Occasion Setting, Disjunctive Problem Structures, and the Art of Rationalizing Mistakes
Bibliographic record
Abstract
I endorse the efforts proposed by Leising et al. (2025) to bridge terminological and conceptual gaps within and across disciplines.Occasion setting may indeed represent one of the most universally studied problems in human and nonhuman learning, occurring whenever a learned contingency between two variables depends on the status of a third (explicit or latent) variable.I argue that identifying the (partial) "disjunctive structure" and stimulus representations fundamental to occasion setting allows for recognizing a broader range of relevant tasks and phenomena of theoretical interest in human category learning, operant conditioning, and related fields.This perspective has potential implications for theoretical concepts of error-driven reinforcement learning and may inform investigations into how humans reason about occasions when learned stimulus-outcome contingencies are reinforced or nonreinforced.Such insights could enhance our understanding of behavioral adaptation on a broader scale (e.g., the cognitive processes underlying lying, or rationalization of errors).
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.038 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".