Intact modulation of response vigor in major depressive disorder
Bibliographic record
Abstract
Abstract Blunted motivation is a core symptom of Major Depressive Disorder (MDD). Although the empirical picture is mixed, cognitive processes that can be collectively referred to as reward processing have been found to be consistently muted in MDD; most notably, reward sensitivity and reinforcement learning. Works on the modulation of response vigor in individuals with MDD have examined various types of reward, but recent research has shown that in the general population, response vigor is not modulated by type of reward on tasks that are highly similar to those used in these experiments. The present study implemented a form of non-reward related reinforcement which has repeatedly been shown to modulate response vigor in the general population. It investigated whether modulation of response vigor by this type of reinforcement would be effective in individuals with MDD. Clinically depressed individuals (N = 121; 76 post-exclusion) engaged in a task in which their responses led to predictable and immediate sensorimotor effects, or no such effects. Response vigor increased when responses led to sensorimotor effects, which was comparable to the increase found in the general population. These findings support the utility of isolating the computations leading to different reinforcement types and suggest that motivational deficits in MDD may be specific to the type of reward (i.e., hedonically or otherwise explicitly desired stimuli). These results contribute to the literature by suggesting that the reinforcement from sensorimotor predictability stems from processes devolved to motor control, whereas reinforcement from rewards may depend on more general-purpose processes.
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 | 0.001 |
| 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".