Stimuli, Responses and State Dependence: Occasion Setting as a General Mechanism of Associative Control
Bibliographic record
Abstract
Associative learning is a powerful learning mechanism that encodes the predictive relationship between stimuli (and responses) and outcomes in the environment.But sometimes the same stimulus can predict different outcomes depending on the context in which it is encountered.For example, word meaning can be conceptualized as an association between an item and its verbal label.For a bilingual person a newspaper, for example, has different labels depending on the language that is being spoken-newspaper, periódico, shimbun, and so on.Occasion setting is the mechanism that allows us to select the object's name in the language we are speaking-or more generally, the appropriate association for the current context.Leising et al. highlight many procedures in which occasion setting might play a role, and attempt to identify a set of diagnostic tests to identify it, in order to promote wider use of occasion setting.In this commentary I argue that using a less empirical, more theoretical analysis might make the concept of occasion setting accessible to an even wider audience.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".