The conceptualization, measurement, and critical appraisal of computational models of anhedonia in depression
Bibliographic record
Abstract
Anhedonia, a cardinal feature of depressive disorders, is classically defined as an inability to experience pleasure, but modern definitions also encompass deficits in anticipatory pleasure and motivation. Validated scales and behavioural tasks have evolved alongside these conceptual shifts, and when integrated with computational modelling, may help reveal mechanisms underlying anhedonia. Reinforcement learning (RL) is the dominant computational framework for studying anhedonia, but fails to fully capture anticipation and motivation. We reviewed the operationalization and measurement of anhedonia from a historical perspective, and conducted a scoping review and critical appraisal of 19 computational models using a structured appraisal guide to assess face, construct and predictive validity. We focussed on generative models (i.e., models that can simulate behavioural data) that were paired with measures of anhedonia in both clinical and non-clinical samples. Model types include RL models, RL integrated with functional magnetic resonance imaging, and models of decision making, effort expenditure, and selective attention. Our review suggests that anhedonia-related deficits span not only reward processing, but also executive and sensory processing. Models generally demonstrated face validity, lacked predictive validity, and showed construct validity for cognitive-behavioural, but not neurobiological, domains. We propose an integrative, systems neuroscience-inspired approach, which aligns with multidimensional definitions of anhedonia.
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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 | 0.046 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| 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".