How Interpersonal Distance Between Avatar and Human Influences Facial Affect Recognition in Immersive Virtual Reality

Juan Del Aguila; Luz M González-Gualda; María Angeles Játiva; Patricia Fernández-Sotos; Antonio Fernández-Caballero; Arturo S García

doi:10.3389/fpsyg.2021.675515

How Interpersonal Distance Between Avatar and Human Influences Facial Affect Recognition in Immersive Virtual Reality

Front Psychol. 2021 Jul 15:12:675515. doi: 10.3389/fpsyg.2021.675515. eCollection 2021.

Authors

Juan Del Aguila¹, Luz M González-Gualda¹, María Angeles Játiva², Patricia Fernández-Sotos^{1

3}, Antonio Fernández-Caballero^{2

3

4}, Arturo S García^{2

4}

Affiliations

¹ Complejo Hospitalario Universitario de Albacete (CHUA), Servicio de Salud de Castilla-La Mancha, Albacete, Spain.
² Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain.
³ CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain.
⁴ Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain.

Abstract

Purpose: The purpose of this study was to determine the optimal interpersonal distance (IPD) between humans and affective avatars in facial affect recognition in immersive virtual reality (IVR). The ideal IPD is the one in which the humans show the highest number of hits and the shortest reaction times in recognizing the emotions displayed by avatars. The results should help design future therapies to remedy facial affect recognition deficits. Methods: A group of 39 healthy volunteers participated in an experiment in which participants were shown 65 dynamic faces in IVR and had to identify six basic emotions plus neutral expression presented by the avatars. We decided to limit the experiment to five different distances: D1 (35 cm), D2 (55 cm), D3 (75 cm), D4 (95 cm), and D5 (115 cm), all belonging to the intimate and personal interpersonal spaces. Of the total of 65 faces, 13 faces were presented for each of the included distances. The views were shown at different angles: 50% in frontal view, 25% from the right profile, and 25% from the left profile. The order of appearance of the faces presented to each participant was randomized. Results: The overall success rate in facial emotion identification was 90.33%, being D3 the IPD with the best overall emotional recognition hits, although statistically significant differences could not be found between the IPDs. Consistent with results obtained in previous studies, identification rates for negative emotions were higher with increasing IPD, whereas the recognition task improved for positive emotions when IPD was closer. In addition, the study revealed irregular behavior in the facial detection of the emotion surprise. Conclusions: IVR allows us to reliably assess facial emotion recognition using dynamic avatars as all the IPDs tested showed to be effective. However, no statistically significant differences in facial emotion recognition were found among the different IPDs.

Keywords: emotion identification; facial affect recognition; interpersonal distance; reaction time; virtual reality.