Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task.

action units affective states cognitive load human-robot interaction social robots

Journal

Frontiers in neurorobotics
ISSN: 1662-5218
Titre abrégé: Front Neurorobot
Pays: Switzerland
ID NLM: 101477958

Informations de publication

Date de publication:
2022
Historique:
received: 23 02 2022
accepted: 28 04 2022
entrez: 18 8 2022
pubmed: 19 8 2022
medline: 19 8 2022
Statut: epublish

Résumé

A key goal in human-robot interaction (HRI) is to design scenarios between humanoid robots and humans such that the interaction is perceived as collaborative and natural, yet safe and comfortable for the human. Human skills like verbal and non-verbal communication are essential elements as humans tend to attribute social behaviors to robots. However, aspects like the uncanny valley and different technical affinity levels can impede the success of HRI scenarios, which has consequences on the establishment of long-term interaction qualities like trust and rapport. In the present study, we investigate the impact of a humanoid robot on human emotional responses during the performance of a cognitively demanding task. We set up three different conditions for the robot with increasing levels of social cue expressions in a between-group study design. For the analysis of emotions, we consider the eye gaze behavior, arousal-valence for affective states, and the detection of action units. Our analysis reveals that the participants display a high tendency toward positive emotions in presence of a robot with clear social skills compared to other conditions, where we show how emotions occur only at task onset. Our study also shows how different expression levels influence the analysis of the robots' role in HRI. Finally, we critically discuss the current trend of automatized emotion or affective state recognition in HRI and demonstrate issues that have direct consequences on the interpretation and, therefore, claims about human emotions in HRI studies.

Identifiants

pubmed: 35978569
doi: 10.3389/fnbot.2022.882483
pmc: PMC9377278
doi:

Types de publication

Journal Article

Langues

eng

Pagination

882483

Informations de copyright

Copyright © 2022 Jirak, Aoki, Yanagi, Takamatsu, Bouet, Yamamura, Sandini and Rea.

Déclaration de conflit d'intérêts

MA, TYan, AT, SB, and TYam were employed by the Nissan Motor Corporation. The authors declare that this study received funding from NISSAN. The funder had the following involvement in the study: study design, collection, analysis, interpretation of data, the writing of this article and the decision to submit it for publication.

Références

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Auteurs

Doreen Jirak (D)

Robotics, Brain and Cognitive Science Group (RBCS), Istituto Italiano di Tecnologia, Genova, Italy.

Motonobu Aoki (M)

Robotics, Brain and Cognitive Science Group (RBCS), Istituto Italiano di Tecnologia, Genova, Italy.
Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy.
Mobility and AI Laboratory, Research Division, Nissan Motor Co., Ltd., Atsugi, Japan.

Takura Yanagi (T)

Mobility and AI Laboratory, Research Division, Nissan Motor Co., Ltd., Atsugi, Japan.

Atsushi Takamatsu (A)

Mobility and AI Laboratory, Research Division, Nissan Motor Co., Ltd., Atsugi, Japan.

Stephane Bouet (S)

Mobility and AI Laboratory, Research Division, Nissan Motor Co., Ltd., Atsugi, Japan.

Tomohiro Yamamura (T)

Mobility and AI Laboratory, Research Division, Nissan Motor Co., Ltd., Atsugi, Japan.

Giulio Sandini (G)

Robotics, Brain and Cognitive Science Group (RBCS), Istituto Italiano di Tecnologia, Genova, Italy.

Francesco Rea (F)

Robotics, Brain and Cognitive Science Group (RBCS), Istituto Italiano di Tecnologia, Genova, Italy.

Classifications MeSH