Evidence map›Paper›PMID 39117987›Full record

ArticleBehavior research methods2024

A tutorial: Analyzing eye and head movements in virtual reality.

Walter F Bischof, Nicola C Anderson, Alan Kingstone

Abstract read
PubMed Publisher
In one paragraph

Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Walter F BischofDepartment of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada. wfb@ualberta.ca.ORCID 0000-0001-5508-0421
Nicola C AndersonDepartment of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada.ORCID 0000-0003-0975-475X
Alan KingstoneDepartment of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada.ORCID 0000-0002-0025-855X

Funding

Natural Sciences and Engineering Research Council of Canada AK: RGPIN-2022-03079Natural Sciences and Engineering Research Council of Canada NCA: Postdoctoral Fellowship
6 · The paper itself

Abstract

This tutorial provides instruction on how to use the eye tracking technology built into virtual reality (VR) headsets, emphasizing the analysis of head and eye movement data when an observer is situated in the center of an omnidirectional environment. We begin with a brief description of how VR eye movement research differs from previous forms of eye movement research, as well as identifying some outstanding gaps in the current literature. We then introduce the basic methodology used to collect VR eye movement data both in general and with regard to the specific data that we collected to illustrate different analytical approaches. We continue with an introduction of the foundational ideas regarding data analysis in VR, including frames of reference, how to map eye and head position, and event detection. In the next part, we introduce core head and eye data analyses focusing on determining where the head and eyes are directed. We then expand on what has been presented, introducing several novel spatial, spatio-temporal, and temporal head-eye data analysis techniques. We conclude with a reflection on what has been presented, and how the techniques introduced in this tutorial provide the scaffolding for extensions to more complex and dynamic VR environments.

Indexed as

Eye MovementsEye-Tracking TechnologyHead MovementsVirtual RealityHumansUser-Computer InterfaceEye movementsHead–eyes relationshipHead movementsSpatial analysisTemporal analysisVirtual reality

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.