Reinforcement of a reinforcing behaviour: Effect of sucrose concentration on wheel-running rate
Bibliographic record
Abstract
Wheel running, unlike typical operant behavior, generates its own automatic reinforcement that alters the control exerted by extrinsic reinforcement on wheel running. The current study investigated the implications of the automatic reinforcement of wheel running by arranging different sucrose concentrations as extrinsic reinforcement for operant wheel running in ad-lib fed and food-deprived rats. Eleven female Long Evans rats ran on fixed revolution 30 schedules that delivered a drop of sucrose solution as reinforcement. Sucrose concentration varied across values of 0%, 2.5%, 5%, 10%, and 15% sucrose (w/v). Results showed that under ad-lib feeding, only the highest concentrations increased operant wheel-running rate. By contrast, under deprivation, all concentrations of sucrose increased the rate of wheel running. Despite the differences in sucrose-reinforced operant wheel-running rates by deprivation level (ad lib vs. deprived), wheel-running rates did not differ at the highest concentrations. Prior research on operant lever pressing, a response generating low (or no) automatic reinforcement, has shown considerably higher lever-pressing rates as a function of increasing amounts of sucrose reinforcement when rats are food deprived. Together, these previous observations and the current study suggest that automatic reinforcement generated by an operant decreases the control exerted by extrinsic reinforcement. Additionally, the regulation by extrinsic reinforcement on automatically reinforcing behavior depends on the organism’s motivation or deprivation level (ad lib vs. deprived).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".