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Record W2300352270 · doi:10.1002/jeab.177

When good pigeons make bad decisions: Choice with probabilistic delays and outcomes

2015· article· en· W2300352270 on OpenAlexaff
Jeffrey M. Pisklak, Margaret A. McDevitt, Roger Dunn, Marcia L. Spetch

Bibliographic record

VenueJournal of the Experimental Analysis of Behavior · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProbabilistic logicFood choicePsychologyReinforcementSocial psychologyComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Pigeons chose between an (optimal) alternative that sometimes provided food after a 10-s delay and other times after a 40-s delay and another (suboptimal) alternative that sometimes provided food after 10 s but other times no food after 40 s. When outcomes were not signaled during the delays, pigeons strongly preferred the optimal alternative. When outcomes were signaled, choices of the suboptimal alternative increased and most pigeons preferred the alternative that provided no food after the long delay despite the cost in terms of obtained food. The pattern of results was similar whether the short delays occurred on 25% or 50% of the trials. Shortening the 40-s delay to food sharply reduced suboptimal choices, but shortening the delay to no food had little effect. The results suggest that a signaled delay to no food does not punish responding in probabilistic choice procedures. The findings are discussed in terms of conditioned reinforcement by signals for good news.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.164
GPT teacher head0.383
Teacher spread0.219 · 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 designBench or experimental
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

Citations29
Published2015
Admission routes1
Has abstractyes

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