Nicole Kramer

Internet users have access to a multitude of science-related information – on journalistic news sites but also on blogs with user-generated content. In this context, we investigated in two studies the factors which influence laypersons’ selective exposure. In an experiment with a collection of online news, parents were asked to search for information about the controversy surrounding violence in the media. Texts from high-reputation sources were clicked on more frequently – regardless of content –, whereas ratings by others had limited effects. In a second experiment, the expertise and gender of blog authors as well as valence and number of ratings were varied. In this setting, texts from sources with positive ratings were read for longer. Results show that laypersons make use of credibility cues when deciding which articles to read. For online news sites, media reputation is most important, whereas in blogs, ratings are taken into account more frequently.

The study of human-human communication and the development of computational models for human-agent communication have diverged significantly throughout the last decade. Yet, despite frequently made claims of “super-human performance” in, e.g., speech recognition or image processing, so far, no system is able to lead a half-decent coherent conversation with a human. In this paper, we argue that we must start to re-consider the hallmarks of cooperative communication and the core capabilities that we have developed for it, and which conversational agents need to be equipped with: incremental joint co-construction and mentalizing. We base our argument on a vast body of work on human-human communication and its psychological processes that we reason to be relevant and necessary to take into account when modeling human-agent communication. We contrast those with current conceptualizations of human-agent interaction and formulate suggestions for the development of future systems.

The aim of this chapter is to provide a framework which structures different aspects that might positively and negatively influence the sensations nonliving sexual partners might evoke in order to guide future empirical research in the investigation of sexual responses toward machines. For this purpose, influential concepts from media psychology, human–machine interaction, and sexual science are explained and transferred to interactions with sex robots. This theoretical foundation is then used to develop the sexual interaction illusion model, which aims to conceptualize factors that are shaping users’ psychological immersion in sexual interaction with technology-based sex partners. More specifically, the model focuses on understanding users’ subjective experience that the interaction with an artificial partner feels like a sexual interaction with an existing, living social being.

Based on its prevalence, there is an urgent need to better understand the mechanisms, opportunities and risks of sexual interaction in digital contexts that are related with sexual arousal. While there is a growing body of literature on SIDC, there is also a lack of conceptual clarity and classification. Therefore, based on a conceptual analysis, we propose to distinguish between sexual interaction through, via, and with digital technologies. Sexual interactions through digital technologies are face-to-face sexual interactions that have been started digitally or are accompanied by digital technology. Sexual interactions via digital technology are technology-mediated interpersonal sexual interactions. Sexual interactions with digital technology occur when the technology itself has the role of an interaction partner. The three types of SIDC and their respective subtypes are explained and backed up with empirical studies that are grouped according to two major mediators: consent and commerce. Regarding the causes and consequences of the three types of SIDC we suggest a classification that entails biological, psychological, social, economic, and technological factors. Regarding implications of SIDC we suggest to focus on both opportunities and risks for sexual health. The proposed conceptual framework of SIDC is meant to inform future research.

Robots are used in various social interactions that require them to be perceived as credible agents. To be rated credible a robot’s mentalizing abilities have shown to be beneficial because they allow a robot to infer users’ inner states, thus serving as a prerequisite for understanding their beliefs and attitudes. However, social robots are often deployed by private and thus profit-oriented companies. In such cases where an organization’s implied manipulative intent is salient, the effect of robots’ mentalizing abilities might be reversed. The reason for this is that mentalizing abilities could pose a persuasive threat to users rather than a feature for better understanding, thereby decreasing credibility attributions. These assumptions were tested in a three by two between-subjects, pre-registered, laboratory experiment during which participants interacted with a social robot that recommended experience vouchers as potential gifts for participants’ target persons. Contrary to our assumptions, inferential statistical results revealed no significant differences in explicit or indirect credibility attributions caused by the experimental manipulation. The external manipulative intent of an organization using the robot caused no differences in participants’ behavioral intentions or evaluations of it. Furthermore, only participants’ attribution of empathic understanding to the robot varied significantly between the three mentalizing conditions. Our results suggest that people focus more on the robot than on the organization using it, causing potential opportunities for such organizations to hide their economic interests from the users.