Evidence map›Paper›PMID 38448515›Full record

ArticleScientific reports2024

An exploratory study of the effect of age and gender on face scanning during affect recognition in immersive virtual reality.

Luz M González-Gualda, Miguel A Vicente-Querol, Arturo S García, José P Molina, José M Latorre, Patricia Fernández-Sotos, Antonio Fernández-Caballero

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 25% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

7 authors at 3 institutions in 2 countries.

Luz M González-GualdaServicio de Salud de Castilla-La Mancha, Complejo Hospitalario Universitario de Albacete, Servicio de Salud Mental, 02004, Albacete, Spain.
Miguel A Vicente-QuerolNeurocognition and Emotion Unit, Instituto de Investigación en Informática de Albacete, 02071, Albacete, Spain.
Arturo S GarcíaNeurocognition and Emotion Unit, Instituto de Investigación en Informática de Albacete, 02071, Albacete, Spain.
José P MolinaNeurocognition and Emotion Unit, Instituto de Investigación en Informática de Albacete, 02071, Albacete, Spain.
José M LatorreDepartmento de Psicología, Universidad de Castilla-La Mancha, 02071, Albacete, Spain.
Patricia Fernández-SotosServicio de Salud de Castilla-La Mancha, Complejo Hospitalario Universitario de Albacete, Servicio de Salud Mental, 02004, Albacete, Spain.
Antonio Fernández-CaballeroNeurocognition and Emotion Unit, Instituto de Investigación en Informática de Albacete, 02071, Albacete, Spain. antonio.fdez@uclm.es.ORCID 0000-0002-8211-0398
University of Castilla-La Mancha · ESInstituto de Salud Carlos III · ESServicio de Salud de Castilla La Mancha · ES

Funding

Ministry of Economy and Competitiveness | Agencia Estatal de Investigacin (Spanish Agencia Estatal de Investigacin) EQC2019-006063-PMinistry of Economy and Competitiveness | Agencia Estatal de Investigacin (Spanish Agencia Estatal de Investigacin) PID2020-115220RB-C21
6 · The paper itself

Abstract

A person with impaired emotion recognition is not able to correctly identify facial expressions represented by other individuals. The aim of the present study is to assess eyes gaze and facial emotion recognition in a healthy population using dynamic avatars in immersive virtual reality (IVR). For the first time, the viewing of each area of interest of the face in IVR is studied by gender and age. This work in healthy people is conducted to assess the future usefulness of IVR in patients with deficits in the recognition of facial expressions. Seventy-four healthy volunteers participated in the study. The materials used were a laptop computer, a game controller, and a head-mounted display. Dynamic virtual faces randomly representing the six basic emotions plus neutral expression were used as stimuli. After the virtual human represented an emotion, a response panel was displayed with the seven possible options. Besides storing the hits and misses, the software program internally divided the faces into different areas of interest (AOIs) and recorded how long participants looked at each AOI. As regards the overall accuracy of the participants' responses, hits decreased from the youngest to the middle-aged and older adults. Also, all three groups spent the highest percentage of time looking at the eyes, but younger adults had the highest percentage. It is also noteworthy that attention to the face compared to the background decreased with age. Moreover, the hits between women and men were remarkably similar and, in fact, there were no statistically significant differences between them. In general, men paid more attention to the eyes than women, but women paid more attention to the forehead and mouth. In contrast to previous work, our study indicates that there are no differences between men and women in facial emotion recognition. Moreover, in line with previous work, the percentage of face viewing time for younger adults is higher than for older adults. However, contrary to earlier studies, older adults look more at the eyes than at the mouth.Consistent with other studies, the eyes are the AOI with the highest percentage of viewing time. For men the most viewed AOI is the eyes for all emotions in both hits and misses. Women look more at the eyes for all emotions, except for joy, fear, and anger on hits. On misses, they look more into the eyes for almost all emotions except surprise and fear.

Indexed as

Virtual RealityAgedAngerAvatarEmotionsFearFemaleHumansMaleMiddle Aged

Identifiers

PMID38448515
PMCPMC10918108
OpenAlexW4392519290

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.