Yes, We Are Studying Occasion Setting: A Configural Complement to Leising et al.
Bibliographic record
Abstract
The article "Are You Studying Occasion Setting?A Review for Inquiring Minds" offers a valuable and comprehensive look at how stimuli can influence or "set the occasion" for responding to another cue, organizing its discussion around four principal experimental tests.By distinguishing direct (excitatory or inhibitory) stimulus control from a more indirect, hierarchical form of stimulus modulation, Leising et al. (2025) make a strong case for why occasion setting warrants further study.Although they acknowledge both hierarchical-modulatory and associative-configural approaches, the article's emphasis on hierarchical terminology may inadvertently suggest that purely associative (configural) theories have less explanatory power.This focus can overshadow the potential theoretical and empirical contributions of configural models.With this commentary, we emphasize the strengths of so-called configural explanations and illustrate how they address the same core tasks, drawing on principles from Wagner's SOP with Replaced Elements (SOP-REM) model.Our hope is that this complementary view will further enrich the discussion on occasion setting and demonstrate the versatility of associative frameworks in explaining complex cue-modulation phenomena.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".