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.

[PREPRINT] Mind the Machines: Applying Implicit Measures of Mind Perception to Social Robotics

Beyond conscious beliefs and goals, automatic cognitive processes shape our social encounters, and interactions with complex machines like social robots are no exception. With this in mind, it is surprising that research in human-robot interaction (HRI) almost exclusively uses explicit measures, such as subjective ratings and questionnaires, to assess human attitudes towards robots - seemingly ignoring the importance of implicit measures. This is particularly true for research focusing on the question whether or not humans are willing to attribute complex mental states mind perception, such as agency (i.e., the capacity to plan and act) and experience (i.e., the capacity to sense and feel), to robotic agents. In the current study, we (i) created the mind perception implicit association test (MP-IAT) to examine subconscious attributions of mental capacities to agents of different degrees of human-likeness (here: human vs. humanoid robot), and (ii) compared the outcomes of the MP-IAT to explicit mind perception ratings of the same agents.Results indicate that (i) already at the subconscious level, robots are associated with lower levels of agency and experience compared to humans, and that (ii) implicit and explicit measures of mind perception are not significantly correlated. This suggests that mind perception (i) has an implicit component that can be measured using implicit tests like the IAT and (ii) might be difficult to modulate via design or experimental procedures due to its fast-acting, automatic nature.

Human-like Computers - A Lesson in Absurdity

Some 50 years ago, the computer scientist Joseph Weizenbaum found that the idea that the socialization of a machine might in any way be comparable to the one of a human, is a sign of the madness of our time. Today, the idea is mostly not seen as a mad but rather as a quite plausible one, as are many other ideas about human-likeness of computers, like them having consciousness. At a closer look, however, the alleged human-likeness of computers is merely derived from weak analogies, like them having intelligence just because they can play chess (and nothing else). The book details the psychological and physiological preconditions for human mental functions to occur, ones that cannot possibly be fulfilled by computers. It puts the computers-as-humans issue into the broader philosophical frame of the scientistic view that man is basically a machine.

Cognitive prediction of obstacle's movement for reinforcement learning pedestrian interacting model

Recent studies in pedestrian simulation have been able to construct a highly realistic navigation behaviour in many circumstances. However, when replicating the close interactions between pedestrians, the replicated behaviour is often unnatural and lacks human likeness. One of the possible reasons is that the current models often ignore the cognitive factors in the human thinking process. Another reason is that many models try to approach the problem by optimising certain objectives. On the other hand, in real life, humans do not always take the most optimised decisions, particularly when interacting with other people. To improve the navigation behaviour in this circumstance, we proposed a pedestrian interacting model using reinforcement learning. Additionally, a novel cognitive prediction model, inspired by the predictive system of human cognition, is also incorporated. This helps the pedestrian agent in our model to learn to interact and predict the movement in a similar practice as humans. In our experimental results, when compared to other models, the path taken by our model’s agent is not the most optimised in certain aspects like path lengths, time taken and collisions. However, our model is able to demonstrate a more natural and human-like navigation behaviour, particularly in complex interaction settings.

Images of Ukrainian demonology in folk stage choreography

The purpose of the article is to find out the characteristic features of the use of the images of Ukrainian demonology in the folk-stage choreography of Ukraine. The methodology consists in the application of general scientific methods of theoretical and empirical levels: analysis and generalization of scientific and theoretical bases of research, comparative and logical methods, interview method; also art analysis of choreographic numbers of amateur and professional groups of Ukraine. These methods make it possible to characterize the most common images of Ukrainian demonology and to determine the peculiarities of their use and embodiment in folk-stage choreography. Scientific novelty. For the first time, the article, based on the analysis of choreographic works from the repertoires of amateur and professional groups, highlights the characteristic features of the use of images of Ukrainian demonology in folk-stage dance art. Conclusions. Demonological beliefs of Ukrainians are a kind of synthesis of mythological and religious ideas of the human about the universe and at the same time the embodiment of zest for life and democracy. Demon spirits are endowed with anthropomorphic features, i.e. they usually have a human likeness, and this presupposes the possibility of their use in folk-stage dance art. Choreographers create choreographic works based on their own plot, based on folk beliefs, rituals, and legends, as well as on the motives of literary works, the main characters of which are demonological characters. A stylized folk-stage dance with elements of acrobatics is often used to convey all the versatility and mysticism of mythological images. The use of images of Ukrainian demonology in folk-stage choreography contributes to the popularization of folk choreographic art, as well as increases the interest of modern society in national traditions. Keywords: folk-stage dance; Ukrainian demonology, folk choreographic art.

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

Intentional and Phenomenal attributions in the light of the influence of personality traits, and Attitudes towards robots on pro-social behaviour in human-robot interaction

In the decades to come, robots could become more present in the human environment increasing the likelihood to interact with them. When reasoning about them, individuals tend to endow robots with human-like characteristics such as intentions or emotions, they develop attitudes toward them and differ in their likelihood to cooperate with them . However, how these different variables emerge, interact in the human mind and effect actual behaviour in HRI is still poorly understood. In three studies, using the intentional and phenomenal stance theoretical framework, the attitudes toward robots evaluation and the Big-Five personality traits framework we investigated the attribution of intentional and phenomenal experience to robots and the influence of imaginative representation robots on the interpretative attributions (Experiment 1). We also evaluated how the context of evaluation presenting robots with different level of human-likeness as potential social actors compared to mere technological prototypes and the prior attitudes toward them could bias intentional/phenomenal attributions (Experiment 2). Finally, we used a human-robot a prisoner’s dilemma game and developed a structural integrative model using attributions, attitudes and personality traits to evaluate the likelihood of participants to make a prosocial decision in HRI (Experiment 3).Experiment 1, 2 and 3 results showed that intentional stance is more readily adopted than phenomenal stance and that the imaginative type of the stances predicts the interpretative type. In experiment 2 level of attributions were predicted by attitudes toward robots. Also, attributions were influenced by robot human-likeness and the presentation of robots as social, compared to non-social, agents. Finally, experiment 3 structural integrative model showed a predominance of personality traits and attitudes to predict the likelihood to cooperate in an actual HRI.

Human-like Robots and the Uncanny Valley: A Meta-Analysis of User Responses Based on the Godspeed Scales

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 increasingly 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 likability. 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.