Anxious gambling: Anxiety is associated with higher frontal midline theta predicting less risky decisions
Bibliographic record
Abstract
In this study, we address the effect of anxiety measured with the State-Trait Anxiety Inventory (STAI) on EEG and risk decisions. We selected 20 high and 20 low anxious participants based on their STAI trait scores in the upper or lower quartile of the norm distribution and implemented a risk game developed in our laboratory. We investigate if high anxious individuals exert more cognitive control, reflected in higher frontal midline theta (FMT) power when they make a risky decision, and if they act less risky compared to low anxious individuals. Participants played a risk game while we recorded their brain responses via EEG. High anxious participants played less risky compared to low anxious participants. Further, high anxious participants showed higher FMT power immediately before they chose one of two risk options, suggesting higher cognitive control during the decision time compared to low anxious participants. Via a mediation analysis, we show that the effect of anxiety on risk behavior is fully mediated by FMT power. Further, questionnaire responses revealed that high anxious participants rated risk situations as riskier compared to low anxious participants. We conclude that anxious individuals perceive risky situations as riskier and thus exert more cognitive control during their risk choices, reflected in higher FMT power, which leads to less risky decisions.
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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 | 0.002 |
| 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 | 0.001 | 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".