Pavlovian and Instrumental Conditioning and Their Mutual Influence in Major Depressive Disorder: The Mood and Emotional Learning Dynamics (MELD) Framework
Bibliographic record
Abstract
Major Depressive Disorder (MDD) poses a significant burden for individuals and society. It compromises quality of life, disrupts productivity, and strains social relationships. Pavlovian and instrumental conditioning play an important role in psychopathology, yet the independent and joint contributions of these forms of emotional learning to depressed mood are not well understood. In particular, studies of Pavlovian conditioning and Pavlovian-instrumental transfer (PIT) in MDD are few. Studying PIT in MDD may improve our understanding of how emotional learning influences depressive symptoms that is more reflective of how Pavlovian and instrumental conditioning operate in the real world. In this review, we first provide a narrative summary of past research investigating emotional learning in MDD, including computational modeling approaches. Based on these findings, we then build on an existing theoretical framework characterizing emotional learning and decision making in depression by incorporating the potential influence of Pavlovian conditioned cues on instrumental behaviour in individuals experiencing depressed mood. Specifically, we propose that, in MDD, adaptive instrumental behaviours are interrupted by the exaggerated inhibitory influence of aversive Pavlovian cues and insufficiently invigorated by appetitive Pavlovian cues. We conclude with recommendations for future research investigating emotional learning in MDD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".