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Record W2923612422 · doi:10.1037/com0000174

Dogs (Canis familiaris) use odor cues to show episodic-like memory for what, where, and when.

2019· article· en· W2923612422 on OpenAlexaff
Ka Ho Lo, William A. Roberts

Bibliographic record

VenueJournal of comparative psychology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsEpisodic memoryEncoding (memory)OdorPsychologyCognitive psychologyOlfactory cuesSemantic memoryNeuroscienceOlfactionCognition

Abstract

fetched live from OpenAlex

Episodic-like memory is a personal memory that contains what happened, where it happened, and when it happened. Although episodic-like memory in nonhuman animals has been shown using what-where-when memory paradigms, it has not previously been shown in dogs. Dogs are an excellent candidate for developing translational models of neurodegenerative disorders related to episodic memory, including Alzheimer's disease. Dogs were tested in experiments that involved spatially and temporally unique sequences of odor stimuli to see if they remembered the odors, their locations, and their times of presentation. By choosing the earlier exposed odor on two-choice tests, dogs showed the ability to encode what-when, where-when, or what-where-when memory. Further tests revealed that dogs performed optimally when all three components of what-where-when memory were available for encoding and could flexibly use this information on unpredictable tests. Although the experiments reported here show that dogs remembered what, where, and when, they did not indicate whether these components were part of an integrated single memory or were retrieved from separate files. Evidence on the question of integrated memory requires trials on which all three components are tested. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.247
GPT teacher head0.378
Teacher spread0.131 · 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 designNot applicable
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

Citations15
Published2019
Admission routes1
Has abstractyes

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