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dc.contributor.authorKim Sangmin-
dc.contributor.authorSeok Sukyung-
dc.contributor.authorJongSuk Choi-
dc.contributor.authorLim Yoonseob-
dc.contributor.authorSona Kwak-
dc.date.accessioned2024-01-19T08:38:35Z-
dc.date.available2024-01-19T08:38:35Z-
dc.date.created2021-12-14-
dc.date.issued2021-09-
dc.identifier.issn2153-0858-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/113325-
dc.description.abstractA non-lexical backchannel is known to be dependent on the conversational context, and its form can be distinguished by the social relation between the speaker and the listener in the Korean language. Thus, to investigate the effect of a non-lexical backchannel, we conducted a 2 (context: information-centric versus emotion-centric) x 3 (forms of backchannel: "ne" versus "eo" versus "eum") mixed- participant experiment (N = 96). After watching video stimuli, the participants evaluated a robot using one of the three forms of non- lexical backchannels under both contexts. No significant main effect of the forms of the non-lexical backchannel was found. By contrast, we found that task attraction and appropriateness of the robot were rated more positively under the information-centric context than under the emotion-centric context. The functions of the non-lexical backchannel of attentive listening and understanding were manifested more under the information-centric context than under the emotion-centric context. Furthermore, these functions mediated the effect of conversational context on the user perception of the robot in terms of task attraction and appropriateness.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleEffects of Conversational Contexts and Forms of Non-lexical Backchannel on User Perception of Robots-
dc.typeConference-
dc.identifier.doi10.1109/IROS51168.2021.9636589-
dc.description.journalClass1-
dc.identifier.bibliographicCitationIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.3042 - 3047-
dc.citation.titleIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)-
dc.citation.startPage3042-
dc.citation.endPage3047-
dc.citation.conferencePlaceUS-
dc.citation.conferencePlaceOnline-
dc.citation.conferenceDate2021-09-27-
dc.relation.isPartOf2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)-
dc.identifier.wosid000755125502063-
dc.identifier.scopusid2-s2.0-85124346917-
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KIST Conference Paper > 2021
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