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Record W2794384276 · doi:10.1037/xan0000161

Cognitive flexibility and dual processing in pigeons: Temporal and contextual control of midsession reversal.

2018· article· en· W2794384276 on OpenAlexafffund
Hayden MacDonald, William A. Roberts

Bibliographic record

VenueJournal of Experimental Psychology Animal Learning and Cognition · 2018
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContext (archaeology)TimerPsychologyFlexibility (engineering)Interval (graph theory)Cognitive psychologyControl (management)CognitionAudiologyComputer scienceNeuroscienceArtificial intelligenceMedicineStatistics

Abstract

fetched live from OpenAlex

Evidence is reported showing that pigeons flexibly use temporal and contextual cues to maximize reward obtained in a midsession reversal task. Pigeons were trained to choose between red and green sidekeys for 60 trials in a session, with choice of one color correct on Trials 1-30 and choice of the other color correct on Trials 31-60 (midsession reversal). Pigeons showed anticipatory errors before reversal and perseverative errors after reversal, and manipulations of the length of the intertrial interval and the point of reversal suggested that pigeons used an internal timer to track the point of reversal. When houselight context cues that signaled the correct choice were presented throughout trials in Experiment 1, choice behavior rapidly came under context control, leading pigeons to rarely make errors and to show no effect of intertrial interval or point of reversal. In Experiments 2 and 3, switches between context and no-context cues occurred among trials. These manipulations revealed that pigeons can readily switch between context control and temporal control of behavior. The internal timer continued to run throughout context trials and could readily be accessed to control choice behavior when a context cue was removed. (PsycINFO Database Record

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

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

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 head0.046
GPT teacher head0.396
Teacher spread0.350 · 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 teacher head, 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

Citations9
Published2018
Admission routes2
Has abstractyes

Explore more

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