Using Progressive Ratio Schedules to Evaluate Edible, Leisure, and Token Reinforcement
Bibliographic record
Abstract
The general purpose of the current study was to evaluate the potency of different categories of reinforcers with young children diagnosed with developmental delays. The participants were two boys and one girl who were between the ages of seven and eight. In Phase 1, we evaluated the reinforcing potency of tokens, edible items, and leisure items by using a progressive ratio (PR) schedule. For two participants, we found that tokens resulted in the highest PR break points. For one participant, edibles resulted in the highest break points (tokens were found to have the lowest break points). In Phase 2, we evaluated the effects of presession access on the break points of edibles and tokens. This manipulation served as a preliminary analysis of the extent to which tokens might function as generalized conditioned reinforcers. During Phase 2, presession access altered the break points of edibles, but not tokens. The findings of the current study suggest that PR schedules may be useful as a means to better assess certain dimensions of tasks and how they affect reinforcer effectiveness (e.g., amount of effort the client is willing to exert, the duration at which the client willing to work, how many responses the client will emit, etc.), and to evaluate to what extent tokens actually function as generalized conditioned reinforcers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".