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Charpentier's Papers of 1886 and 1891 on Weight Perception and the Size-Weight Illusion

2012· article· en· W2133276324 on OpenAlexaff
Serge Nicolas, Helen E. Ross, David J. Murray

Bibliographic record

VenuePerceptual and Motor Skills · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsQueen's University
Fundersnot available
KeywordsIllusionPerceptionObject (grammar)PsychologyAffect (linguistics)Cognitive psychologyComputer scienceCommunicationArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

The French physiologist Augustin Charpentier (1852-1916) published the first accounts of the size-weight illusion-the observation that if two objects differ in size but have equal mass, the smaller will feel heavier when lifted. In the current paper, translations are presented of Charpentier's much-cited 1891 paper on weight perception and the size-weight illusion, and his little-known brief 1886 paper which contains the earliest experimental data on the illusion. Charpentier explained weight illusions in terms of the sense of effort involved in lifting the object and the contrast with the expected effort. Modern research shows that people quickly adapt and use the appropriate force to pick up objects, but the illusion persists even when appropriate force is used; expectations therefore affect the perceptual system more strongly than the motor system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0020.007
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2012
Admission routes1
Has abstractyes

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