Translational tests involving non-reward: methodological considerations
Bibliographic record
Abstract
This review is concerned with methods for assessing the processing of unrewarded responses in experimental animals and the mechanisms underlying performance of these tasks. A number of clinical populations, including Parkinson's disease, depression, compulsive disorders, and schizophrenia demonstrate either abnormal processing or learning from non-rewarded responses in laboratory-based reinforcement learning tasks. These effects are hypothesized to result from disturbances in modulatory neurotransmitter systems, including dopamine and serotonin. Parallel work in experimental animals has revealed consistent behavioral patterns associated with non-reward and, consistent with the human literature, modulatory roles for specific neurotransmitters. Classical tests involving an important reward omission component include appetitive extinction, ratio schedules of responding, reversal learning, and delay and probability discounting procedures. In addition, innovative behavioral tests have recently been developed leverage probabilistic feedback to specifically assay accommodation of, and learning from, non-rewarded responses. These procedures will be described and reviewed with discussion of the behavioral and neural determinants of performance. A final section focusses specifically on the benefits of trial-by-trial analysis of responding during such tasks, and the implications of such analyses for the translation of findings to clinical studies.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".