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Record W2098061942 · doi:10.1109/tvcg.2014.18

Dynamic Affordances in Embodied Interactive Systems: The Role of Display and Mode of Locomotion

2014· article· en· W2098061942 on OpenAlexaff
Timofey Grechkin, Jodie M. Plumert, Joseph K. Kearney

Bibliographic record

VenueIEEE Transactions on Visualization and Computer Graphics · 2014
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAffordanceHuman–computer interactionEmbodied cognitionJoystickVirtual realityComputer sciencePerceptionMode (computer interface)Action (physics)Task (project management)SimulationArtificial intelligencePsychologyEngineering

Abstract

fetched live from OpenAlex

We investigated how the properties of interactive virtual reality systems affect user behavior in full-body embodied interactions. Our experiment compared four interactive virtual reality systems using different display types (CAVE vs. HMD) and modes of locomotion (walking vs. joystick). Participants performed a perceptual-motor coordination task, in which they had to choose among a series of opportunities to pass through a gate that cycled open and closed and then board a moving train. Mode of locomotion, but not type of display, affected how participants chose opportunities for action. Both mode of locomotion and display affected performance when participants acted on their choices. We conclude that technological properties of virtual reality system (both display and mode of locomotion) significantly affected opportunities for action available in the environment (affordances) and discuss implications for design and practical applications of immersive interactive systems.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.271
Teacher spread0.263 · 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 designSimulation or modeling
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

Citations48
Published2014
Admission routes1
Has abstractyes

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