The Good, The Bad, The Weird: Audience Evaluation of a “Real” Robot in Relation to Science Fiction and Mass Media

Bruckenberger, Ulrike; Weiss, Astrid; Mirnig, Nicole; Strasser, Ewald; Stadler, Susanne; Tscheligi, Manfred

doi:10.1007/978-3-319-02675-6_30

Cited by 39 publications

(30 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…. "leads to "weird", double-minded feelings towards real robots [56]". Others underline that the ontological categories that people would habitually use to classify objects are challenged when confronted with social robots [17,57].…”

Section: Discussionmentioning

confidence: 99%

What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life

Damholdt

Vestergaard

Kryvous

et al. 2019

Paladyn, Journal of Behavioral Robotics

View full text Add to dashboard Cite

We explore the impressions and conceptualisations produced by participants after their first encounter with the teleoperated robot, Telenoid R1.Participants were invited to freely report the first three words that came to mind after seeing the robot. Here we triangulate (i) three-word data from an online survey (n=340) where respondents saw a brief video of the Telenoid with (ii) three-word data from an interaction study where participants interacted with a physically present Telenoid (n=75) and, (iii) data from qualitative interviews (n=7) with participants who had engaged with the Telenoid. Data were subjected to sentiment analysis, linguistic analysis and regression analysis.Ranking of the most frequently produced words in the two groups revealed an overlap on the top-10 produced words (6 out of 10 words). Sentiment analysis and regression revealed an association between negative predicates and the online condition. Sentiments were not convincingly associated with age or gender. Linguistic categorisations of the data revealed that especially adjectives expressing response-dependent features were frequent. We did not find any consistent statistical effect on categorising the words into cognitive and emotional predicates.The proposed three-word method offers, unguided approach to explore initial conceptualisations of robots.

show abstract

Section: Discussionmentioning

confidence: 99%

What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life

Damholdt

Vestergaard

Kryvous

et al. 2019

Paladyn, Journal of Behavioral Robotics

View full text Add to dashboard Cite

show abstract

“…Despite this high relevance, very little is known about what people expect and, furthermore, often no distinction was made between what people actually expect and what they prefer. Thus, expectancy reports are usually mixed with preferences and were never deliberately contrasted before (e.g., Arras and Cerqui, 2005; Bruckenberger et al, 2013). Additionally, to understand people’s expectancies more thoroughly, mechanisms influencing the development of these expectancies should be considered.…”

Section: Introductionmentioning

confidence: 99%

“…One pivotal question is, what sources of information people could possibly use to reduce their uncertainty and to form their expectancies regarding social robots. Here, science fiction is assumed to have a substantive influence since social robots are currently not very widespread and only few people come in contact with social robots (e.g., Bartneck, 2004; Bruckenberger et al, 2013; Sandoval et al, 2014). An additional question is, how people’s technical affinity and their locus of control when using technologies influence their formation of expectations.…”

Section: Introductionmentioning

confidence: 99%

Great Expectations? Relation of Previous Experiences With Social Robots in Real Life or in the Media and Expectancies Based on Qualitative and Quantitative Assessment

Horstmann

Krämer

2019

Front. Psychol.

View full text Add to dashboard Cite

Social robots, which mostly look and behave like humans, are often perceived as somehow alive and treated similar to humans, despite the fact that they are non-living electronic devices. Based on considerations of the uncertainty reduction theory, the question arises what expectancies regarding social robots people have and what sources they use to achieve these expectancies. To receive an in-depth understanding of people’s expectancies regarding social robots and particularly how these expectancies are influenced by people’s experiences with real robots but also with fictional robots from media, thirteen semi-structured interviews and a quantitative online study ( n = 433) were conducted. Results indicate that people’s experiences with robots in the media lead to high expectations regarding the skills of robots, which in turn increase people’s general expectancies regarding social robots being part of the society as well as their personal lives. Furthermore, knowledge of negatively perceived fictional robots increases negative expectancies of robots becoming a threat to humans, while technical affinity reduces general robot anxiety.

show abstract

“…Aronson et al (1966) suggest that superior people may be viewed as superhuman and distant while a mistake would make them seem more human. Similarly, one could argue that robots are often seen as impeccable, since this is how they are presented in the media (Bruckenberger et al, 2013). Especially, people who have not interacted with robots themselves build their mental models and expectations about robots from those media.…”

Section: Introductionmentioning

confidence: 99%

To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot

et al. 2017

Self Cite

View full text Add to dashboard Cite

We conducted a user study for which we purposefully programmed faulty behavior into a robot's routine. It was our aim to explore if participants rate the faulty robot different from an error-free robot and which reactions people show in interaction with a faulty robot. The study was based on our previous research on robot errors where we detected typical error situations and the resulting social signals of our participants during social human-robot interaction. In contrast to our previous work, where we studied video material in which robot errors occurred unintentionally, in the herein reported user study, we purposefully elicited robot errors to further explore the human interaction partners' social signals following a robot error. Our participants interacted with a human-like NAO, and the robot either performed faulty or free from error. First, the robot asked the participants a set of predefined questions and then it asked them to complete a couple of LEGO building tasks. After the interaction, we asked the participants to rate the robot's anthropomorphism, likability, and perceived intelligence. We also interviewed the participants on their opinion about the interaction. Additionally, we video-coded the social signals the participants showed during their interaction with the robot as well as the answers they provided the robot with. Our results show that participants liked the faulty robot significantly better than the robot that interacted flawlessly. We did not find significant differences in people's ratings of the robot's anthropomorphism and perceived intelligence. The qualitative data confirmed the questionnaire results in showing that although the participants recognized the robot's mistakes, they did not necessarily reject the erroneous robot. The annotations of the video data further showed that gaze shifts (e.g., from an object to the robot or vice versa) and laughter are typical reactions to unexpected robot behavior. In contrast to existing research, we assess dimensions of user experience that have not been considered so far and we analyze the reactions users express when a robot makes a mistake. Our results show that decoding a human's social signals can help the robot understand that there is an error and subsequently react accordingly.

show abstract

The Good, The Bad, The Weird: Audience Evaluation of a “Real” Robot in Relation to Science Fiction and Mass Media

Cited by 39 publications

References 9 publications

What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life

What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life

Great Expectations? Relation of Previous Experiences With Social Robots in Real Life or in the Media and Expectancies Based on Qualitative and Quantitative Assessment

To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot

Contact Info

Product

Resources

About