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Record W2610317393 · doi:10.1145/3025453.3025488

Effects of Tactile Feedback on the Perception of Virtual Shapes on Non-Planar DisplayObjects

2017· article· en· W2610317393 on OpenAlexafffund
Juan Pablo Carrascal, Roel Vertegaal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIllusionPlanarPrismTactile perceptionPerceptionHaptic technologyEnhanced Data Rates for GSM EvolutionFlat surfaceComputer visionArtificial intelligenceComputer scienceOpticsMaterials sciencePsychologyComputer graphics (images)Physics

Abstract

fetched live from OpenAlex

In this paper, we report on a study investigating a novel haptic illusion for altering the perception of 3D shapes using a non-planar screen and vibrotactile friction. In our study, we presented an image of a rectangular prism on a cylindrical and a flat display. Participants were asked to move their index finger horizontally along the surface of the displays towards the edge of the rectangular prism. Participants were asked whether they were experiencing a flat, cylindrical or rectangular shape. In one condition, a vibrotactile stimulus simulated increasing friction towards the visible edge of the rectangular prism, with a sudden drop-off when this edge was crossed by the finger. Results suggest that presenting an image of a rectangular prism, and applying vibrotactile friction, particularly on a cylindrical display, significantly increased participant ratings stating that they were experiencing a physical rectangular shape.

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)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.283
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 source (direct Gemma or distilled Codex), 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

Citations5
Published2017
Admission routes2
Has abstractyes

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