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      The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial

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          Abstract

          Background

          For robots to be effectively used in health applications, they need to display appropriate social behaviors. A fundamental requirement in all social interactions is the ability to engage, maintain, and demonstrate attention. Attentional behaviors include leaning forward, self-disclosure, and changes in voice pitch.

          Objective

          This study aimed to examine the effect of robot attentional behaviors on user perceptions and behaviors in a simulated health care interaction.

          Methods

          A parallel randomized controlled trial with a 1:1:1 allocation ration was conducted. We randomized participants to 1 of 4 experimental conditions before engaging in a scripted face-to-face interaction with a fully automated medical receptionist robot. Experimental conditions included a self-disclosure condition, voice pitch change condition, forward lean condition, and neutral condition. Participants completed paper-based postinteraction measures relating to engagement, perceived robot attention, and perceived robot empathy. We video recorded interactions and coded for participant attentional behaviors.

          Results

          A total of 181 participants were recruited from the University of Auckland. Participants who interacted with the robot in the forward lean and self-disclosure conditions found the robot to be significantly more stimulating than those who interacted with the robot in the voice pitch or neutral conditions ( P=.03). Participants in the forward lean, self-disclosure, and neutral conditions found the robot to be significantly more interesting than those in the voice pitch condition ( P<.001). Participants in the forward lean and self-disclosure conditions spent significantly more time looking at the robot than participants in the neutral condition ( P<.001). Significantly, more participants in the self-disclosure condition laughed during the interaction ( P=.01), whereas significantly more participants in the forward lean condition leant toward the robot during the interaction ( P<.001).

          Conclusions

          The use of self-disclosure and forward lean by a health care robot can increase human engagement and attentional behaviors. Voice pitch changes did not increase attention or engagement. The small effects with regard to participant perceptions are potentially because of the limitations in self-report measures or a lack of comparison for most participants who had never interacted with a robot before. Further research could explore the use of self-disclosure and forward lean using a within-subjects design and in real health care settings.

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          Most cited references50

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          Attention, Joint Attention, and Social Cognition.

          Before social cognition there is joint processing of information about the attention of self and others. This joint attention requires the integrated activation of a distributed cortical network involving the anterior and posterior attention systems. In infancy, practice with the integrated activation of this distributed attention network is a major contributor to the development of social cognition. Thus, the functional neuroanatomies of social cognition and the anterior-posterior attention systems have much in common. These propositions have implications for understanding joint attention, social cognition, and autism.
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            Toward sociable robots

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              The psychosocial effects of a companion robot: a randomized controlled trial.

              To investigate the psychosocial effects of the companion robot, Paro, in a rest home/hospital setting in comparison to a control group. Randomized controlled trial. Residents were randomized to the robot intervention group or a control group that attended normal activities instead of Paro sessions. Sessions took place twice a week for an hour over 12 weeks. Over the trial period, observations were conducted of residents' social behavior when interacting as a group with the robot. As a comparison, observations were also conducted of all the residents during general activities when the resident dog was or was not present. A residential care facility in Auckland, New Zealand. Forty residents in hospital and rest home care. Residents completed a baseline measure assessing cognitive status, loneliness, depression, and quality of life. At follow-up, residents completed a questionnaire assessing loneliness, depression, and quality of life. During observations, behavior was noted and collated for instances of talking and stroking the dog/robot. In comparison with the control group, residents who interacted with the robot had significant decreases in loneliness over the period of the trial. Both the resident dog and the seal robot made an impact on the social environment in comparison to when neither was present. Residents talked to and touched the robot significantly more than the resident dog. A greater number of residents were involved in discussion about the robot in comparison with the resident dog and conversation about the robot occurred more. Paro is a positive addition to this environment and has benefits for older people in nursing home care. Paro may be able to address some of the unmet needs of older people that a resident animal may not, particularly relating to loneliness. Copyright © 2013 American Medical Directors Association, Inc. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                October 2019
                4 October 2019
                : 21
                : 10
                : e13667
                Affiliations
                [1 ] Department of Psychological Medicine Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand
                [2 ] Centre for Automation and Robotic Engineering Science The University of Auckland Auckland New Zealand
                [3 ] Department of Electrical, Computer, and Software Engineering Faculty of Engineering The University of Auckland Auckland New Zealand
                Author notes
                Corresponding Author: Elizabeth Broadbent e.broadbent@ 123456auckland.ac.nz
                Author information
                https://orcid.org/0000-0002-0056-9657
                https://orcid.org/0000-0001-7418-6280
                https://orcid.org/0000-0001-7602-8497
                https://orcid.org/0000-0001-9676-9348
                https://orcid.org/0000-0002-8707-5069
                https://orcid.org/0000-0003-3461-779X
                https://orcid.org/0000-0003-1005-6369
                https://orcid.org/0000-0003-3626-9100
                Article
                v21i10e13667
                10.2196/13667
                6914232
                31588904
                236c9488-dea3-45ec-9d42-e109373c2f08
                ©Deborah L Johanson, Ho Seok Ahn, Bruce A MacDonald, Byeong Kyu Ahn, JongYoon Lim, Euijun Hwang, Craig J Sutherland, Elizabeth Broadbent. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.10.2019

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 9 February 2019
                : 15 March 2019
                : 6 May 2019
                : 17 July 2019
                Categories
                Original Paper
                Original Paper

                Medicine
                robotics,health care robotics,social interaction,engagement,social intelligence
                Medicine
                robotics, health care robotics, social interaction, engagement, social intelligence

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