Breaking Fresh Ground in Humanâ€“Media Interaction Research

Nijholt, Anton

doi:10.3389/fict.2014.00004

Cited by 10 publications

(10 citation statements)

References 11 publications

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“…Beyond the scripted and one-way social touch cues employed in the examples above, human-computer interaction applications increasingly deploy intelligent agents to support the social aspects of the interaction (Nijholt 2014). Social agents are used to communicate, express, and perceive emotions, maintain social relationships, interpret natural cues, and develop social competencies (Fong et al 2003;Li et al 2011).…”

Section: Generating Social Touch Signalsmentioning

confidence: 99%

Social Touch in Humanâ€“Computer Interaction

Erp

Toet²

2015

Front. Digit. Humanit.

144

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Touch is our primary non-verbal communication channel for conveying intimate emotions and as such essential for our physical and emotional wellbeing. In our digital age, human social interaction is often mediated. However, even though there is increasing evidence that mediated touch affords affective communication, current communication systems (such as videoconferencing) still do not support communication through the sense of touch. As a result, mediated communication does not provide the intense affective experience of co-located communication. The need for ICT mediated or generated touch as an intuitive way of social communication is even further emphasized by the growing interest in the use of touch-enabled agents and robots for healthcare, teaching, and telepresence applications. Here, we review the important role of social touch in our daily life and the available evidence that affective touch can be mediated reliably between humans and between humans and digital agents. We base our observations on evidence from psychology, computer science, sociology, and neuroscience with focus on the first two. Our review shows that mediated affective touch can modulate physiological responses, increase trust and affection, help to establish bonds between humans and avatars or robots, and initiate pro-social behavior. We argue that ICT mediated or generated social touch can (a) intensify the perceived social presence of remote communication partners and (b) enable computer systems to more effectively convey affective information. However, this research field on the crossroads of ICT and psychology is still embryonic and we identify several topics that can help to mature the field in the following areas: establishing an overarching theoretical framework, employing better research methodologies, developing basic social touch building blocks, and solving specific ICT challenges.

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Section: Generating Social Touch Signalsmentioning

confidence: 99%

Social Touch in Humanâ€“Computer Interaction

Erp

Toet²

2015

Front. Digit. Humanit.

144

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“…It provides a new and complex human-computer interaction paradigm (Nijholt, 2014), since users are no longer "external observers of images on a computer screen but are active participants in a computer-generated three-dimensional (3D) world" (Bowman and Hodges, 1999, p. 37). With VR applications, ecologically valid training and therapy scenarios can be presented that otherwise are hard to realize (for example, for training a presentation in front of a large audience or an audience with a different cultural background).…”

mentioning

confidence: 99%

Virtual Reality Training for Public Speaking—A QUEST-VR Framework Validation

Poeschl

2017

Front. ICT

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Good public speaking skills are essential in many professions as well as everyday life, but speech anxiety is a common problem. While it is established that public speaking training in virtual reality (VR) is effective, comprehensive studies on the underlying factors that contribute to this success are rare. The "quality evaluation of user-system interaction in virtual reality" framework for evaluation of VR applications is presented that includes system features, user factors, and moderating variables. Based on this framework, variables that are postulated to influence the quality of a public speaking training application were selected for a first validation study. In a cross-sectional, repeated measures laboratory study [N = 36 undergraduate students; 36% men, 64% women, mean age = 26.42 years (SD = 3.42)], the effects of task difficulty (independent variable), ability to concentrate, fear of public speaking, and social presence (covariates) on public speaking performance (dependent variable) in a virtual training scenario were analyzed, using stereoscopic visualization on a screen. The results indicate that the covariates moderate the effect of task difficulty on speech performance, turning it into a non-significant effect. Further interrelations are explored. The presenter's reaction to the virtual agents in the audience shows a tendency of overlap of explained variance with task difficulty. This underlines the need for more studies dedicated to the interaction of contributing factors for determining the quality of VR public speaking applications.

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“…From a technological point of view, the main interest of the findings above is that social and psychological phenomena that cannot be observed and accessed directly can still be inferred from physical traces -the non-verbal cues and their frequency -that can be sensed and detected automatically. In this respect, the analysis presented in this work provides a solid ground for domains like social signal processing (Vinciarelli et al, 2012), computational paralinguistics (Schuller and Batliner, 2013), or human-media interaction (Nijholt, 2014) that aim at making machines socially intelligent, i.e., capable to understand social interactions in the same terms as humans do. In particular, the observations suggest that it is possible to develop automatic approaches for the inference of the factors the cues account for (e.g., mode of interaction, topic of conversation, conflict handling style, etc.).…”

Section: Resultsmentioning

confidence: 99%

“…Before participating in the experiment, the two subjects involved in the same call are asked to look at a list of 12 items and decide, for each of them, whether it increases the chances of survival in a polar environment or not. In this way, it is possible to know whether the two subjects agree (they have made the same decision) or disagree (they have made a different decision) about an item, given that agreement can be defined as "a relation of identity, similarity or congruence between the opinions of two or more persons" (Poggi et al, 2011). During the call, the two subjects are asked to discuss the items sequentially, one at a time, and to reach a consensual decision for each of them.…”

Section: Mode Of Interaction Effects: Agreement Vs Disagreementmentioning

confidence: 99%

“…The rationale behind the choice of the cues above is that they tend to appear frequently in conversations (see Section 2) and this is probably an indication of their primacy in humanhuman communication. Furthermore, the five cues are the subject of extensive work in social signal processing (Vinciarelli et al, 2012), computational paralinguistics (Schuller and Batliner, 2013), and human-media interaction (Nijholt, 2014), three computing domains involving automatic detection and interpretation of nonverbal behavioral cues. In this respect, the findings of this work can be helpful for automatic approaches aimed at automatically making sense of social interactions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

When the Words are Not Everything: The Use of Laughter, Fillers, Back-Channel, Silence, and Overlapping Speech in Phone Calls

Vinciarelli

Chatziioannou

Esposito³

2015

Front. ICT

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This article presents an observational study on how some common conversational cueslaughter, fillers, back-channel, silence, and overlapping speech-are used during mobile phone conversations. The observations are performed over the SSPNet Mobile Corpus, a collection of 60 calls between pairs of unacquainted individuals (120 subjects for roughly 12 h of material in total). The results show that the temporal distribution of the social signals above is not uniform, but it rather reflects the social meaning they carry and convey. In particular, the results show significant use differences depending on factors such as gender, role (caller or receiver), topic, mode of interaction (agreement or disagreement), personality traits, and conflict handling style.

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Breaking Fresh Ground in Humanâ€“Media Interaction Research

Cited by 10 publications

References 11 publications

Social Touch in Humanâ€“Computer Interaction

Social Touch in Humanâ€“Computer Interaction

Virtual Reality Training for Public Speaking—A QUEST-VR Framework Validation

When the Words are Not Everything: The Use of Laughter, Fillers, Back-Channel, Silence, and Overlapping Speech in Phone Calls

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