Face the Uncanny: The Effects of Doppelganger Talking Head Avatars on Affect-Based Trust Toward Artificial Intelligence Technology are Mediated by Uncanny Valley Perceptions

Fluid face but not gender: Enfacement illusion through digital face filters does not affect gender identity

It has been shown that observing a face being touched or moving in synchrony with our own face increases self-identification with the former which might alter both cognitive and affective processes. The induction of this phenomenon, termed enfacement illusion, has often relied on laboratory tools that are unavailable to a large audience. However, digital face filters applications are nowadays regularly used and might provide an interesting tool to study similar mechanisms in a wider population. Digital filters are able to render our faces in real time while changing important facial features, for example, rendering them more masculine or feminine according to normative standards. Recent literature using full-body illusions has shown that participants' own gender identity shifts when embodying a different gendered avatar. Here we studied whether participants' filtered faces, observed while moving in synchrony with their own face, may induce an enfacement illusion and if so, modulate their gender identity. We collected data from 35 female and 33 male participants who observed a stereotypically gender mismatched version of themselves either moving synchronously or asynchronously with their own face on a screen. Our findings showed a successful induction of the enfacement illusion in the synchronous condition according to a questionnaire addressing the feelings of ownership, agency and perceived similarity. However, we found no evidence of gender identity being modulated, neither in explicit nor in implicit measures of gender identification. We discuss the distinction between full-body and facial processing and the relevance of studying widely accessible devices that may impact the sense of a bodily self and our cognition, emotion and behaviour.

Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: a systematic review

PURPOSE

We present a systematic literature review of dialogue agents for Artificial Intelligence (AI) and agent-based conversational systems dealing with cognitive disability of aged and impaired people including dementia and Parkinson's disease. We analyze current applications, gaps, and challenges in the existing research body, and provide guidelines and recommendations for their future development and use.

MATERIALS AND METHODS

We perform this study by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We performed a systematic search using relevant databases (ACM Digital Library, Google Scholar, IEEE Xplore, PubMed, and Scopus).

RESULTS

This study identified 468 articles on the use of conversational agents in healthcare. We finally selected 124 articles based on their objectives and content as directly related to our main topic.

CONCLUSION

We identified the main challenges in the field and analyzed the typical examples of the application of conversational agents in the healthcare domain, the desired characteristics of conversational agents, and chatbot support for aged people and people with cognitive disabilities. Our results contribute to a discussion on conversational health agents and emphasize current knowledge gaps and challenges for future research.IMPLICATIONS FOR REHABILITATIONA systematic literature review of dialogue agents for artificial intelligence and agent-based conversational systems dealing with cognitive disability of aged and impaired people.Main challenges and desired characteristics of the conversational agents, and chatbot support for aged people and people with cognitive disability.Current knowledge gaps and challenges for remote healthcare and rehabilitation.Guidelines and recommendations for future development and use of conversational systems.

Using deepfakes for experiments in the social sciences - A pilot study

The advent of deepfakes - the manipulation of audio records, images and videos based on deep learning techniques - has important implications for science and society. Current studies focus primarily on the detection and dangers of deepfakes. In contrast, less attention is paid to the potential of this technology for substantive research - particularly as an approach for controlled experimental manipulations in the social sciences. In this paper, we aim to fill this research gap and argue that deepfakes can be a valuable tool for conducting social science experiments. To demonstrate some of the potentials and pitfalls of deepfakes, we conducted a pilot study on the effects of physical attractiveness on student evaluations of teachers. To this end, we created a deepfake video varying the physical attractiveness of the instructor as compared to the original video and asked students to rate the presentation and instructor. First, our results show that social scientists without special knowledge in computer science can successfully create a credible deepfake within reasonable time. Student ratings of the quality of the two videos were comparable and students did not detect the deepfake. Second, we use deepfakes to examine a substantive research question: whether there are differences in the ratings of a physically more and a physically less attractive instructor. Our suggestive evidence points toward a beauty penalty. Thus, our study supports the idea that deepfakes can be used to introduce systematic variations into experiments while offering a high degree of experimental control. Finally, we discuss the feasibility of deepfakes as an experimental manipulation and the ethical challenges of using deepfakes in experiments.

The deviation-from-familiarity effect: Expertise increases uncanniness of deviating exemplars

Humanlike entities deviating from the norm of human appearance are perceived as strange or uncanny. Explanations for the eeriness of deviating humanlike entities include ideas specific to human or animal stimuli like mate selection, avoidance of threat or disease, or dehumanization; however, deviation from highly familiar categories may provide a better explanation. Here it is tested whether experts and novices in a novel (greeble) category show different patterns of abnormality, attractiveness, and uncanniness responses to distorted and averaged greebles. Greeble-trained participants assessed the abnormality, attractiveness, uncanniness of normal, averaged, and distorted greebles and their responses were compared to participants who had not previously seen greebles. The data show that distorted greebles were more uncanny than normal greebles only in the training condition, and distorted greebles were more uncanny in the training compared to the control condition. In addition, averaged greebles were not more attractive than normal greebles regardless of condition. The results suggest uncanniness is elicited by deviations from stimulus categories of expertise rather than being a purely biological human- or animal-specific response.

Fabulation, Machine Agents, and Spiritually Authorizing Encounters

This paper uses a Tavesian model of religious experience to make a modest theorization about the role of “fabulation”, an embodied and affective process, to understand how some contemporary AI and robotics designers and users consider encounters with these technologies to be spiritually “authorizing”. By “fabulation”, we mean the Bergsonian concept of an evolved capacity that allows humans to see the potentialities of complex action within another object—in other words, an interior agential image, or “soul”; and by “authorizing”, we mean “deemed as having some claim to arbitration, persuasion, and legitimacy” such that the user might make choices that affect their life or others in accordance with the AI or might have their spiritual needs met. We considered two case studies where this agency took on a spiritual or religious valence when contextualized as such for the user: a robotic Buddhist priest known as Mindar, and a chatbot called The Spirituality Chatbot. We show how understanding perceptions of AI or robots as being spiritual or religious in a way that authorizes behavioral changes requires understanding tendencies of the human body more so than it does any metaphysical nature of the technology itself.