Evidence map›Paper›PMID 34770482›Full record

ArticleSensors (Basel, Switzerland)2021

A High-Density EEG Study Investigating VR Film Editing and Cognitive Event Segmentation Theory.

Feng Tian, Hui Wang, Wanqiu Cheng, Wenrui Zhang, Yingjie Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Rethinking Naturalistic Movie Neuroimaging Through Film Form.Behavioral sciences (Basel, Switzerland) · 2026
    Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. 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

5 authors.

Feng TianShanghai Film Academy, Shanghai University, Shanghai 200072, China.
Hui WangShanghai Film Academy, Shanghai University, Shanghai 200072, China.
Wanqiu ChengShanghai Film Academy, Shanghai University, Shanghai 200072, China.
Wenrui ZhangShanghai Film Academy, Shanghai University, Shanghai 200072, China.
Yingjie LiSchool of Life Sciences, Shanghai University, Shanghai 200444, China.

Funding

the National Social Science Fund of China 17BC043
6 · The paper itself

Abstract

This paper introduces a cognitive psychological experiment that was conducted to analyze how traditional film editing methods and the application of cognitive event segmentation theory perform in virtual reality (VR). Thirty volunteers were recruited and asked to watch a series of short VR videos designed in three dimensions: time, action (characters), and space. Electroencephalograms (EEG) were recorded simultaneously during their participation. Subjective results show that any of the editing methods used would lead to an increased load and reduced immersion. Furthermore, the cognition of event segmentation theory also plays an instructive role in VR editing, with differences mainly focusing on frontal, parietal, and central regions. On this basis, visual evoked potential (VEP) analysis was performed, and the standardized low-resolution brain electromagnetic tomography algorithm (sLORETA) traceability method was used to analyze the data. The results of the VEP analysis suggest that shearing usually elicits a late event-related potential component, while the sources of VEP are mainly the frontal and parietal lobes. The insights derived from this work can be used as guidance for VR content creation, allowing VR image editing to reveal greater richness and unique beauty.

Indexed as

Virtual RealityCognitionElectroencephalographyEvoked PotentialsEvoked Potentials, VisualHumanscognitive event segmentation theoryEEGvisual evoked potentialVR film

Identifiers

PMID34770482
PMCPMC8586935

What OpenQuestion holds

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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.