Autistic traits, personality, and evaluations of humanoid robots by young and older adults

Research Hotspots and Trends of Social Robot Interaction Design: A Bibliometric Analysis

(1) Background: Social robot interaction design is crucial for determining user acceptance and experience. However, few studies have systematically discussed the current focus and future research directions of social robot interaction design from a bibliometric perspective. Therefore, we conducted this study in order to identify the latest research progress and evolution trajectory of research hotspots in social robot interaction design over the last decade. (2) Methods: We conducted a comprehensive review based on 2416 papers related to social robot interaction design obtained from the Web of Science (WOS) database. Our review utilized bibliometric techniques and integrated VOSviewer and CiteSpace to construct a knowledge map. (3) Conclusions: The current research hotspots of social robot interaction design mainly focus on #1 the study of human-robot relationships in social robots, #2 research on the emotional design of social robots, #3 research on social robots for children's psychotherapy, #4 research on companion robots for elderly rehabilitation, and #5 research on educational social robots. The reference co-citation analysis identifies the classic literature that forms the basis of the current research, which provides theoretical guidance and methods for the current research. Finally, we discuss several future research directions and challenges in this field.

Perceived naturalness of emotional voice morphs

Research into voice perception benefits from manipulation software to gain experimental control over acoustic expression of social signals such as vocal emotions. Today, parameter-specific voice morphing allows a precise control of the emotional quality expressed by single vocal parameters, such as fundamental frequency (F0) and timbre. However, potential side effects, in particular reduced naturalness, could limit ecological validity of speech stimuli. To address this for the domain of emotion perception, we collected ratings of perceived naturalness and emotionality on voice morphs expressing different emotions either through F0 or Timbre only. In two experiments, we compared two different morphing approaches, using either neutral voices or emotional averages as emotionally non-informative reference stimuli. As expected, parameter-specific voice morphing reduced perceived naturalness. However, perceived naturalness of F0 and Timbre morphs were comparable with averaged emotions as reference, potentially making this approach more suitable for future research. Crucially, there was no relationship between ratings of emotionality and naturalness, suggesting that the perception of emotion was not substantially affected by a reduction of voice naturalness. We hold that while these findings advocate parameter-specific voice morphing as a suitable tool for research on vocal emotion perception, great care should be taken in producing ecologically valid stimuli.

Who Likes Artificial Intelligence? Personality Predictors of Attitudes toward Artificial Intelligence

We examined how individuals' personality relates to various attitudes toward artificial intelligence (AI). Attitudes were organized into two dimensions of affective components (positive and negative emotions) and two dimensions of cognitive components (sociality and functionality). For personality, we focused on the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, openness) and personal innovativeness in information technology. Based on a survey of 1,530 South Korean adults, we found that extraversion was related to negative emotions and low functionality. Agreeableness was associated with both positive and negative emotions, and it was positively associated with sociality and functionality. Conscientiousness was negatively related to negative emotions, and it was associated with high functionality, but also with low sociality. Neuroticism was related to negative emotions, but also to high sociality. Openness was positively linked to functionality, but did not predict other attitudes when other proximal predictors were included (e.g. prior use, personal innovativeness). Personal innovativeness in information technology consistently showed positive attitudes toward AI across all four dimensions. These findings provide mixed support for our hypotheses, and we discuss specific implications for future research and practice.

Applying Service-Dominant Logic and Conversation Management Principles to Social Robotics for Autism Spectrum Disorder

Anthropomorphism in social robots amplifies the big five human personality traits (extraversion, conscientiousness, neuroticism, openness to experience, and agreeableness), consequently aiding with the social motivation needs of consumers (especially, individuals with Autism Spectrum Disorder or ASD). According to the social motivation theory of autism, consumers with ASD show deficits in orienting toward social stimuli, engaging with humans, and maintaining social relations. Robotic anthropomorphism has been found to be positively related to the big five human personality types and robot likeability in human-robot interaction (HRI) situations. This research focuses on the conversational approach of social robotics using service dominant approach. Conversational-based perspectives have been studied extensively in organizational and management literature, however, these perspectives have not been utilized in the context of social robots, HRI, and autism.

Do People Regard Robots as Human-Like Social Partners? Evidence From Perspective-Taking in Spatial Descriptions

Spatial communications are essential to the survival and social interaction of human beings. In science fiction and the near future, robots are supposed to be able to understand spatial languages to collaborate and cooperate with humans. However, it remains unknown whether human speakers regard robots as human-like social partners. In this study, human speakers describe target locations to an imaginary human or robot addressee under various scenarios varying in relative speaker–addressee cognitive burden. Speakers made equivalent perspective choices to human and robot addressees, which consistently shifted according to the relative speaker–addressee cognitive burden. However, speakers’ perspective choice was only significantly correlated to their social skills when the addressees were humans but not robots. These results suggested that people generally assume robots and humans with equal capabilities in understanding spatial descriptions but do not regard robots as human-like social partners.