Empirical evaluation of the uncanny valley hypothesis fails to confirm the predicted effect of motion

Biological postural oscillations during facial expression of pain in virtual characters modulate early and late ERP components associated with empathy: A pilot study

There is a surge in the use of virtual characters in cognitive sciences. However, their behavioural realism remains to be perfected in order to trigger more spontaneous and socially expected reactions in users. It was recently shown that biological postural oscillations (idle motion) were a key ingredient to enhance the empathic response to its facial pain expression. The objective of this study was to examine, using electroencephalography, whether idle motion would modulate the neural response associated with empathy when viewing a pain-expressing virtual character. Twenty healthy young adults were shown video clips of a virtual character displaying a facial expression of pain while its body was either static (Still condition) or animated with pre-recorded human postural oscillations (Idle condition). Participants rated the virtual human's facial expression of pain as significantly more intense in the Idle condition compared to the Still condition. Both the early (N2-N3) and the late (rLPP) event-related potentials (ERPs) associated with distinct dimensions of empathy, affective resonance and perspective-taking, respectively, were greater in the Idle condition compared to the Still condition. These findings confirm the potential of idle motion to increase empathy for pain expressed by virtual characters. They are discussed in line with contemporary empathy models in relation to human-machine interactions.

Shape of the Uncanny Valley and Emotional Attitudes Toward Robots Assessed by an Analysis of YouTube Comments

The uncanny valley hypothesis (UVH) suggests that almost, but not fully, humanlike artificial characters elicit a feeling of eeriness or discomfort in observers. This study used Natural Language Processing of YouTube comments to provide ecologically-valid, non-laboratory results about people’s emotional reactions toward robots. It contains analyses of 224,544 comments from 1515 videos showing robots from a wide humanlikeness spectrum. The humanlikeness scores were acquired from the Anthropomorphic roBOT database. The analysis showed that people use words related to eeriness to describe very humanlike robots. Humanlikeness was linearly related to both general sentiment and perceptions of eeriness—-more humanlike robots elicit more negative emotions. One of the subscales of humanlikeness, Facial Features, showed a UVH-like relationship with both sentiment and eeriness. The exploratory analysis demonstrated that the most suitable words for measuring the self-reported uncanny valley effect are: ‘scary’ and ‘creepy’. In contrast to theoretical expectations, the results showed that humanlikeness was not related to either pleasantness or attractiveness. Finally, it was also found that the size of robots influences sentiment toward the robots. According to the analysis, the reason behind this is the perception of smaller robots as more playable (as toys), although the prediction that bigger robots would be perceived as more threatening was not supported.

Monkey visual attention does not fall into the uncanny valley

Very humanlike artificial agents can induce feelings of uneasiness in human perceivers. Stimuli that generate this response are said to occupy "the uncanny valley". Given inconsistent findings in the literature, whether or not nonhuman animals experience the uncanny valley is unclear. Here, we recorded the visual attention of eleven male rhesus monkeys as they viewed faces varying in realness across five levels, with visual attention measured by both number and duration of visual fixations on faces as a whole and on areas of interest within the faces (e.g., eyes, mouth). Face stimuli varied in terms of the realism of the image and behavior depicted by the face (lipsmack, threat, bared teeth, and neutral). We largely found no support that rhesus monkeys perceive an uncanny valley when viewing our stimuli; however, monkeys did generally pay more attention to eyes and less attention to mouths in real images compared to less realistic images. Across all stimuli, monkeys' visual attention was drawn to the mouths of images when teeth were visible. These findings suggest that rhesus monkeys in our study did not display an uncanny valley effect when viewing realistic stimuli but did percieve affective information depicted by faces regardless of how real those faces appear.

Accepting Human-like Avatars in Social and Professional Roles

Humans report perceptions of unease or eeriness as humanoid/android robots and digital avatars approach human-like physical resemblance, a phenomenon alluded by the Uncanny Valley theory. This study extends the discussions on interactions and acceptance of digital avatars with findings from three experiments. In the first, perceptive evaluation of actors in clips from computer-generated animation and a live-action version of the same movie was examined. In the second experiment, we considered short clips with highly realistic digital avatars to measure recognition ability, the extent of eeriness, and specific physical features identified as unreal. The fixation area and pupil size variation recorded using an eye tracker were analyzed to infer attention to the body, face, and emotional response, respectively. Building on these findings, the third experiment looked at acceptance in roles requiring human skill, empathy, and cognitive ability. The results show that based on perceptions from physical attributes, the eeriness scores diverge from the uncanny valley theory as human-likeness increases. The realistic CGI and mocap technology could have helped cross the valley. Visual attention inferred from gaze behavior was similar for live-action and CGI. At the same time, we observe pupil size changes reflecting emotions like eeriness when the avatars either talked or smiled. Proficiency and acceptance scores were lower for roles requiring complex social cognition processes, such as friends and judicial decision-making. Interestingly, real-life stereotypes of gender roles were transferred to digital avatars too. The findings suggest an ambiguity in accepting human-like avatars in social and professional interactions, emphasizing the need for a multi-dimensional approach when applying the uncanny valley theory. A detailed and contextual examination is imperative as technological advancements have placed humans closer to co-existing with digital or physical android/humanoid robots.

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.

Natural human postural oscillations enhance the empathic response to a facial pain expression in a virtual character

Virtual reality platforms producing interactive and highly realistic characters are being used more and more as a research tool in social and affective neuroscience to better capture both the dynamics of emotion communication and the unintentional and automatic nature of emotional processes. While idle motion (i.e., non-communicative movements) is commonly used to create behavioural realism, its use to enhance the perception of emotion expressed by a virtual character is critically lacking. This study examined the influence of naturalistic (i.e., based on human motion capture) idle motion on two aspects (the perception of other’s pain and affective reaction) of an empathic response towards pain expressed by a virtual character. In two experiments, 32 and 34 healthy young adults were presented video clips of a virtual character displaying a facial expression of pain while its body was either static (still condition) or animated with natural postural oscillations (idle condition). The participants in Experiment 1 rated the facial pain expression of the virtual human as more intense, and those in Experiment 2 reported being more touched by its pain expression in the idle condition compared to the still condition, indicating a greater empathic response towards the virtual human’s pain in the presence of natural postural oscillations. These findings are discussed in relation to the models of empathy and biological motion processing. Future investigations will help determine to what extent such naturalistic idle motion could be a key ingredient in enhancing the anthropomorphism of a virtual human and making its emotion appear more genuine.

Design of a hesitant movement gesture for mobile robots

In previous experiments, a back-off movement was introduced as a motion strategy of robots to facilitate the order of passage at bottlenecks in human-robot spatial interaction. In this article we take a closer look at the appropriate application of motion parameters that make the backward movement legible. Related works in distance perception, size-speed illusions, and viewpoint-based legibility considerations suggest a relationship between the size of the robot and the observer’s perspective on the expected execution of this movement. We performed a participant experiment (N = 50) in a virtual reality environment where participants adjusted the minimum required back-off length and preferred back-off speed as a result of the robot size, and the viewpoint of the back-off movement. We target a model-based approach on how appropriate back-off design translates to different sized robots and observer’s viewpoints. Thus, we allow the application of back-off in a variety of autonomous moving systems. The results show a significant correlation between the increasingly expected back-off lengths with increasing robot size, but only weak effects of the viewpoint on the requirements of this movement. An exploratory analysis suggests that execution time might be a promising parameter to consider for the design of legible motion.

Integrated jerk as an indicator of affinity for artificial agent kinematics: laptop and virtual reality experiments involving index finger motion during two-digit grasping

Uncanny valley research has shown that human likeness is an important consideration when designing artificial agents. It has separately been shown that artificial agents exhibiting human-like kinematics can elicit positive perceptual responses. However the kinematic characteristics underlying that perception have not been elucidated. This paper proposes kinematic jerk amplitude as a candidate metric for kinematic human likeness, and aims to determine whether a perceptual optimum exists over a range of jerk values. We created minimum-jerk two-digit grasp kinematics in a prosthetic hand model, then added different amplitudes of temporally smooth noise to yield a variety of animations involving different total jerk levels, ranging from maximally smooth to highly jerky. Subjects indicated their perceptual affinity for these animations by simultaneously viewing two different animations side-by-side, first using a laptop, then separately within a virtual reality (VR) environment. Results suggest that (a) subjects generally preferred smoother kinematics, (b) subjects exhibited a small preference for rougher-than minimum jerk kinematics in the laptop experiment, and that (c) the preference for rougher-than minimum-jerk kinematics was amplified in the VR experiment. These results suggest that non-maximally smooth kinematics may be perceptually optimal in robots and other artificial agents.

A Naturalistic Dynamic Monkey Head Avatar Elicits Species-Typical Reactions and Overcomes the Uncanny Valley

Research on social perception in monkeys may benefit from standardized, controllable, and ethologically valid renditions of conspecifics offered by monkey avatars. However, previous work has cautioned that monkeys, like humans, show an adverse reaction toward realistic synthetic stimuli, known as the "uncanny valley" effect. We developed an improved naturalistic rhesus monkey face avatar capable of producing facial expressions (fear grin, lip smack and threat), animated by motion capture data of real monkeys. For validation, we additionally created decreasingly naturalistic avatar variants. Eight rhesus macaques were tested on the various videos and avoided looking at less naturalistic avatar variants, but not at the most naturalistic or the most unnaturalistic avatar, indicating an uncanny valley effect for the less naturalistic avatar versions. The avoidance was deepened by motion and accompanied by physiological arousal. Only the most naturalistic avatar evoked facial expressions comparable to those toward the real monkey videos. Hence, our findings demonstrate that the uncanny valley reaction in monkeys can be overcome by a highly naturalistic avatar.

How Foot Tracking Matters: The Impact of an Animated Self-Avatar on Interaction, Embodiment and Presence in Shared Virtual Environments

The use of a self-avatar representation in head-mounted displays has been shown to have important effects on user behavior. However, relatively few studies focus on feet and legs. We implemented a shared virtual reality for consumer virtual reality systems where each user could be represented by a gender-matched self-avatar controlled by multiple trackers. The self-avatar allowed users to see their feet, legs and part of their torso when they looked down. We implemented an experiment where participants worked together to solve jigsaw puzzles. Participants experienced either no-avatar, a self-avatar with floating feet, or a self-avatar with tracked feet, in a between-subjects manipulation. First, we found that participants could solve the puzzle more quickly with self-avatars than without self-avatars; but there was no significant difference between the latter two conditions, solely on task completion time. Second, we found participants with tracked feet placed their feet statistically significantly closer to obstacles than participants with floating feet, whereas participants who did not have a self-avatar usually ignored obstacles. Our post-experience questionnaire results confirmed that the use of a self-avatar has important effects on presence and interaction. Together the results show that although the impact of animated legs might be subtle, it does change how users behave around obstacles. This could have important implications for the design of virtual spaces for applications such as training or behavioral analysis.

Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley

Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial social partners remain unclear. The uncanny valley (UV) hypothesis predicts that humans prefer anthropomorphic agents but reject them if they become too humanlike-the so-called UV reaction. Using fMRI, we investigated neural activity when subjects evaluated artificial agents and made decisions about them. Across two experimental tasks, the ventromedial prefrontal cortex (VMPFC) encoded an explicit representation of subjects' UV reactions. Specifically, VMPFC signaled the subjective likability of artificial agents as a nonlinear function of humanlikeness, with selective low likability for highly humanlike agents. In exploratory across-subject analyses, these effects explained individual differences in psychophysical evaluations and preference choices. Functionally connected areas encoded critical inputs for these signals: the temporoparietal junction encoded a linear humanlikeness continuum, whereas nonlinear representations of humanlikeness in dorsomedial prefrontal cortex (DMPFC) and fusiform gyrus emphasized a human-nonhuman distinction. Following principles of multisensory integration, multiplicative combination of these signals reconstructed VMPFC's valuation function. During decision making, separate signals in VMPFC and DMPFC encoded subjects' decision variable for choices involving humans or artificial agents, respectively. A distinct amygdala signal predicted rejection of artificial agents. Our data suggest that human reactions toward artificial agents are governed by a neural mechanism that generates a selective, nonlinear valuation in response to a specific feature combination (humanlikeness in nonhuman agents). Thus, a basic principle known from sensory coding-neural feature selectivity from linear-nonlinear transformation-may also underlie human responses to artificial social partners.SIGNIFICANCE STATEMENT Would you trust a robot to make decisions for you? Autonomous artificial agents are increasingly entering our lives, but how the human brain responds to these new artificial social partners remains unclear. The uncanny valley (UV) hypothesis-an influential psychological framework-captures the observation that human responses to artificial agents are nonlinear: we like increasingly anthropomorphic artificial agents, but feel uncomfortable if they become too humanlike. Here we investigated neural activity when humans evaluated artificial agents and made personal decisions about them. Our findings suggest a novel neurobiological conceptualization of human responses toward artificial agents: the UV reaction-a selective dislike of highly humanlike agents-is based on nonlinear value-coding in ventromedial prefrontal cortex, a key component of the brain's reward system.

Attitude Towards Humanoid Robots and the Uncanny Valley Hypothesis

The main aim of the presented study was to check whether the well-established measures concerning the attitude towards humanoid robots are good predictors for the uncanny valley effect. We present a study in which 12 computer rendered humanoid models were presented to our subjects. Their declared comfort level was cross-referenced with the Belief in Human Nature Uniqueness (BHNU) and the Negative Attitudes toward Robots that Display Human Traits (NARHT) scales. Subsequently, there was no evidence of a statistical significance between these scales and the existence of the uncanny valley phenomenon. However, correlations between expected stress level while human-robot interaction and both BHNU, as well as NARHT scales, were found. The study covered also the evaluation of the perceived robots’ characteristic and the emotional response to them.

Comparative thanatology, an integrative approach: exploring sensory/cognitive aspects of death recognition in vertebrates and invertebrates

Evolutionary thanatology benefits from broad taxonomic comparisons of non-human animals' responses to death. Furthermore, exploring the sensory and cognitive bases of these responses promises to allow classification of the underlying mechanisms on a spectrum from phylogenetically ancient to more derived traits. We draw on studies of perception and cognition in invertebrate and vertebrate taxa (with a focus on arthropods, corvids, proboscids, cetaceans and primates) to explore the cues that these animals use to detect life and death in others, and discuss proximate and ultimate drivers behind their capacities to do so. Parallels in thanatological behaviour exhibited by the last four taxa suggest similar sensory-cognitive processing rules for dealing with corpses, the evolution of which may have been driven by complex social environments. Uniting these responses is a phenomenon we term 'animacy detection malfunction', whereupon the corpse, having both animate and inanimate attributes, creates states of fear/curiosity manifested as approach/avoidance behaviours in observers. We suggest that integrating diverse lines of evidence (including the 'uncanny valley' effect originating from the field of robotics) provides a promising way to advance the field, and conclude by proposing avenues for future research.This article is part of the theme issue 'Evolutionary thanatology: impacts of the dead on the living in humans and other animals'.

Pupillary Responses to Robotic and Human Emotions: The Uncanny Valley and Media Equation Confirmed

Physiological responses during human–robots interaction are useful alternatives to subjective measures of uncanny feelings for nearly humanlike robots (uncanny valley) and comparable emotional responses between humans and robots (media equation). However, no studies have employed the easily accessible measure of pupillometry to confirm the uncanny valley and media equation hypotheses, evidence in favor of the existence of these hypotheses in interaction with emotional robots is scarce, and previous studies have not controlled for low level image statistics across robot appearances. We therefore recorded pupil size of 40 participants that viewed and rated pictures of robotic and human faces that expressed a variety of basic emotions. The robotic faces varied along the dimension of human likeness from cartoonish to humanlike. We strictly controlled for confounding factors by removing backgrounds, hair, and color, and by equalizing low level image statistics. After the presentation phase, participants indicated to what extent the robots appeared uncanny and humanlike, and whether they could imagine social interaction with the robots in real life situations. The results show that robots rated as nearly humanlike scored higher on uncanniness, scored lower on imagined social interaction, evoked weaker pupil dilations, and their emotional expressions were more difficult to recognize. Pupils dilated most strongly to negative expressions and the pattern of pupil responses across emotions was highly similar between robot and human stimuli. These results highlight the usefulness of pupillometry in emotion studies and robot design by confirming the uncanny valley and media equation hypotheses.

Uncanny valley as a window into predictive processing in the social brain

Uncanny valley refers to humans' negative reaction to almost-but-not-quite-human agents. Theoretical work proposes prediction violation as an explanation for uncanny valley but no empirical work has directly tested it. Here, we provide evidence that supports this theory using event-related brain potential recordings from the human scalp. Human subjects were presented images and videos of three agents as EEG was recorded: a real human, a mechanical robot, and a realistic robot in between. The real human and the mechanical robot had congruent appearance and motion whereas the realistic robot had incongruent appearance and motion. We hypothesize that the appearance of the agent would provide a context to predict her movement, and accordingly the perception of the realistic robot would elicit an N400 effect indicating the violation of predictions, whereas the human and the mechanical robot would not. Our data confirmed this hypothesis suggesting that uncanny valley could be explained by violation of one's predictions about human norms when encountered with realistic but artificial human forms. Importantly, our results implicate that the mechanisms underlying perception of other individuals in our environment are predictive in nature.

Why and how to use virtual reality to study human social interaction: The challenges of exploring a new research landscape

As virtual reality (VR) technology and systems become more commercially available and accessible, more and more psychologists are starting to integrate VR as part of their methods. This approach offers major advantages in experimental control, reproducibility, and ecological validity, but also has limitations and hidden pitfalls which may distract the novice user. This study aimed to guide the psychologist into the novel world of VR, reviewing available instrumentation and mapping the landscape of possible systems. We use examples of state‐of‐the‐art research to describe challenges which research is now solving, including embodiment, uncanny valley, simulation sickness, presence, ethics, and experimental design. Finally, we propose that the biggest challenge for the field would be to build a fully interactive virtual human who can pass a VR Turing test – and that this could only be achieved if psychologists, VR technologists, and AI researchers work together.

Subthalamic nucleus detects unnatural android movement

An android, i.e., a realistic humanoid robot with human-like capabilities, may induce an uncanny feeling in human observers. The uncanny feeling about an android has two main causes: its appearance and movement. The uncanny feeling about an android increases when its appearance is almost human-like but its movement is not fully natural or comparable to human movement. Even if an android has human-like flexible joints, its slightly jerky movements cause a human observer to detect subtle unnaturalness in them. However, the neural mechanism underlying the detection of unnatural movements remains unclear. We conducted an fMRI experiment to compare the observation of an android and the observation of a human on which the android is modelled, and we found differences in the activation pattern of the brain regions that are responsible for the production of smooth and natural movement. More specifically, we found that the visual observation of the android, compared with that of the human model, caused greater activation in the subthalamic nucleus (STN). When the android's slightly jerky movements are visually observed, the STN detects their subtle unnaturalness. This finding suggests that the detection of unnatural movements is attributed to an error signal resulting from a mismatch between a visual input and an internal model for smooth movement.

Understanding the Uncanny: Both Atypical Features and Category Ambiguity Provoke Aversion toward Humanlike Robots

Robots intended for social contexts are often designed with explicit humanlike attributes in order to facilitate their reception by (and communication with) people. However, observation of an “uncanny valley”—a phenomenon in which highly humanlike entities provoke aversion in human observers—has lead some to caution against this practice. Both of these contrasting perspectives on the anthropomorphic design of social robots find some support in empirical investigations to date. Yet, owing to outstanding empirical limitations and theoretical disputes, the uncanny valley and its implications for human-robot interaction remains poorly understood. We thus explored the relationship between human similarity and people's aversion toward humanlike robots via manipulation of the agents' appearances. To that end, we employed a picture-viewing task (N_agents = 60) to conduct an experimental test (N_participants = 72) of the uncanny valley's existence and the visual features that cause certain humanlike robots to be unnerving. Across the levels of human similarity, we further manipulated agent appearance on two dimensions, typicality (prototypic, atypical, and ambiguous) and agent identity (robot, person), and measured participants' aversion using both subjective and behavioral indices. Our findings were as follows: (1) Further substantiating its existence, the data show a clear and consistent uncanny valley in the current design space of humanoid robots. (2) Both category ambiguity, and more so, atypicalities provoke aversive responding, thus shedding light on the visual factors that drive people's discomfort. (3) Use of the Negative Attitudes toward Robots Scale did not reveal any significant relationships between people's pre-existing attitudes toward humanlike robots and their aversive responding—suggesting positive exposure and/or additional experience with robots is unlikely to affect the occurrence of an uncanny valley effect in humanoid robotics. This work furthers our understanding of both the uncanny valley, as well as the visual factors that contribute to an agent's uncanniness.

Human-likeness assessment for the Uncanny Valley Hypothesis

The Uncanny Valley Hypothesis (UVH, proposed in the 1970s) suggests that looking at or interacting with almost human-like artificial characters would trigger eeriness or discomfort. We studied how well subjects can assess degrees of human likeness for computer-generated characters. We conducted two studies, where subjects were asked to assess human likeness of given computer-generated models (Study 1) and to point the most typical model for a given category (Study 2). The results suggest that evaluation of the way human likeness is assessed should be an internal part of UVH research.

Circling Around the Uncanny Valley: Design Principles for Research Into the Relation Between Human Likeness and Eeriness

The uncanny valley effect (UVE) is a negative emotional response experienced when encountering entities that appear almost human. Research on the UVE typically investigates individual, or collections of, near human entities but may be prone to methodological circularity unless the properties that give rise to the emotional response are appropriately defined and quantified. In addition, many studies do not sufficiently control the variation in human likeness portrayed in stimulus images, meaning that the nature of stimuli that elicit the UVE is also not well defined or quantified. This article describes design criteria for UVE research to overcome the above problems by measuring three variables (human likeness, eeriness, and emotional response) and by using stimuli spanning the artificial to human continuum. These criteria allow results to be plotted and compared with the hypothesized uncanny valley curve and any effect observed can be quantified. The above criteria were applied to the methods used in a subset of existing UVE studies. Although many studies made use of some of the necessary measurements and controls, few used them all. The UVE is discussed in relation to this result and research methodology more broadly.

The Uncanny Valley: Existence and Explanations

More than 40 years ago, Japanese roboticist Masahiro Mori (1970/2005) proposed the “uncanny valley” hypothesis, which predicted a nonlinear relation between robots’ perceived human likeness and their likability. Although some studies have corroborated this hypothesis and proposed explanations for its existence, the evidence on both fronts has been mixed and open to debate. We first review the literature to ascertain whether the uncanny valley exists. We then try to explain the uncanny phenomenon by reviewing hypotheses derived from diverse theoretical and methodological perspectives within psychology and allied fields, including evolutionary, social, cognitive, and psychodynamic approaches. Next, we provide an evaluation and critique of these studies by focusing on their methodological limitations, leading us to question the accepted definition of the uncanny valley. We examine the definitions of human likeness and likability, and propose a statistical test to preliminarily quantify their nonlinear relation. We argue that the uncanny valley hypothesis is ultimately an engineering problem that bears on the possibility of building androids that may some day become indistinguishable from humans. In closing, we propose a dehumanization hypothesis to explain the uncanny phenomenon.

Comprehension and engagement in survey interviews with virtual agents

This study investigates how an onscreen virtual agent's dialog capability and facial animation affect survey respondents' comprehension and engagement in “face-to-face” interviews, using questions from US government surveys whose results have far-reaching impact on national policies. In the study, 73 laboratory participants were randomly assigned to respond in one of four interviewing conditions, in which the virtual agent had either high or low dialog capability (implemented through Wizard of Oz) and high or low facial animation, based on motion capture from a human interviewer. Respondents, whose faces were visible to the Wizard (and videorecorded) during the interviews, answered 12 questions about housing, employment, and purchases on the basis of fictional scenarios designed to allow measurement of comprehension accuracy, defined as the fit between responses and US government definitions. Respondents answered more accurately with the high-dialog-capability agents, requesting clarification more often particularly for ambiguous scenarios; and they generally treated the high-dialog-capability interviewers more socially, looking at the interviewer more and judging high-dialog-capability agents as more personal and less distant. Greater interviewer facial animation did not affect response accuracy, but it led to more displays of engagement—acknowledgments (verbal and visual) and smiles—and to the virtual interviewer's being rated as less natural. The pattern of results suggests that a virtual agent's dialog capability and facial animation differently affect survey respondents' experience of interviews, behavioral displays, and comprehension, and thus the accuracy of their responses. The pattern of results also suggests design considerations for building survey interviewing agents, which may differ depending on the kinds of survey questions (sensitive or not) that are asked.

Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not

Human replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica's realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes-eyelashes-mouth and skin-nose-eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations-those eliciting the greatest category uncertainty-were neither the eeriest nor the coldest.

Persistence of the uncanny valley: the influence of repeated interactions and a robot's attitude on its perception

The uncanny valley theory proposed by Mori has been heavily investigated in the recent years by researchers from various fields. However, the videos and images used in these studies did not permit any human interaction with the uncanny objects. Therefore, in the field of human-robot interaction it is still unclear what, if any, impact an uncanny-looking robot will have in the context of an interaction. In this paper we describe an exploratory empirical study using a live interaction paradigm that involved repeated interactions with robots that differed in embodiment and their attitude toward a human. We found that both investigated components of the uncanniness (likeability and eeriness) can be affected by an interaction with a robot. Likeability of a robot was mainly affected by its attitude and this effect was especially prominent for a machine-like robot. On the other hand, merely repeating interactions was sufficient to reduce eeriness irrespective of a robot's embodiment. As a result we urge other researchers to investigate Mori's theory in studies that involve actual human-robot interaction in order to fully understand the changing nature of this phenomenon.

Is it the real deal? Perception of virtual characters versus humans: an affective cognitive neuroscience perspective

Recent developments in neuroimaging research support the increased use of naturalistic stimulus material such as film, avatars, or androids. These stimuli allow for a better understanding of how the brain processes information in complex situations while maintaining experimental control. While avatars and androids are well suited to study human cognition, they should not be equated to human stimuli. For example, the uncanny valley hypothesis theorizes that artificial agents with high human-likeness may evoke feelings of eeriness in the human observer. Here we review if, when, and how the perception of human-like avatars and androids differs from the perception of humans and consider how this influences their utilization as stimulus material in social and affective neuroimaging studies. First, we discuss how the appearance of virtual characters affects perception. When stimuli are morphed across categories from non-human to human, the most ambiguous stimuli, rather than the most human-like stimuli, show prolonged classification times and increased eeriness. Human-like to human stimuli show a positive linear relationship with familiarity. Secondly, we show that expressions of emotions in human-like avatars can be perceived similarly to human emotions, with corresponding behavioral, physiological and neuronal activations, with exception of physical dissimilarities. Subsequently, we consider if and when one perceives differences in action representation by artificial agents versus humans. Motor resonance and predictive coding models may account for empirical findings, such as an interference effect on action for observed human-like, natural moving characters. However, the expansion of these models to explain more complex behavior, such as empathy, still needs to be investigated in more detail. Finally, we broaden our outlook to social interaction, where virtual reality stimuli can be utilized to imitate complex social situations.

A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness

The uncanny valley hypothesis, proposed already in the 1970s, suggests that almost but not fully humanlike artificial characters will trigger a profound sense of unease. This hypothesis has become widely acknowledged both in the popular media and scientific research. Surprisingly, empirical evidence for the hypothesis has remained inconsistent. In the present article, we reinterpret the original uncanny valley hypothesis and review empirical evidence for different theoretically motivated uncanny valley hypotheses. The uncanny valley could be understood as the naïve claim that any kind of human-likeness manipulation will lead to experienced negative affinity at close-to-realistic levels. More recent hypotheses have suggested that the uncanny valley would be caused by artificial–human categorization difficulty or by a perceptual mismatch between artificial and human features. Original formulation also suggested that movement would modulate the uncanny valley. The reviewed empirical literature failed to provide consistent support for the naïve uncanny valley hypothesis or the modulatory effects of movement. Results on the categorization difficulty hypothesis were still too scarce to allow drawing firm conclusions. In contrast, good support was found for the perceptual mismatch hypothesis. Taken together, the present review findings suggest that the uncanny valley exists only under specific conditions. More research is still needed to pinpoint the exact conditions under which the uncanny valley phenomenon manifests itself.

Perception of gait patterns that deviate from normal and symmetric biped locomotion

This study examines the range of gait patterns that are perceived as healthy and human-like with the goal of understanding how much asymmetry is allowable in a gait pattern before other people start to notice a gait impairment. Specifically, this study explores if certain abnormal walking patterns can be dismissed as unimpaired or not uncanny. Altering gait biomechanics is generally done in the fields of prosthetics and rehabilitation, however the perception of gait is often neglected. Although a certain gait can be functional, it may not be considered as normal by observers. On the other hand, an abnormally perceived gait may be more practical or necessary in some situations, such as limping after an injury or stroke and when wearing a prosthesis. This research will help to find the balance between the form and function of gait. Gait patterns are synthetically created using a passive dynamic walker (PDW) model that allows gait patterns to be systematically changed without the confounding influence from human sensorimotor feedback during walking. This standardized method allows the perception of specific changes in gait to be studied. The PDW model was used to produce walking patterns that showed a degree of abnormality in gait cadence, knee height, step length, and swing time created by changing the foot roll-over-shape, knee damping, knee location, and leg masses. The gait patterns were shown to participants who rated them according to separate scales of impairment and uncanniness. The results indicate that some pathological and asymmetric gait patterns are perceived as unimpaired and normal. Step time and step length asymmetries less than 5%, small knee location differences, and gait cadence changes of 25% do not result in a change in perception. The results also show that the parameters of a pathologically or uncanny perceived gait can be beneficially altered by increasing other independent parameters, in some sense masking the initial pathology.