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Record W2083543561 · doi:10.1089/109493103769710541

Eye-Tracking in Immersive Environments: A General Methodology to Analyze Affordance-Based Interactions from Oculomotor Dynamics

2003· article· en· W2083543561 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCyberPsychology & Behavior · 2003
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversité du Québec en OutaouaisInstitut national de psychiatrie légale Philippe-PinelUniversité du Québec à Montréal
FundersUniversité du Québec en Outaouais
KeywordsAffordanceComputer visionComputer scienceEye movementArtificial intelligenceEye trackingPerceptionSightHuman–computer interactionPsychologyPhysicsOptics

Abstract

fetched live from OpenAlex

This paper aims at presenting a new methodology to study how perceptual and motor processes organized themselves in order to achieve invariant visual information picking-up in virtual immersions. From a head-mounted display, head and eye movements were recorded using tracking devices (magnetic and infrared) that render the six degrees-of-freedom associated with the position and orientation of head movements, and two degrees-of-freedom from one eye. We measured the continuous line of sight's deviation from a pre-selected area on a virtual stimulus. Some preliminary analyses of the dynamical properties of the emergent perceptual and motor patterns are presented as they are considered to be representative of the process of affordance extraction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.380
Threshold uncertainty score1.000

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)0.0020.000

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 head0.098
GPT teacher head0.425
Teacher spread0.327 · 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