Evidence map›Paper›PMID 38978970›Full record

ArticleJournal of eye movement research2022

An investigation of feed-forward and feedback eye movement training in immersive virtual reality.

David J Harris, Mark R Wilson, Martin I Jones, Toby de Burgh, Daisy Mundy, Tom Arthur, Mayowa Olonilua, Samuel J Vine

Abstract read
In one paragraph

Article in Journal of eye movement research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

David J HarrisSchool of Public Health and Sport Sciences, University of Exeter, UK.
Mark R WilsonSchool of Public Health and Sport Sciences, University of Exeter, UK.
Martin I JonesDefence Science and Technology Laboratory, Salisbury, UK.
Toby de BurghCineon Training, Exeter, UK.
Daisy MundyRINA, Swindon, Wiltshire, UK.
Tom ArthurSchool of Public Health and Sport Sciences, University of Exeter, UK.
Mayowa OloniluaDefence Science and Technology Laboratory, Salisbury, UK.
Samuel J VineSchool of Public Health and Sport Sciences, University of Exeter, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The control of eye gaze is critical to the execution of many skills. The observation that task experts in many domains exhibit more efficient control of eye gaze than novices has led to the development of gaze training interventions that teach these behaviours. We aimed to extend this literature by i) examining the relative benefits of feed-forward (observing an expert's eye movements) versus feed-back (observing your own eye movements) training, and ii) automating this training within virtual reality. Serving personnel from the British Army and Royal Navy were randomised to either feed-forward or feed-back training within a virtual reality simulation of a room search and clearance task. Eye movement metrics - including visual search, saccade direction, and entropy - were recorded to quantify the efficiency of visual search behaviours. Feed-forward and feed-back eye movement training produced distinct learning benefits, but both accelerated the development of efficient gaze behaviours. However, we found no evidence that these more efficient search behaviours transferred to better decision making in the room clearance task. Our results suggest integrating eye movement training principles within virtual reality training simulations may be effective, but further work is needed to understand the learning mechanisms.

Indexed as

defenceEye movementeye trackingmilitaryskill acquisitionVR

Identifiers

PMID38978970
PMCPMC11229047

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.