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Record W1983087177 · doi:10.1037/0735-7036.114.4.315

Spatial encoding of hidden objects in dogs (Canis familiaris).

2000· article· en· W1983087177 on OpenAlexaff
Sylvain Fiset, Sylvain Gagnon, Claude Beaulieu

Bibliographic record

VenueJournal of comparative psychology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsFrame of referenceReference frameSpatial analysisCanisObject (grammar)LandmarkEncoding (memory)Frame (networking)Artificial intelligenceENCODECommunicationPosition (finance)Spatial learningComputer visionSpatial cognitionCartographyComputer sciencePattern recognition (psychology)GeographyPsychologyNeuroscienceBiologyCognitionEcology

Abstract

fetched live from OpenAlex

The authors investigated the type of spatial information that controls domestic dogs' (Canis familiaris) search behavior in a situation in which they have to locate a spatial position where they saw an object move and disappear. In Experiments 1 and 2, the authors manipulated all local and global sources of allocentric spatial information surrounding the hiding location. The results revealed that dogs relied on an egocentric frame of reference. Experiment 3 showed that dogs also encoded allocentric information when egocentric information was irrelevant. The authors conclude that dogs simultaneously encode both egocentric and allocentric spatial information to locate a spatial position, but they primarily base their search behavior on an egocentric frame of reference. The authors discuss under which natural conditions dogs might use these 2 sources of spatial information and detail the nature of spatial egocentric information and the circumstances underlying its use by dogs.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.421
Teacher spread0.376 · 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 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

Citations61
Published2000
Admission routes1
Has abstractyes

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