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Record W2073594070 · doi:10.4236/ojas.2013.33a001

Encoding of local and global cues in domestic dogs’ spatial working memory

2013· article· en· W2073594070 on OpenAlexafffund
Sylvain Fiset, Nathalie Malenfant

Bibliographic record

VenueOpen Journal of Animal Sciences · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversité de Moncton
FundersEuropean Regional Development FundNatural Sciences and Engineering Research Council of CanadaUniversité de Moncton
KeywordsObject (grammar)Sensory cuePsychologyCommunicationEncoding (memory)Cognitive psychologySocial cueComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The current study investigated whether domestic dogs encode local and/or global cues in spatial working memory. Seven dogs were trained to use a source of allocentric information (local and/or global cues) to locate an attractive object they saw move and disappear behind one of the three opaque boxes arrayed in front of them. To do so, after the disappearance of the target object and out of the dogs’ knowledge, all sources of allocentric information were simultaneously shifted to a new spatial position and the dogs were forced to follow a U-shaped pathway leading to the hiding box. Out of the seven dogs that were trained in the detour problem, only three dogs learned to use the cues that were moved from trial to trial. On tests, local (boxes and experimenter) and/or global cues (walls of the room) were systematically and drastically shifted to a new position in the testing chamber. Although they easily succeeded the control trials, the three dogs failed to use a specific source of allocentric information when local and global cues were put in conflict. In discussion, we explore several hypotheses to explain why dogs have difficulties to use allocentric cues to locate a hidden object in a detour problem and why they do not differentiate the local and global cues in this particular experimental setting.

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 score0.645
Threshold uncertainty score0.206

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 head0.040
GPT teacher head0.382
Teacher spread0.342 · 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
Published2013
Admission routes2
Has abstractyes

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