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

Power-Posing Robots: The Influence of a Humanoid Robot’s Posture and Size on its Perceived Dominance, Competence, Eeriness, and Threat

When interacting with sophisticated digital technologies, people often fall back on the same interaction scripts they apply to the communication with other humans—especially if the technology in question provides strong anthropomorphic cues (e.g., a human-like embodiment). Accordingly, research indicates that observers tend to interpret the body language of social robots in the same way as they would with another human being. Backed by initial evidence, we assumed that a humanoid robot will be considered as more dominant and competent, but also as more eerie and threatening once it strikes a so-called power pose. Moreover, we pursued the research question whether these effects might be accentuated by the robot’s body size. To this end, the current study presented 204 participants with pictures of the robot NAO in different poses (expansive vs. constrictive), while also manipulating its height (child-sized vs. adult-sized). Our results show that NAO’s posture indeed exerted strong effects on perceptions of dominance and competence. Conversely, participants’ threat and eeriness ratings remained statistically independent of the robot’s depicted body language. Further, we found that the machine’s size did not affect any of the measured interpersonal perceptions in a notable way. The study findings are discussed considering limitations and future research directions.

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.

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.

Human-Like Arm Motion Generation: A Review

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

Relating Mori's Uncanny Valley in generating conversations with artificial affective communication and natural language processing

Human beings express affinity (Shinwa-kan in Japanese language) in communicating transactive engagements among healthcare providers, patients and healthcare robots. The appearance of healthcare robots and their language capabilities often feature characteristic and appropriate compassionate dialogical functions in human-robot interactions. Elements of healthcare robot configurations comprising its physiognomy and communication properties are founded on the positivist philosophical perspective of being the summation of composite parts, thereby mimicking human persons. This article reviews Mori's theory of the Uncanny Valley and its consequent debates, and examines "Uncanny" relations with generating healthcare robot conversational content with artificial affective communication (AAC) using natural language processing. With healthcare robots provoking influential physical composition and sensory expressions, the relations in human-healthcare robot transactive engagements are argued as supportive of the design and development in natural language processing. This implies that maintaining human-healthcare robot interaction and assessing the eeriness situations explained in the Uncanny Valley theory are crucial positions for healthcare robot functioning as a valuable commodity in health care. As such, physical features, language capabilities and mobility of healthcare robots establish the primacy of the AAC with natural language processing as integral to healthcare robot-human healthcare practice.

Dissociating affective and cognitive dimensions of uncertainty by altering regulatory focus

Cognitive uncertainty is evidenced across learning, memory, and decision-making tasks. Uncertainty has also been examined in studies of positive affect and preference by manipulating stimulus presentation frequency. Despite the extensive research in both of these areas, there has been little systematic study into the relationship between affective and cognitive uncertainty. Using a categorization task, the present study examined changes in cognitive and affective uncertainty by manipulating stimulus presentation frequency and processing focus (i.e., promotion v. prevention focus). Following training, participants categorized stimuli and provided ratings of both typicality and negative affect. Results indicated that cognitive uncertainty was influenced by a categorical representation of stimuli whereas affective uncertainty was also influenced by exemplar presentation frequency during training. We additionally found that when the training was framed in terms of the avoidance of errors (i.e., a prevention focus), categorization performance was affected across the stimulus continuum whereas affective ratings remained unchanged.

Social robotics and the modulation of social perception and bias

The field of social robotics offers an unprecedented opportunity to probe the process of impression formation and the effects of identity-based stereotypes (e.g. about gender or race) on social judgements and interactions. We present the concept of fair proxy communication-a form of robot-mediated communication that proceeds in the absence of potentially biasing identity cues-and describe how this application of social robotics may be used to illuminate implicit bias in social cognition and inform novel interventions to reduce bias. We discuss key questions and challenges for the use of robots in research on the social cognition of bias and offer some practical recommendations. We conclude by discussing boundary conditions of this new form of interaction and by raising some ethical concerns about the inclusion of social robots in psychological research and interventions. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.

Frequency of Switching Touching Mode Reflects Tactile Preference Judgment

We can judge affective aspects of objects by actively exploring them with our hands. Previous studies have mainly focused on how the physical properties of an object’s surface affect tactile preference evaluations. However, despite the widely accepted notion that the participant’s strategy has a great impact on how they explore an object, there is a lack of investigations of hand motion during preference judgment and its impact on preference rating. This paper recruits the recurrence plot technique to illustrate the temporal dynamics of explorative hand motion. In an experiment, participants were asked to freely explore the surface of tactile stimuli and rate their tactile preference for them. The temporal dynamics of finger velocity and force were visualized and characterized by using recurrence quantification analysis. We found correlations between preference ratings and recurrence features that represent the temporal dynamics of explorative hand motion, in addition to correlations between preference ratings and conventional time-averaged features (e.g., averaged finger velocity). One unique feature that correlated with preference ratings was TREND, which represents to what extent similar motion patterns repeatedly occur. The results of a subsidiary analysis supported the possibility that the TREND difference can be interpreted as the frequency of switching touching modes (e.g., stroking and pushing motions). Taken together, these results suggest that participants tend to perform the same hand motion repeatedly for preferable objects, while they tend to combine different touching modes for less preferable objects. They also indicate that the recurrence plot scheme is a promising way to extract humans’ strategies for tactile exploration.

Long-term memory specificity depends on inhibition of related items

Long-term memory relies on both accurately retrieving specific details and inhibiting competing information. In the current investigation, we evaluated the specificity of long-term memory representations for faces. During each study phase, participants were presented with neutral Caucasian male and female faces. During the corresponding test phase, old faces, related faces, and new faces were presented and participants made "old"-"new" recognition judgments. Related faces were created by morphing along a continuum in steps of 20% (i.e., 20%, 40%, 60% and 80% morphs) between old faces and new faces (independent ratings indicated that the pairs of to-be-morphed old faces and new faces were perceptually dissimilar). In two experiments, memory representations were very specific as the "old" response rate for old faces was significantly higher than closely related faces (i.e., 20% morphs). Furthermore, there was evidence of memory inhibition, as the "old" response rate for 20% morphs was significantly lower than 40% morphs (the identical pattern of results was observed with a d' analysis). These findings may reflect an evolutionary advantage for recognising specific faces, which may require inhibition of closely related faces.

Sensorimotor Oscillations During a Reciprocal Touch Paradigm With a Human or Robot Partner

Robots provide an opportunity to extend research on the cognitive, perceptual, and neural processes involved in social interaction. This study examined how sensorimotor oscillatory electroencephalogram (EEG) activity can be influenced by the perceived nature of a task partner – human or robot – during a novel “reciprocal touch” paradigm. Twenty adult participants viewed a demonstration of a robot that could “feel” tactile stimulation through a haptic sensor on its hand and “see” changes in light through a photoreceptor at the level of the eyes; the robot responded to touch or changes in light by moving a contralateral digit. During EEG collection, participants engaged in a joint task that involved sending tactile stimulation to a partner (robot or human) and receiving tactile stimulation back. Tactile stimulation sent by the participant was initiated by a button press and was delivered 1500 ms later via an inflatable membrane on the hand of the human or on the haptic sensor of the robot partner. Stimulation to the participant’s finger (from the partner) was sent on a fixed schedule, regardless of partner type. We analyzed activity of the sensorimotor mu rhythm during anticipation of tactile stimulation to the right hand, comparing mu activity at central electrode sites when participants believed that tactile stimulation was initiated by a robot or a human, and to trials in which “nobody” received stimulation. There was a significant difference in contralateral mu rhythm activity between anticipating stimulation from a human partner and the “nobody” condition. This effect was less pronounced for anticipation of stimulation from the robot partner. Analyses also examined beta rhythm responses to the execution of the button press, comparing oscillatory activity when participants sent tactile stimulation to the robot or the human partner. The extent of beta rebound at frontocentral electrode sites following the button press differed between conditions, with a significantly larger increase in beta power when participants sent tactile stimulation to a robot partner compared to the human partner. This increase in beta power may reflect greater predictably in event outcomes. This new paradigm and the novel findings advance the neuroscientific study of human–robot interaction.

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.