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Record W4294366840 · doi:10.26451/abc.09.03.03.2022

Reward Type Affects Dogs' Performance in the Cylinder Task

2022· article· en· W4294366840 on OpenAlexaboutno aff
Sarah Krichbaum, Lucia Lazarowski

Bibliographic record

VenueAnimal Behavior and Cognition · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsImpulsivityTask (project management)Inhibitory controlPsychologyCognitive psychologyCylinderCognitionDevelopmental psychologyMathematicsNeuroscienceEngineering

Abstract

fetched live from OpenAlex

The cylinder task, which requires detouring around an obstacle to retrieve a reward, is a popular method for assessing inhibitory control in dogs and other species. However, performance on the cylinder task has poor construct validity represented by its lack of correlations with other inhibitory control measures, ceiling effects, and influence of non-cognitive factors. In the current study we examined whether reward type affected dogs’ performance in the cylinder task. We compared working-line Labrador retrievers' (n = 38) performance on two conditions of the cylinder task, one with a treat and another with a ball reward, and found that dogs performed significantly better when a treat was used. Our secondary goal was to determine if how we defined a dog’s response changed interpretation of the results. We found better performance when a narrower definition of an inhibitory control failure was used. Further, under one condition of reward type and response definition, cylinder task performance was predicted by another measure of inhibitory control (Dog Impulsivity Assessment Scale scores). These findings are the first to show the effect of reward type on cylinder task performance as well as a relationship between the cylinder task and another measure of inhibitory control in dogs. We discuss these results in relation to previous findings on the effects of task features on cylinder task performance and its construct validity.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.325
Teacher spread0.300 · 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 designObservational
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

Citations11
Published2022
Admission routes1
Has abstractyes

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