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Record W2187571431 · doi:10.1037/a0039227

Visual choice behavior by bumblebees (Bombus impatiens) confirms unsupervised neural network’s predictions.

2015· article· en· W2187571431 on OpenAlexafffund
Levente L. Orbán, Catherine Plowright, Sylvain Chartier, Emma Thompson, Vicki Xu

Bibliographic record

VenueJournal of comparative psychology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBumblebeeContext (archaeology)PreferenceSymmetry (geometry)Artificial intelligenceArtificial neural networkCommunicationComputer sciencePsychologyCognitive psychologyMathematicsEcologyBiologyStatisticsGeometryPollinator

Abstract

fetched live from OpenAlex

The behavioral experiment herein tests the computational load hypothesis generated by an unsupervised neural network to examine bumblebee (Bombus impatiens) behavior at 2 visual properties: spatial frequency and symmetry. Untrained "flower-naïve" bumblebees were hypothesized to prefer symmetry only when the spatial frequency of artificial flowers is high and therefore places great information-processing demands on the bumblebees' visual system. Bumblebee choice behavior was recorded using high-definition motion-sensitive camcorders. The results support the computational model's prediction: 1-axis symmetry influenced bumblebees' preference behavior at low and high spatial frequency patterns. Additionally, increasing the level of symmetry from 1 axis to 4 axes amplified preference toward the symmetric patterns of both low and high spatial frequency patterns. The results are discussed in the context of the artificial neural network model and other hypotheses generated from the behavioral literature.

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 score0.493
Threshold uncertainty score0.274

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 head0.146
GPT teacher head0.353
Teacher spread0.208 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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