such as drug abuse, overeating, or pathological gambling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".