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Record W4412361427 · doi:10.1038/s41598-025-10319-0

How a lack of haptic feedback affects eye-hand coordination and embodiment in virtual reality

2025· article· en· W4412361427 on OpenAlexafffund
Ewen B. Lavoie, Jacqueline S. Hebert, Craig S. Chapman

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced ResearchTD Bank
KeywordsHaptic technologyVirtual realityComputer scienceEye–hand coordinationHuman–computer interactionVisual feedbackArtificial intelligence

Abstract

fetched live from OpenAlex

Intuitively, we know that how we perceive and act in the world is profoundly affected when the lights go out. But what happens to visuomotor control when our sense of touch is taken away? Notably this happens in Virtual Reality (VR) and for prosthesis users. We test this question by combining VR and hand-, motion- and eye-tracking to give and deprive full haptic feedback to individuals with normal hand function during a validated object interaction task. Returning haptic feedback in VR generated eye-hand coordination more similar to real-world interactions. Interestingly, VR users and prosthesis users have both reported reduced feelings of embodiment towards their limbs. Therefore, we also quantified the sense of embodiment which increased with haptic feedback. We further reported a correlation between eye-hand coordination and an individual's sense of embodiment suggesting that the embodiment is experienced as the synchronized reception of sensory information and that eye-hand coordination measures are an objective way to quantify these experiences.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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 designTheoretical or conceptual
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

Citations9
Published2025
Admission routes2
Has abstractyes

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