Academic literature on the topic 'Humanlikeness'

Abstract In human-chatbot interaction, users casually and regularly offend and abuse the chatbot they are interacting with. The current paper explores the relationship between chatbot humanlikeness on the one hand and sexual advances and verbal aggression by the user on the other hand. 283 conversations between the Cleverbot chatbot and its users were harvested and analysed. Our results showed higher counts of user verbal aggression and sexual comments towards Cleverbot when Cleverbot appeared more humanlike in its behaviour. Caution is warranted with the interpretation of the results however as no experimental manipulation was conducted and causality can thus not be inferred. Nonetheless, the findings are relevant for both the research on the abuse of conversational agents, and the development of efficient approaches to discourage or prevent verbal aggression by chatbot users.

We are increasingly communicating with robots, and the design of lifelike humanoid robots will face at least five challenges. 1) Robots should meet social needs – people are getting older, lonelier, and are looking for alternatives to face-to-face contact, for instance, in coaching and therapy. 2) Robot designers assume that the better they can simulate social and affective behavior, the more effective the robotic partner is. Yet, it is not always clear what natural behavior is, nor whether maximal humanlikeness equals optimal user satisfaction. 3) Multidisciplinary design teams require transdisciplinary theory to create mutual understanding and cover the wide-ranging facets of robot design. However, combining diverse theories from different domains means confronting major unification problems. 4) Empirical approaches to robot design usually miss out on logic consistency within theory. Conversely, formalization and mathematical modeling often lack external validity. Verification before validation seems to be the new way to go. 5) Finally, if it is actually possible to create lifelike, affective, sociable androids, the cognitive models underlying their behavior may be open to abuse, for example in malicious software that spies on people.

Drawing upon the theory of human–robot interaction (HRI), this study examined the relations among perceived characteristics of fashion robot advisors (FRAs), consumers’ negative preconceptions toward robots, and positive dispositions toward technology to identify network differences in adoption and nonadoption groups. For interviews, pretests, and main data collection, we presented video clips of FRAs as stimuli. Based on the data ( n = 464) collected via an online survey, we conducted psychological network analysis to explore defining factors that differentiate adoption and nonadoption groups. The results indicate that perceived characteristics of social intelligence, humanlikeness, and knowledgeableness combined with a positive disposition of technological self-efficacy lead to adoption of FRAs. This study contributes to the literature on the theory of HRI and technology acceptance models, particularly in fashion retail sectors. Furthermore, this study provides a new graphical approach to networks that conceptualizes shoppers’ adoption of technology as a complex interplay of psychological attributes.

Robots are rapidly becoming part of our lives, coexisting, interacting, and collaborating with humans in dynamic and unstructured environments. Mapping of human to robot motion has become increasingly important, as human demonstrations are employed in order to “teach” robots how to execute tasks both efficiently and anthropomorphically. Previous mapping approaches utilized complex analytical or numerical methods for the computation of the robot inverse kinematics (IK), without considering the humanlikeness of robot motion. The scope of this work is to synthesize humanlike robot trajectories for robot arm-hand systems with arbitrary kinematics, formulating a constrained optimization scheme with minimal design complexity and specifications (only the robot forward kinematics (FK) are used). In so doing, we capture the actual human arm-hand kinematics, and we employ specific metrics of anthropomorphism, deriving humanlike poses and trajectories for various arm-hand systems (e.g., even for redundant or hyper-redundant robot arms and multifingered robot hands). The proposed mapping scheme exhibits the following characteristics: (1) it achieves an efficient execution of specific human-imposed goals in task-space, and (2) it optimizes anthropomorphism of robot poses, minimizing the structural dissimilarity/distance between the human and the robot arm-hand systems.

Anthropomorphism is a common phenomenon when people attribute human characteristics to non-human objects. It plays an important role in acceptance of robots in natural human environments. Various studies in the field of Human-Robot Interaction (HRI) show that there are various factors that can affect the extent to which a robot is anthropomorphized. However, our knowledge of this phenomenon is segmented, as there is a lack of a coherent model of anthropomorphism that could consistently explain these findings. A robot should be able to adjust its level of anthropomorphism to a level that can optimize its task performance. In order to do that, robotic system designers must know which characteristics affect the perception of robots' anthropomorphism. Currently, existing models of anthropomorphism emphasize the importance of the context and perceiver in this phenomenon, but provide little guidelines regarding the factors of a perceived object that are affecting it. The proposed reverse process to anthropomorphization is known as dehumanization. In the recent years research in social psychology has found which characteristics are deprived from people who are perceived as subhumans or are objectified. Furthermore, the process of dehumanization is two dimensional rather than unidimensional. This thesis discusses a model of anthropomorphism that uses characteristics from both dimensions of dehumanization and those relating to robots' physical appearance to affect the anthropomorphism of a robot. Furthermore, involvement of implicit and explicit processes in anthropomorphization are discussed. In this thesis I present five empirical studies that were conducted to explore anthropomorphism in HRI. Chapter 3 discusses development and validation of a cognitive measurement of humanlikeness using the magnitude of the inversion effect. Although robot stimuli were processed more similarly to human stimuli rather than objects and induced the inversion effect, the results suggest that this measure has limited potential for measuring humanlikeness due to the low variance that it can explain. The second experiment, presented in Chapter 4 explored the involvement of Type I and Type II processing in anthropomorphism. The main findings of this study suggest that anthropomorphism is not a result of a dual-process and self-reports have a potential to be suitable measurement tools of anthropomorphism. Chapter 5 presents the first empirical work on the dimensionality of anthropomorphism. Only perceived emotionality of a robot, but not its perceived intelligence, affects its anthropomorphization. This finding is further supported by a follow up experiment, presented in Chapter 6, that shows that Human Uniqueness dimension is less relevant for a robot's anthropomorphiazability than Human Nature (HN) dimension. Intentionality of a robot did not result in its higher anthropomorphizability. Furthermore, this experiment showed that humanlike appearance of a robot is not linearly related with its anthropomorphism during HRI. The lack of linear relationship between humanlike appearance and attribution of HN traits to a robot during HRI is further supported by the study described in Chapter 7. This last experiment shows also that another factor of HN, sociability, affects the extent to which a robot is anthropomorphized and therefore the relevance of HN dimension in the process of anthropomorphization. This thesis elaborates on the process of anthropomorphism as an important factor affecting HRI. Without fully understanding the process itself and what factors make robots to be anthropomorphized it is hard to measure the impact of anthropomorphism on HRI. It is hoped that understanding anthropomorphism in HRI will make it possible to design interactions in a way that optimizes the benefits of that phenomenon for an interaction.