Occasion Setting, Disjunctive Problem Structures, and the Art of Rationalizing Mistakes
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
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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