Backchannel Behavior Influences the Perceived Personality of Human and Artificial Communication Partners

Blomsma, Peter; Skantze, Gabriel; Swerts, Marc

doi:10.3389/frai.2022.835298

Cited by 6 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results revealed a positive association between a higher number of identified BOPs and elevated levels of agreeableness, conscientiousness and openness. This is in line with the results of an earlier study that showed that different types of backchannel behavior correlate with various impressions of people's specific personalities (Blomsma et al, 2022).…”

Section: Current Worksupporting

confidence: 92%

See 1 more Smart Citation

Backchannel behavior is idiosyncratic

Blomsma,

Vaitonyté,

Skantze

et al. 2024

Lang. cogn.

Self Cite

View full text Add to dashboard Cite

In spoken conversations, speakers and their addressees constantly seek and provide different forms of audiovisual feedback, also known as backchannels, which include nodding, vocalizations and facial expressions. It has previously been shown that addressees backchannel at specific points during an interaction, namely after a speaker provided a cue to elicit feedback from the addressee. However, addressees may differ in the frequency and type of feedback that they provide, and likewise, speakers may vary the type of cues they generate to signal the backchannel opportunity points (BOPs). Research on the extent to which backchanneling is idiosyncratic is scant. In this article, we quantify and analyze the variability in feedback behavior of 14 addressees who all interacted with the same speaker stimulus. We conducted this research by means of a previously developed experimental paradigm that generates spontaneous interactions in a controlled manner. Our results show that (1) backchanneling behavior varies between listeners (some addressees are more active than others) and (2) backchanneling behavior varies between BOPs (some points trigger more responses than others). We discuss the relevance of these results for models of human–human and human–machine interactions.

show abstract

Section: Current Worksupporting

confidence: 92%

“…In a follow-up experiment, we showed that the type of backchannel behavior indeed influences the personality perception of the listener. Listeners who produced head nods with a bigger amplitude are, for example, perceived as being more extraverted, compared to listeners whose head nods are smaller (Blomsma et al, 2022).…”

Section: Variability Between Addresseesmentioning

confidence: 99%

Backchannel behavior is idiosyncratic

Blomsma,

Vaitonyté,

Skantze

et al. 2024

Lang. cogn.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Non-verbal cues also play a significant role in the perception of personality [6]. This has been shown in studies of gaze [7], back channels [8] and body language cues [9].…”

Section: Introductionmentioning

confidence: 85%

Perceived personality state estimation in dyadic and small group interaction with deep learning methods

Fenech¹,

Fodor²,

Bergeron³

et al. 2022

Preprint

View full text Add to dashboard Cite

Dyadic and small group collaboration is an evolutionary advantageous behaviour and the need for such collaboration is a regular occurrence in day to day life. In this paper we estimate the perceived personality traits of individuals in dyadic and small groups over thin-slices of interaction on four multimodal datasets. We find that our transformer based predictive model performs similarly to human annotators tasked with predicting the perceived big-five personality traits of participants. Using this model we analyse the estimated perceived personality traits of individuals performing tasks in small groups and dyads. Permutation analysis shows that in the case of small groups undergoing collaborative tasks, the perceived personality of group members clusters, this is also observed for dyads in a collaborative problem solving task, but not in dyads under non-collaborative task settings. Additionally, we find that the group level average perceived personality traits provide a better predictor of group performance than the group level average self-reported personality traits.

show abstract

“…Different backchannel formulation methods have been implemented and tested in HRI, from copying the actual backchannels of a human listener in the same conversation ( Gratch et al., 2007 ; Poppe et al., 2011 ; Blomsma et al., 2022 ), over learning from observing human listeners and formulating rules ( Gratch et al., 2007 ; Al Moubayed et al., 2009 ) or from data using machine learning approaches ( Adiba et al., 2021 ), to hand-crafted backchannels deemed suitable for the robot at hand ( Park et al., 2017 ; Murray et al., 2022 ). Backchannels specifically created for robots include head nods ( Fujie et al., 2004 ; Al Moubayed et al., 2009 ; Park et al., 2017 ; Murray et al., 2022 ), gaze and eyebrow movement ( Park et al., 2017 ), smiling ( Al Moubayed et al., 2009 ; Park et al., 2017 ) and short utterances ( Fujie et al., 2004 ; Park et al., 2017 ; Sebo et al., 2020 ).…”

Section: Previous Workmentioning

confidence: 99%

“…Natural backchanneling is also challenging for conversational agents and robots and numerous studies ( Gratch et al., 2007 ; Al Moubayed et al., 2009 ; Poppe et al., 2011 ; Inden et al., 2013 ; Hussain et al., 2019 ; Sebo et al., 2020 ; Adiba et al., 2021 ; Blomsma et al., 2022 ; Murray et al., 2022 ) have been devoted to endowing social robots and agents with a human-like and/or adequate backchannel strategy. It has been shown that the robot’s backchanneling affects how human subjects perceive it ( Gratch et al., 2007 ; Blomsma et al., 2022 ) and it is thus a key factor for successful spoken human-robot interaction (HRI).…”

Section: Introductionmentioning

confidence: 99%

Socio-cultural perception of robot backchannels

2023

View full text Add to dashboard Cite

Introduction: Backchannels, i.e., short interjections by an interlocutor to indicate attention, understanding or agreement regarding utterances by another conversation participant, are fundamental in human-human interaction. Lack of backchannels or if they have unexpected timing or formulation may influence the conversation negatively, as misinterpretations regarding attention, understanding or agreement may occur. However, several studies over the years have shown that there may be cultural differences in how backchannels are provided and perceived and that these differences may affect intercultural conversations. Culturally aware robots must hence be endowed with the capability to detect and adapt to the way these conversational markers are used across different cultures. Traditionally, culture has been defined in terms of nationality, but this is more and more considered to be a stereotypic simplification. We therefore investigate several socio-cultural factors, such as the participants’ gender, age, first language, extroversion and familiarity with robots, that may be relevant for the perception of backchannels.Methods: We first cover existing research on cultural influence on backchannel formulation and perception in human-human interaction and on backchannel implementation in Human-Robot Interaction. We then present an experiment on second language spoken practice, in which we investigate how backchannels from the social robot Furhat influence interaction (investigated through speaking time ratios and ethnomethodology and multimodal conversation analysis) and impression of the robot (measured by post-session ratings). The experiment, made in a triad word game setting, is focused on if activity-adaptive robot backchannels may redistribute the participants’ speaking time ratio, and/or if the participants’ assessment of the robot is influenced by the backchannel strategy. The goal is to explore how robot backchannels should be adapted to different language learners to encourage their participation while being perceived as socio-culturally appropriate.Results: We find that a strategy that displays more backchannels towards a less active speaker may substantially decrease the difference in speaking time between the two speakers, that different socio-cultural groups respond differently to the robot’s backchannel strategy and that they also perceive the robot differently after the session.Discussion: We conclude that the robot may need different backchanneling strategies towards speakers from different socio-cultural groups in order to encourage them to speak and have a positive perception of the robot.

show abstract

Backchannel Behavior Influences the Perceived Personality of Human and Artificial Communication Partners

Cited by 6 publications

References 33 publications

Backchannel behavior is idiosyncratic

Backchannel behavior is idiosyncratic

Perceived personality state estimation in dyadic and small group interaction with deep learning methods

Socio-cultural perception of robot backchannels

Contact Info

Product

Resources

About