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Record W2984006497 · doi:10.1037/com0000205

A comparative study of memory for olfactory discriminations: Dogs (Canis familiaris), rats (Rattus norvegicus), and humans (Homo sapiens).

2019· article· en· W2984006497 on OpenAlexafffund
Gordon Ka-Ho Lo, Krista Macpherson, Hayden MacDonald, William A. Roberts

Bibliographic record

VenueJournal of comparative psychology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHomo sapiensCanisPsychologyOlfactory cuesBiologyZoologyDevelopmental psychologyCommunicationOlfactionNeuroscienceEcology

Abstract

fetched live from OpenAlex

Disagreement has arisen in the scientific literature regarding the relative olfactory ability of humans relative to other mammals, specifically canines and rodents. A series of experiments are reported in which memory for multiple olfactory discriminations was measured in dogs, rats, and humans. Participants from all three species learned a sequence of 20 different discriminations between an S + odor and an S- odor. Choice of the S+ odor was rewarded with food for dogs and rats and with positive verbal feedback for humans. After learning the discriminations, an initial memory test was given that involved presentation of all 20 S + and S- pairs. A subsequent mix-and-match test was given in which each S + odor was presented with three different S- odors. The memory tests revealed that dogs were superior to rats and that dogs and rats were superior to humans. The relatively poor performance of humans contrasts with prior findings of high recognition memory for odors followed by slow forgetting. We attribute the low accuracy of humans in our experiments to the requirement that participants had to remember the outcome associated with S + (correct) and S- (incorrect) cues and not just their familiarity. (PsycInfo Database Record (c) 2020 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.889

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 communication0.0000.000
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 head0.304
GPT teacher head0.403
Teacher spread0.100 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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