The topography of the uncanny valley and individuals’ need for structure: A nonlinear mixed effects analysis

From Freud to Android: Constructing a Scale of Uncanny Feelings

The uncanny valley is a topic for engineers, animators, and psychologists, yet uncanny emotions are without a clear definition. Across three studies, we developed an 8-item measure of unnerved feelings, finding that it was discriminable from other affective experiences. In Study 1, we conducted an exploratory factor analysis that yielded two factors; an unnerved factor, which connects to emotional reactions to the uncanny, and a disoriented factor, which connects to mental state changes more distally following uncanny experiences. Focusing on the unnerved measure, Study 2 tests the scale's convergent and discriminant validity, concluding that participants who watched an uncanny video were more unnerved than those who watched a disgusting, fearful, or a neutral video. In Study 3, we determined that our scale detects unnerved feelings created during early 2020 by the coronavirus pandemic; a distinct source of uncanniness. These studies contribute to the psychological and interdisciplinary understanding of this strange, eerie phenomenon of being confronted with what looms just beyond our understanding.

Robot Voices in Daily Life: Vocal Human-Likeness and Application Context as Determinants of User Acceptance

The growing popularity of speech interfaces goes hand in hand with the creation of synthetic voices that sound ever more human. Previous research has been inconclusive about whether anthropomorphic design features of machines are more likely to be associated with positive user responses or, conversely, with uncanny experiences. To avoid detrimental effects of synthetic voice design, it is therefore crucial to explore what level of human realism human interactors prefer and whether their evaluations may vary across different domains of application. In a randomized laboratory experiment, 165 participants listened to one of five female-sounding robot voices, each with a different degree of human realism. We assessed how much participants anthropomorphized the voice (by subjective human-likeness ratings, a name-giving task and an imagination task), how pleasant and how eerie they found it, and to what extent they would accept its use in various domains. Additionally, participants completed Big Five personality measures and a tolerance of ambiguity scale. Our results indicate a positive relationship between human-likeness and user acceptance, with the most realistic sounding voice scoring highest in pleasantness and lowest in eeriness. Participants were also more likely to assign real human names to the voice (e.g., “Julia” instead of “T380”) if it sounded more realistic. In terms of application context, participants overall indicated lower acceptance of the use of speech interfaces in social domains (care, companionship) than in others (e.g., information & navigation), though the most human-like voice was rated significantly more acceptable in social applications than the remaining four. While most personality factors did not prove influential, openness to experience was found to moderate the relationship between voice type and user acceptance such that individuals with higher openness scores rated the most human-like voice even more positively. Study results are discussed in the light of the presented theory and in relation to open research questions in the field of synthetic voice design.

A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity , and eeriness , and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance , and viewing duration . This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research.

Human-Like Robots and the Uncanny Valley

Abstract. In the field of human-robot interaction, the well-known uncanny valley hypothesis proposes a curvilinear relationship between a robot’s degree of human likeness and the observers’ responses to the robot. While low to medium human likeness should be associated with increased positive responses, a shift to negative responses is expected for highly anthropomorphic robots. As empirical findings on the uncanny valley hypothesis are inconclusive, we conducted a random-effects meta-analysis of 49 studies (total N = 3,556) that reported 131 evaluations of robots based on the Godspeed scales for anthropomorphism (i.e., human likeness) and likeability. Our results confirm more positive responses for more human-like robots at low to medium anthropomorphism, with moving robots rated as more human-like but not necessarily more likable than static ones. However, because highly anthropomorphic robots were sparsely utilized in previous studies, no conclusions regarding proposed adverse effects at higher levels of human likeness can be made at this stage.

Exploring the Perceptions of Cognitive and Affective Capabilities of Four, Real, Physical Robots with a Decreasing Degree of Morphological Human Likeness

This paper describes an investigation of student perceptions of the cognitive and affective capabilities of four robots that have a decreasing degree of morphological human likeness. We showed and illustrated the robots (i.e., InMoov, Padbot, Joy Robot and Turtlebot) to 62 students. After showing the students each of these robots, and explaining their main features and capabilities, we administered a fill-in questionnaire to the students. Our main hypothesis was that the perception of a robot’s cognitive and affective capabilities varied in correspondence with their appearance and in particular with their different degree of human likeness. The main results of this study indicate that the scores attributed to the cognitive and emotional capabilities of these robots are not modulated correspondingly to their different morphological similarity to humans. Furthermore, overall, the scores given to all of these robots regarding their ability to explicate mental functions are low, and even lower scores are given to their ability to feel emotions. There is a split between InMoov, the robot which has the highest degree of human likeness, and all of the others. Our results also indicate that: (1) morphological similarity of a robot to humans is not perceived automatically as such by observers, which is not considered a value in itself for the robot; and (2) even at lower levels of robot–human likeness, an uncanny valley effect arises but is quite mitigated by curiosity.

Perceived match between own and observed models' bodies: influence of face, viewpoints, and body size

People are generally unable to accurately determine their own body measurements and to translate this knowledge to identifying a model/avatar that best represents their own body. This inability has not only been related to health problems (e.g. anorexia nervosa), but has important practical implications as well (e.g. online retail). Here we aimed to investigate the influence of three basic visual features—face presence, amount of viewpoints, and observed model size—on the perceived match between own and observed models’ bodies and on attitudes towards these models. Models were real-life models (Experiment 1) or avatar models based on participants’ own bodies (Experiment 2). Results in both experiments showed a strong effect of model size, irrespective of participants’ own body measurements. When models were randomly presented one by one, participants gave significantly higher ratings to smaller- compared to bigger-sized models. The reverse was true, however, when participants observed and compared models freely, suggesting that the mode of presentation affected participants’ judgments. Limited evidence was found for an effect of facial presence or amount of viewpoints. These results add evidence to research on visual features affecting the ability to match observed bodies with own body image, which has biological, clinical, and practical implications.

Otakuism and the Appeal of Sex Robots

Social robots are becoming increasingly prevalent in everyday life and sex robots are a sub-category of especially high public interest and controversy. Starting from the concept of the otaku, a term from Japanese youth culture that describes secluded persons with a high affinity for fictional manga characters, we examine individual differences behind sex robot appeal (anime and manga fandom, interest in Japanese culture, preference for indoor activities, shyness). In an online-experiment, 261 participants read one out of three randomly assigned descriptions of future technologies (sex robot, nursing robot, genetically modified organism) and reported on their overall evaluation, eeriness, and contact/purchase intentions. Higher anime and manga fandom was associated with higher appeal for all three future technologies. For our male subsample, sex robots and GMOs stood out as shyness yielded a particularly strong relationship to contact/purchase intentions for these new technologies.