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Record W2991859347 · doi:10.5539/ijb.v12n1p30

Ants’ Mental Positioning of Amounts on a Number Line

2019· article· en· W2991859347 on OpenAlexvenueno aff
Roger Cammaerts, Marie‐Claire Cammaerts

Bibliographic record

VenueInternational Journal of Biology · 2019
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
Fundersnot available
KeywordsNumerosity adaptation effectMathematicsLine (geometry)StatisticsLinear relationshipPower functionLogarithmic scaleLogarithmFunction (biology)Value (mathematics)CombinatoricsBiologyEvolutionary biologyPhysicsMathematical analysisGeometryCognition

Abstract

fetched live from OpenAlex

Myrmica sabuleti ants have a mental number line on which numbers (non-symbolic displayed amounts) are ranked, the smaller on the left and the larger on the right. Here we try to know if the difference between two successive numbers is identically estimated all along this line or is less and less well estimated with increasing number magnitude. Ants were trained to distinguish two successive numbers differing by one unit (1 vs 2, 2 vs 3, …, 6 vs 7) during 72 hours and tested after 7, 24, 31, 48, 55 and 72 h. The ants responded less well for larger numbers (e.g. their response to 6 vs 7 was weaker than that to 1 vs 2). The relation between the ants’ ability in discriminating two successive numbers according to their size, ratio or relative difference was best described by a non-linear, power function and somewhat less well by a logarithmic function. A linear function could only significantly better fit the data when large fluctuations in the ants’ discrimination score appeared in the course of increasing training time. The ants’ mental positioning of numerosity on their number line appears thus to be compressed along a non-linear scale, most likely according to a power function of the numbers’ magnitude characteristics.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.999

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)0.0010.000

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 head0.024
GPT teacher head0.354
Teacher spread0.330 · 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.

Study designTheoretical or conceptual
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

Citations10
Published2019
Admission routes1
Has abstractyes

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