EXCLUSIVE PREFERENCE DEVELOPS LESS READILY ON CONCURRENT RATIO SCHEDULES WITH WHEEL‐RUNNING THAN WITH SUCROSE REINFORCEMENT
Bibliographic record
Abstract
Previous research suggested that allocation of responses on concurrent schedules of wheel-running reinforcement was less sensitive to schedule differences than typically observed with more conventional reinforcers. To assess this possibility, 16 female Long Evans rats were exposed to concurrent FR FR schedules of reinforcement and the schedule value on one alternative was systematically increased. In one condition, the reinforcer on both alternatives was .1 ml of 7.5% sucrose solution; in the other, it was a 30-s opportunity to run in a wheel. Results showed that the average ratio at which greater than 90% of responses were allocated to the unchanged alternative was higher with wheel-running reinforcement. As the ratio requirement was initially increased, responding strongly shifted toward the unchanged alternative with sucrose, but not with wheel running. Instead, responding initially increased on both alternatives, then subsequently shifted toward the unchanged alternative. Furthermore, changeover responses as a percentage of total responses decreased with sucrose, but not wheel-running reinforcement. Finally, for some animals, responding on the increasing ratio alternative decreased as the ratio requirement increased, but then stopped and did not decline with further increments. The implications of these results for theories of choice are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".