Model-based planning is unaffected by ketamine, antidepressant and internet delivered cognitive behavioural therapy treatments in depression
Bibliographic record
Abstract
Cognitive impairments have been observed in patients with depression. These include deficits in inhibition, shifting, and updating; cognitive processes that are critical for goal-directed control over behavior ('model-based planning'). Nevertheless, results of model-based planning in depression have been mixed. We aimed to address this by taking a within-person approach, examining model-based planning before and after a range of effective treatments for depression. Across two parallel studies, participants completed a two-step reinforcement learning paradigm before and after antidepressant medication, internet-based cognitive behavioral therapy (iCBT) or intravenous (IV) ketamine infusion. In experiment 1, 93 patients with treatment-resistant depression were randomized to a single dose of IV ketamine (0.5 mg/kg) or IV saline (50 mL 0.9% NaCl). In Experiment 2, 781 participants were followed for four weeks of antidepressant (N = 83), or iCBT (N = 611) treatment. N = 87 participants without any psychiatric diagnosis were followed as a control group. In both experiments, depressive symptoms significantly improved in treatment groups compared to their corresponding control groups, but we did not find evidence of changes in model-based planning. Moreover, we failed to find associations between individual differences in model-based planning and differential response to ketamine, iCBT or antidepressant treatments. Individual differences in model-based planning at baseline were associated with compulsivity, but not with depression symptoms. These findings suggest that model-based planning is not necessarily compromised in depression and does not improve following treatments. This result provides evidence for the trait-like nature of model-based planning and underscores the specificity of its relation to disorders of compulsivity.
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 | 0.002 |
| 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) | 0.001 | 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".