Effects of pre‐operant running and sucrose concentration on operant wheel running on a fixed interval schedule of reinforcement
Bibliographic record
Abstract
Prior research proposed that temporal control over the pattern of operant wheel running on a fixed interval (FI) schedule of sucrose reinforcement is a function of automatic reinforcement generated by wheel running and the experimentally arranged sucrose reinforcement. Two experiments were conducted to assess this prediction. In the first experiment, rats ran for different durations (0, 30, 60, and 180 min) prior to a session of operant wheel running on a FI 120-s schedule. In the second experiment, the concentration of sucrose reinforcement on a FI 180-s schedule was varied across values of 0, 5, 15, and 25%. In Experiment 1, as the duration of pre-operant running increased, the postreinforcement pause before initiation of running lengthened while wheel revolutions in the latter part of the FI interval increased. In Experiment 2, wheel revolutions markedly increased then decreased to a plateau early in the FI interval. Neither manipulation increased temporal control of the pattern of wheel running. Instead, results indicate that operant wheel running is regulated by automatic reinforcement generated by wheel activity and an adjunctive pattern of running induced by the temporal presentation of sucrose. Furthermore, the findings question whether the sucrose contingency regulates wheel running as a reinforcing consequence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".