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Record W4408741823 · doi:10.3819/ccbr.2025.200006

Setting the Occasion for Suboptimal Choice

2025· article· en· W4408741823 on OpenAlexvenueno aff
Kent D. Bodily

Bibliographic record

VenueComparative Cognition & Behavior Reviews · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsComparative cognitionAnimal behaviorPsychologyCognitive psychologyCognitive scienceZoologyCognitionNeuroscienceBiology

Abstract

fetched live from OpenAlex

Occasion setting occurs when a stimulus effectively modulates the relationship between a conditioned stimulus and reinforcement-specifically, when behavior is elicited in response to a conditioned stimulus when an occasion setter is present but not in its absence.In the target article "Are You Studying Occasion Setting?A Review for Inquiring Minds," Leising et al. (2025) extensively review many testing procedures in which occasion setters are present to highlight the importance of their presence and impact on performance.In this commentary, we broaden this discussion by revisiting a suboptimal choice procedure and reframing it using the lens of occasion setting.We propose that there are stimuli within this choice task that serve as occasion setters for behavior.Using this interpretation of the suboptimal choice procedure illuminates a potential explanation for why a suboptimal preference has been observed by pigeons but not by human participants.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.013
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.202
GPT teacher head0.370
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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