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Record W2751098927 · doi:10.1037/com0000083

On the clock: Interval timing and overshadowing in domestic dogs (Canis familiaris).

2017· article· en· W2751098927 on OpenAlexfundno aff
Krista Macpherson, William A. Roberts

Bibliographic record

VenueJournal of comparative psychology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStimulus (psychology)AudiologyPsychologyInterval (graph theory)Time perceptionTone burstTone (literature)Vigilance (psychology)CommunicationNeuroscienceCognitionMathematicsCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

Interval timing is an important skill that allows animals to approximate how much time has elapsed since a given event. Little, however, is known about interval timing in domestic dogs. In an initial experiment, dogs were trained to make an operant response on 30-s fixed intervals, with either a light or a tone + light compound signaling the beginning of the fixed interval. When dogs in the compound group were subsequently tested with nonreinforced 60-s tone-only probe trials, the dogs' rate of responding peaked near 30 s. When the same dogs were tested with light-only probes, however, no evidence of timing was found. In a second experiment, a bisection task was used in which dogs had to learn to approach 1 feeder when given an 8-s tone + light signal, and another feeder when given a 2-s tone + light signal. When subsequently tested at intermediate durations, psychophysical curves showed clear control of timing by the tone stimulus but not by the light stimulus. These findings clearly demonstrate that dogs are able to time fixed intervals and show the existence of an overshadowing effect, in which dogs are able to time a light cue presented alone but do not attend to the light when it is presented simultaneously with a tone. (PsycINFO Database Record

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 score0.835
Threshold uncertainty score0.297

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 head0.094
GPT teacher head0.470
Teacher spread0.375 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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