What Are They Learning: a Study of Errors Produced During Behavior Acquisition Utilizing Two Prompting Procedures with a Cat
Bibliographic record
Abstract
Prompting methods are common amongst animal trainers, both novice and experts. However, there is little empirical evidence to demonstrate the strengths or weaknesses of common prompting procedures. The current study assessed the strengths and weaknesses during behavior acquisition of two prompting methods, luring and targeting. Luring placed an edible directly in front of the animal which guided the animal through the desired behavior. Targeting, however used a target, an arbitrary object the animal has been trained to touch, guide behavior. A cat was trained, using each method, to walk around a flower. Walking around the right flower pot was trained using luring and walking around the left flower pot was trained using targeting. After both behaviors were acquired, a delay cue method was designed to transfer stimulus control. Later a combination of a delay cue and prompt fading was used. During acquisition the luring method acquired the behavior of walking around a pot more quickly with consistently fewer errors. During stimulus transfer the cat began independently initiating the behavior earlier with the target trained behavior and produced more correct behaviors after the verbal cue. Luring appeared to produce the faster behavior, but after stimulus transfer it could be concluded that the cat did not learn the desired behavior, but rather following the lure. Both methods could be beneficial in different circumstance, however, given the desired behavior was to walk around a flower pot on cue, targeting would be considered best practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".