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      Age-related changes in gaze behaviour during social interaction: An eye-tracking study with an embodied conversational agent

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          Abstract

          Previous research has highlighted age-related differences in social perception, in particular emotional expression processing. To date, such studies have largely focused on approaches that use static emotional stimuli that the participant has to identify passively without the possibility of any interaction. In this study, we propose an interactive virtual environment to better address age-related variations in social and emotional perception. A group of 22 young (18–30 years) and 20 older (60–80 years) adults were engaged in a face-to-face conversation with an embodied conversational agent. Participants were invited to interact naturally with the agent and to identify his facial expression. Their gaze behaviour was captured by an eye-tracking device throughout the interaction. We also explored whether the Big Five personality traits (particularly extraversion) and anxiety modulated gaze during the social interaction. Findings suggested that age-related differences in gaze behaviour were only apparent when decoding social signals (i.e., listening to a partner’s question, identifying facial expressions) and not when communicating social information (i.e., when speaking). Furthermore, higher extraversion levels consistently led to a shorter amount of time gazing towards the eyes, whereas higher anxiety levels led to slight modulations of gaze only when participants were listening to questions. Face-to-face conversation with virtual agents can provide a more naturalistic framework for the assessment of online socio-emotional interaction in older adults, which is not easily observable in classical offline paradigms. This study provides novel and important insights into the specific circumstances in which older adults may experience difficulties in social interactions.

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          Most cited references58

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            Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients.

            The amended (revised) Beck Depression Inventory (BDI-IA; Beck & Steer, 1993b) and the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996) were self-administered to 140 psychiatric outpatients with various psychiatric disorders. The coefficient alphas of the BDI-IA and the BDI-II were, respectively, .89 and .91. The mean rating for Sadness on the BDI-IA was higher than it was on the BDI-II, but the mean ratings for Past Failure, Self-Dislike, Change in Sleeping Pattern, and Change in Appetite were higher on the BDI-II than they were on the BDI-IA. The mean BDI-II total score was approximately 2 points higher than it was for the BDI-IA, and the outpatients also endorsed approximately one more symptom on the BDI-II than they did on the BDI-IA. The correlations of BDI-IA and BDI-II total scores with sex, ethnicity, age, the diagnosis of a mood disorder, and the Beck Anxiety Inventory (Beck & Steer, 1993a) were within 1 point of each other for the same variables.
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              Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Quarterly Journal of Experimental Psychology
                Quarterly Journal of Experimental Psychology
                SAGE Publications
                1747-0218
                1747-0226
                June 2021
                January 10 2021
                June 2021
                : 74
                : 6
                : 1128-1139
                Affiliations
                [1 ]Institut de psychologie, Université de Paris, Boulogne-Billancourt, France
                [2 ]Université de Paris, VAC, Boulogne-Billancourt, France
                [3 ]Laboratoire Cognition Santé Société (EA 6291), Université de Reims Champagne-Ardenne, Reims, France
                [4 ]Institut des systèmes intelligents et de robotique (ISIR), Sorbonne Université, CNRS UMR7222, Paris, France
                Article
                10.1177/1747021820982165
                51201680-ae87-47b6-acf1-d688a7c3d4ba
                © 2021

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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