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Record W2530670733 · doi:10.3390/bs6040022

Learned Use of Picture Cues by Bumblebees (Bombus impatiens) in a Delayed Matching Task

2016· article· en· W2530670733 on OpenAlexafffund
Emma Thompson, Catherine Plowright

Bibliographic record

VenueBehavioral Sciences · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBumblebeeMatching (statistics)Task (project management)PsychologySimilarity (geometry)PreferenceCognitive psychologySensory cueCommunicationObject (grammar)Artificial intelligenceComputer scienceComputer visionMathematicsBiologyEcologyStatistics

Abstract

fetched live from OpenAlex

Picture-object correspondence provides an alternate method of investigating delayed matching by providing a cue (picture) which may be spontaneously perceived as similar but different from a corresponding target. Memory for, and corresponding choice of, a target corresponding to a cue could be facilitated by the use of a picture. Bumblebees have been found to both easily differentiate images from corresponding objects but also spontaneously perceive a similarity between the two. Herein, an approach was designed to test the possible use of picture cues to signal reward in a delayed matching task. Target choice preference corresponding to picture cues was tested among three bumblebee (Bombus impatiens) colonies using photograph cues (presented prior to target stimuli) corresponding to one of four target stimuli. Photograph cues were the only predictor of corresponding target reward, presented in stable locations. Rewarded and unrewarded tests show a choice preference significantly higher than chance for targets matching the cue. Results suggest that bumblebees can learn to use picture cues in a delayed matching task. Furthermore, experience, conditions of reward inconsistency and location, are discussed as possible contributing factors to learning in a delayed matching task.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.114
GPT teacher head0.372
Teacher spread0.258 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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