On the statistical properties of operant settings and their contribution to the evaluation of sensitivity to reinforcement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When using the matching law in applied settings, a recurring problem is to assess when subjects adjust their responses as a function of their associated reinforcers. Specifically, the main concern is to determine whether subjects’ behavior are sensitive to reinforcement or not. Many researchers have followed (explicitly or implicitly) the criterion that 50% of explained variance is deemed acceptable to consider the subject sensitive. However, it is neither theoretically nor empirically grounded. This article presents a null hypothesis statistical test to assess whether an organism’s behavior is sensitive to reinforcement as quantitatively expressed by the matching law. We first introduce the motivation as to why such a test is warranted, formally described the basis of the model used to compute the null hypothesis and then show some of its advantages. We conclude the article with a hypothetical example.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it