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Record W4389062699 · doi:10.46867/ijcp.2023.35.5621

The Creative Canine: Investigating the concept of creativity in dogs (Canis lupus familiaris) using citizen science

2023· article· en· W4389062699 on OpenAlexaboutno aff
Kaitlyn R. Willgohs, Jenna Williams, E. W. Franklin, Lauren Highfill

Bibliographic record

VenueInternational Journal of Comparative Psychology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
FundersAPDT Foundation
KeywordsPuppyCanisPsychologyNoveltyCreativityCognitionLabrador RetrieverCognitive psychologySocial psychologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

There is no shortage of anecdotal evidence that domestic dogs ( Canis lupus familiaris ) can solve problems in individual or creative ways. Whether it is figuring out a new way to knock over the trash can or combining puppy-dog eyes with a whine for some table scraps, dogs approach their world in many ways. In recent years, dogs have been studied for a number of cognitive functions but their ability to demonstrate creative behaviors has not been empirically studied. The present study extends training of the create behavior, as previously trained in dolphins, to dogs. The criteria of the create behavior required the dog to present a behavior that had yet to be performed in the session, therefore, the only incorrect response was a repeated behavior. Mastery of the create command was coded on three components: repetition, energy, and novelty. Possible implications of this research will be discussed. This study adds to the literature on dog cognition and supports the utilization of citizen science for canine cognition research.

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)0.000
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 score0.647
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.089
GPT teacher head0.478
Teacher spread0.389 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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