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2P1-A16 Visual Effect of Spatial Depth Clue on Size-Weight Illusion

2006· article· en· W54411966 on OpenAlex
Masayuki Hara, Takahiro Higuchi, Jian Huang, Tetsuro Yabuta

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2006
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIllusionOptical illusionPerceptionCognitive psychologyObject (grammar)PsychologyComputer visionArtificial intelligenceComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

In general, human beings unconsciously estimate the spatial depth from the two-dimensional figure projected on the retina to understand the three-dimensional space. It is thought that such estimation based on the previous experiences produces various visual illusions like Muller-Lyer visual illusion, Ponzo visual illusion, etc. This paper attempts to examine an effect of a spatial depth clue on the weight perception by using a virtual reality technology. Then, this study focused on the size-weight illusion in which human beings surely perceive the object with large volume being light when comparing the weights of two objects with same weight and different volume. The results imply the possibility to reveal an attractive brain function of human beings.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.280
Teacher spread0.260 · 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