Attention and impulsivity assessment using virtual reality games.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
22 08 2023
Historique:
received: 10 01 2023
accepted: 10 08 2023
medline: 24 8 2023
pubmed: 23 8 2023
entrez: 22 8 2023
Statut: epublish

Résumé

The assessment of cognitive functions is mainly based on standardized neuropsychological tests, widely used in various fields such as personnel recruitment, education, or health. This paper presents a virtual reality game that allows collecting continuous measurements of both the performance and behaviour of the subject in an immersive, controllable, and naturalistic experience. The application registers variables related to the user's eye movements through the use of virtual reality goggles, as well as variables of the game performance. We study how virtual reality can provide data to help predict scores on the Attention Control Scale Test and the Barratt Impulsiveness Scale. We design the application and test it with a pilot group. We build a random forest regressor model to predict the attention and impulsivity scales' total score. When evaluating the performance of the model, we obtain a positive correlation with attention (0.434) and with impulsivity (0.382). In addition, our model identified that the most significant variables are the time spent looking at the target or at distractors, the eye movements variability, the number of blinks and the pupil dilation in both attention and impulsivity. Our results are consistent with previous results in the literature showing that it is possible to use data collected in virtual reality to predict the degree of attention and impulsivity.

Identifiants

pubmed: 37608015
doi: 10.1038/s41598-023-40455-4
pii: 10.1038/s41598-023-40455-4
pmc: PMC10444747
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

13689

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

David Mendez-Encinas (D)

Departamento de Estádistica, Universidad Carlos III de Madrid, Leganes, Spain.

Aaron Sujar (A)

Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Móstoles, Spain. aaron.sujar@urjc.es.

Sofia Bayona (S)

Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Móstoles, Spain.

David Delgado-Gomez (D)

Departamento de Estádistica, Universidad Carlos III de Madrid, Leganes, Spain.

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