The Effects of Increasing Rates of Reinforcement Through an Alternative Fluent Behavior on the Acquisition and Extinction of Behavior in Dogs
Bibliographic record
Abstract
The purpose of the present study was to experimentally investigate the effects of interspersing the opportunity to perform a fluent behavior during the acquisition of a new behavior. The experimenter trained left and right paw movements in domestic canines using a multiple treatment design. One paw movement was trained with a typical shaping procedure while the other was trained with an opportunity to perform a fluent behavior, touching the dog’s nose to a plastic disc, following each successive approximation in the shaping procedure. Two extinction phases were implemented during the experiment. The results showed that higher rates of reinforcement were achieved primarily following changes in criteria for reinforcement for the behavior in acquisition. There were no effects on rate of acquisition of the behavior, but adding an alternative fluent behavior may have slowed the differentiation between the reinforced behavior and alternative behaviors for one dog. The behavior trained with the addition of an alternative fluent behavior extinguished more quickly than in the control condition and extinguished at similar rates to the opposite leg movement. This suggests that the technique of offering an alternative fluent behavior may facilitate the chaining of the opposite behavior with the behavior targeted for reinforcement.
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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 | 0.000 | 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".