Investigating disorder-specific and transdiagnostic alterations in model-based and model-free decision-making
Bibliographic record
Abstract
Background Decision-making alterations are present in psychiatric illnesses like major depressive disorder (MDD), obsessive–compulsive disorder (OCD), and schizophrenia, linked to symptoms of the respective disorders. We sought to analyze unique and shared decision-making alterations in these disorders, which is crucial for early diagnosis and treatment, especially given potential comorbidities. Methods Using 2 computational modelling approaches — logistic regression and hierarchical Bayesian modelling — we analyzed alterations in model-based and model-free decision-making in a transdiagnostic cohort of patients with MDD, OCD, or schizophrenia. Our aim was to identify disorder-specific and shared alterations and their associations with symptoms. Results We included 23 patients with MDD, 25 patients with OCD, 27 patients with schizophrenia, and 25 controls. Overall, participants of all groups relied on model-free decision-making. Patients with schizophrenia had the lowest learning rate and highest switching rate, indicating low perseverance. Furthermore, patients with OCD were more random in both task stages than controls and patients with MDD. All patient groups exhibited more randomness in responses than controls, with the schizophrenia group showing the highest levels. Increased model-free behaviour correlated with elevated depressive symptoms, and more model-based decision-making was linked to lower anhedonia levels across all patient groups. Limitations The sample size in each group was small. Conclusion This study highlights disorder-specific and shared decision-making alterations among people with MDD, OCD, or schizophrenia. Our findings suggest that anhedonia and depressive symptoms, which are present in all 3 disorders, share underlying behavioural mechanisms. Improving model-based behaviour may be a target for intervention and treatment. Furthermore, completely random behaviour in the 2-step task appears to distinctly differentiate patients with schizophrenia in remission.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".