Occasion Setting in Animal Cognition Research: Some Unaddressed Issues
Bibliographic record
Abstract
Holland and colleagues introduced the concept of occasion setting in the 1980s, revolutionizing Pavlovian conditioning research by highlighting the hierarchical properties of stimulus control.Occasion setting involves a stimulus that modulates the association between a conditioned stimulus and an unconditioned stimulus.Building on this, Leising et al. (2025) applies the concept to a broader range of behaviors and cognitive processes, from Pavlovian and instrumental conditioning to theory of mind and language.However, their article leaves several areas requiring further discussions: (a) the effects of temporal gaps between feature and target stimuli on discrimination performance, (b) contextual control in flavor aversion learning, (c) contextual control in spatial learning, (d) mathematical modeling of occasion setting, and (e) the limits of understanding hierarchical event structures in nonhuman animals.Addressing these issues will refine the theoretical and practical understanding of occasion setting across disciplines.
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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 | 0.069 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.009 | 0.032 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".