Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley

Discrepancies in perceived humanness between spatially filtered and unfiltered faces and their associations with uncanny feelings

Human and artificial features that coexist in certain types of human-like robots create a discrepancy in perceived humanness and evoke uncanny feelings in human observers. However, whether this perceptual mismatch in humanness occurs for all faces, and whether it is related to the uncanny feelings toward them, is unknown. We investigated this by examining perceived humanness for a variety of natural images of robot and human faces with different spatial frequency (SF) information: that is, faces with only low SF, middle SF, and high SF information, and intact (spatially unfiltered) faces. Uncanny feelings elicited by these faces were also measured. The results showed perceptual mismatches that LSF, MSF, and HSF faces were perceived as more human than intact faces. This was particularly true for intact robot faces that looked slightly human, which tended to evoke strong uncanny feelings. Importantly, the mismatch in perceived humanness between the intact and spatially filtered faces was positively correlated with uncanny feelings toward intact faces. Given that the human visual system performs SF analysis when processing faces, the perceptual mismatches observed in this study likely occur in real life for all faces, and as such might be a ubiquitous source of uncanny feelings in real-life situations.

A stimulus exposure of 50 ms elicits the uncanny valley effect

The uncanny valley (UV) effect captures the observation that artificial entities with near-human appearances tend to create feelings of eeriness. Researchers have proposed many hypotheses to explain the UV effect, but the visual processing mechanisms of the UV have yet to be fully understood. In the present study, we examined if the UV effect is as accessible in brief stimulus exposures compared to long stimulus exposures (Experiment 1). Forty-one participants, aged 21-31, rated each human-robot face presented for either a brief (50 ms) or long duration (3 s) in terms of attractiveness, eeriness, and humanness (UV indices) in a 7-point Likert scale. We found that brief and long exposures to stimuli generated a similar UV effect. This suggests that the UV effect is accessible at early visual processing. We then examined the effect of exposure duration on the categorisation of visual stimuli in Experiment 2. Thirty-three participants, aged 21-31, categorised faces as either human or robot in a two-alternative forced choice task. Their response accuracy and variance were recorded. We found that brief stimulus exposures generated significantly higher response variation and errors than the long exposure condition. This indicated that participants were more uncertain in categorising faces in the brief exposure condition due to insufficient time. Further comparisons between Experiment 1 and 2 revealed that the eeriest faces were not the hardest to categorise. Overall, these findings indicate (1) that both the UV effect and categorical uncertainty can be elicited through brief stimulus exposure, but (2) that categorical uncertainty is unlikely to cause the UV effect. These findings provide insights towards the perception of robotic faces and implications for the design of robots, androids, avatars, and artificial intelligence agents.

Emotional experiences of service robots' anthropomorphic appearance: a multimodal measurement method

Anthropomorphic appearance is a key factor to affect users' attitudes and emotions. This research aimed to measure emotional experience caused by robots' anthropomorphic appearance with three levels - high, moderate, and low - using multimodal measurement. Fifty participants' physiological and eye-tracker data were recorded synchronously while they observed robot images that were displayed in random order. Afterward, the participants reported subjective emotional experiences and attitudes towards those robots. The results showed that the images of the moderately anthropomorphic service robots induced higher pleasure and arousal ratings, and yielded significantly larger pupil diameter and faster saccade velocity, than did the low or high robots. Moreover, participants' facial electromyography, skin conductance, and heart-rate responses were higher when observing moderately anthropomorphic service robots. An implication of the research is that service robots' appearance should be designed to be moderately anthropomorphic; too many human-like features or machine-like features may disturb users' positive emotions and attitudes.Practitioner Summary: This research aimed to measure emotional experience caused by three types of anthropomorphic service robots using a multimodal measurement experiment. The results showed that moderately anthropomorphic service robots evoked more positive emotion than high and low anthropomorphic robots. Too many human-like features or machine-like features may disturb users' positive emotions.

Effects of Robot Animacy and Emotional Expressions on Perspective-Taking Abilities: A Comparative Study across Age Groups

The global population is inevitably aging due to increased life expectancy and declining birth rates, leading to an amplified demand for innovative social and healthcare services. One promising avenue is the introduction of companion robots. These robots are designed to provide physical assistance as well as emotional support and companionship, necessitating effective human-robot interaction (HRI). This study explores the role of cognitive empathy within HRI, focusing on the influence of robot facial animacy and emotional expressions on perspective-taking abilities-a key aspect of cognitive empathy-across different age groups. To this end, a director task involving 60 participants (30 young and 30 older adults) with varying degrees of robot facial animacy (0%, 50%, 100%) and emotional expressions (happy, neutral) was conducted. The results revealed that older adults displayed enhanced perspective-taking with higher animacy faces. Interestingly, while happiness on high-animacy faces improved perspective-taking, the same expression on low-animacy faces reduced it. These findings highlight the importance of considering facial animacy and emotional expressions in designing companion robots for older adults to optimize user engagement and acceptance. The study's implications are pertinent to the design and development of socially effective service robots, particularly for the aging population.

The inversion effect on the cubic humanness-uncanniness relation in humanlike agents

The uncanny valley describes the typically nonlinear relation between the esthetic appeal of artificial entities and their human likeness. The effect has been attributed to specialized (configural) processing that increases sensitivity to deviations from human norms. We investigate this effect in computer-generated, humanlike android and human faces using dynamic facial expressions. Angry and happy expressions with varying degrees of synchrony were presented upright and inverted and rated on their eeriness, strangeness, and human likeness. A sigmoidal function of human likeness and uncanniness ("uncanny slope") was found for upright expressions and a linear relation for inverted faces. While the function is not indicative of an uncanny valley, the results support the view that configural processing moderates the effect of human likeness on uncanniness and extend its role to dynamic facial expressions.

Towards Living Machines: current and future trends of tactile sensing, grasping, and social robotics

The development of future technologies can be highly influenced by our deeper understanding of the principles that underlie living organisms. The Living Machines conference aims at presenting (among others) the interdisciplinary work of behaving systems based on such principles. Celebrating the 10 years of the conference, we present the progress and future challenges of some of the key themes presented in the robotics workshop of the Living Machines conference. More specifically, in this perspective paper, we focus on the advances in the field of biomimetics and robotics for the creation of artificial systems that can robustly interact with their environment, ranging from tactile sensing, grasping, and manipulation to the creation of psychologically plausible agents.

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.

The uncanniness of written text is explained by configural deviation and not by processing disfluency

Deviating from human norms in human-looking artificial entities can elicit uncanny sensations, described as the uncanny valley. This study investigates in three tasks whether configural deviation in written text also increases uncanniness. It further evaluates whether the uncanniness of text is better explained by perceptual disfluency and especially deviations from specialized categories, or conceptual disfluency caused by ambiguity. In the first task, lower sentence readability predicted uncanniness, but deviating sentences were more uncanny than typical sentences despite being just as readable. Furthermore, familiarity with a language increased the effect of configural deviation on uncanniness but not the effect of non-configural deviation (blur). In the second and third tasks, semantically ambiguous words and sentences were not uncannier than typical sentences, but deviating, non-ambiguous sentences were. Deviations from categories with specialized processing mechanisms thus better fit the observed results as an explanation of the uncanny valley than ambiguity-based explanations.

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.

Creepy cats and strange high houses: Support for configural processing in testing predictions of nine uncanny valley theories

In 1970, Masahiro Mori proposed the uncanny valley (UV), a region in a human-likeness continuum where an entity risks eliciting a cold, eerie, repellent feeling. Recent studies have shown that this feeling can be elicited by entities modeled not only on humans but also nonhuman animals. The perceptual and cognitive mechanisms underlying the UV effect are not well understood, although many theories have been proposed to explain them. To test the predictions of nine classes of theories, a within-subjects experiment was conducted with 136 participants. The theories' predictions were compared with ratings of 10 classes of stimuli on eeriness and coldness indices. One type of theory, configural processing, predicted eight out of nine significant effects. Atypicality, in its extended form, in which the uncanny valley effect is amplified by the stimulus appearing more human, also predicted eight. Threat avoidance predicted seven; atypicality, perceptual mismatch, and mismatch+ predicted six; category+, novelty avoidance, mate selection, and psychopathy avoidance predicted five; and category uncertainty predicted three. Empathy's main prediction was not supported. Given that the number of significant effects predicted depends partly on our choice of hypotheses, a detailed consideration of each result is advised. We do, however, note the methodological value of examining many competing theories in the same experiment.

Self-face perception in 12-month-old infants: A study using the morphing technique

The present study investigated self-face perception in 12-month-old infants using the morphing technique. Twenty-four 12-month-old infants participated in both the main and control experiments. In the main experiment, we used the participant's own face, an unfamiliar infant's face (age- and gender-matched), and a morphed face comprising 50 % each of the self and unfamiliar faces as stimuli. The control experiment followed the same procedure, except that the self-face was replaced with another unfamiliar face. In both experiments, two of these stimuli were presented side by side on a monitor in each trial, and infants' fixation duration was measured. Results showed that shorter fixation durations were found for the morphed face compared with the self-face and the unfamiliar face in the main experiment, but there were no significant preferences for any comparisons in the control experiment. The results suggest that 12-month-old infants could detect subtle differences in facial features between the self-face and the other faces, and infants might show less preference for the self-resembling morphed face due to increased processing costs, which can be interpreted using the uncanny valley hypothesis. Overall, representations of the self-face seem to a certain extent to be formed by the end of the first year of life through daily visual experience.

Trust in AI Agent: A Systematic Review of Facial Anthropomorphic Trustworthiness for Social Robot Design

As an emerging artificial intelligence system, social robot could socially communicate and interact with human beings. Although this area is attracting more and more attention, limited research has tried to systematically summarize potential features that could improve facial anthropomorphic trustworthiness for social robot. Based on the literature from human facial perception, product, and robot face evaluation, this paper systematically reviews, evaluates, and summarizes static facial features, dynamic features, their combinations, and related emotional expressions, shedding light on further exploration of facial anthropomorphic trustworthiness for social robot design.

Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots

Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. This research is useful for modelling the appearance and personality of RHRs with EAI for specific jobs such as care for the elderly and social companions for the young, isolated, and vulnerable.