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Record W2590828580 · doi:10.1093/beheco/arx007

What’s flexible in behavioral flexibility?

2017· article· en· W2590828580 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehavioral Ecology · 2017
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsFlexibility (engineering)Cognitive flexibilitySet (abstract data type)Cognitive psychologyCognitionTerm (time)PersonalityBig Five personality traitsBehavioral syndromeBiologyCognitive sciencePsychologyComputer scienceSocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Behavioral ecologists interested in comparative cognition have struggled to design tasks that are both ecologically relevant and experimentally rigorous. In experimental psychology, standardized tests of reversal learning, set-shifting and self-control have long been used to measure aspects of flexible behavior especially with regards to determining the neural mechanisms that enable animals and humans to rapidly and efficiently adapt to different situations. More recently, behavioral ecologists have used the term “behavioral flexibility” more broadly to explain differences in traits such as personality and innovation. Here, we argue that the term behavioral flexibility designates too many non-equivalent traits, and that this can lead to misconceptions about the nature of cognitive abilities.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.149
GPT teacher head0.451
Teacher spread0.303 · 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