Induced Worry Increases Risk Aversion in Patients with Generalized Anxiety
Bibliographic record
Abstract
Background: Anxiety disorders are characterized by disruptions in decision-making which contribute to daily life impairment. Notable is an enhanced aversion to uncertain decision outcomes (i.e., risk aversion), which is not specific to negative outcomes (i.e., no loss aversion). This uncertainty bias could be a trait-like causal factor contributing to anxiety symptoms, or a state-like feature triggered by anxiety symptoms, such as by worry chains in Generalized Anxiety. Disentangling these possibilities could contribute to a better understanding of anxiety disorders and ultimately inform treatment strategies.Methods: In-patients diagnosed with Major Depression Disorder and comorbid with (N = 16) or without (N = 24) Generalized Anxiety symptoms, as well as age- and gender-matched healthy controls (N = 23), completed an economic decision-making task at baseline and after worry induction. They had to decide, repeatedly, between a certain monetary payoff, and an uncertain gamble, allowing for estimation of their risk and loss aversion in a computational prospect-theoretic model.Results: Risk and loss aversion were similar between the three groups before worry induction. After worry induction, risk aversion, but not loss aversion, was enhanced in patients with generalized anxiety, compared to non-anxious patients or healthy controls. This difference was primarily explained by anxiety symptom scores.Conclusions: Our findings suggest that decision-making disruptions in anxiety disorder may be driven by anxiety symptoms such as worry, rather than causing them. This could inspire etiological models, motivate standardization of emotional state in research on decision-making in anxiety disorders, and inform treatment strategies directly aimed at reducing worry.
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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 | 0.000 | 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".