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Record W2163030742 · doi:10.1145/1620993.1621025

Is seeing a virtual environment like seeing the real thing?

2009· article· en· W2163030742 on OpenAlexaff
Dinara Moura, Bernhard E. Riecke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceHuman–computer interactionSpatial cognitionStimulus (psychology)EntertainmentVirtual realityCognitionVirtual machineNatural (archaeology)Immersion (mathematics)MultimediaCognitive psychologyPsychologyGeography

Abstract

fetched live from OpenAlex

Immersive virtual environments (IVE) are increasingly used in both fundamental research like experimental psychology and applications such as training, phobia therapy, or entertainment. Ideally, people should be able to perceive and behave in such IVEs as naturally and effectively as in real environments --- especially if real-world transfer is desired. Being inherently mobile species, enabling natural spatial orientation and cognition in IVEs is essential. Here, we investigated whether seeing a virtual environment has a similar effect on our spatial cognition and mental spatial representation as a comparable real-world stimulus does -- if it does not, how could we assume real-world transfer?

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 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 score0.767
Threshold uncertainty score0.449

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)0.0000.000

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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.

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

Citations3
Published2009
Admission routes1
Has abstractyes

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