The Effects of Training History on Retention and Reacquisition of Stimulus Control
Bibliographic record
Abstract
The purpose of this experiment was to study the effects of training history on retention and re-acquisition of stimulus control of previously learned behaviors. In Phase I, two pairs of behaviors were alternately trained. Circle and touch behaviors were trained concurrently until two consecutive errorless sessions were run. Spin and down behaviors were trained together in the same manner. Probe sessions, in which all four cues were presented, were conducted each time a pair of behaviors reached this criterion. Training of one pair did not occur until the other pair had reached criterion and probe sessions were run. Despite achieving the designated criterion during training, stimulus control changed during probes. During probe sessions, errors increased under the cues that were not currently being trained. In most cases, the type of errors emitted for each cue was the same as the behavior that was trained concurrently. The number of training sessions required to reach criterion accuracy was high during the first set of sessions and decreased over the course of the experiment. In Phase II, spin and circle behaviors were trained concurrently. The number of sessions required to reach stimulus control criteria remained low, and the number of errors emitted under the spin and circle cues during probe sessions decreased. However, the number of errors increased under the touch cue. In Phase III, a reinforce-all procedure was used instead of extinction to test stimulus control. The highest frequency of errors occurred under the touch cue, but the down error was almost exclusively emitted under every cue during the last several sessions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".