Anthropomorphism is a far-reaching phenomenon that incorporates ideas from social psychology, cognitive psychology, developmental psychology, and the neurosciences. Although commonly considered to be a relatively universal phenomenon with only limited importance in modern industrialized societies—more cute than critical—our research suggests precisely the opposite. In particular, we provide a measure of stable individual differences in anthropomorphism that predicts three important consequences for everyday life. This research demonstrates that individual differences in anthropomorphism predict the degree of moral care and concern afforded to an agent, the amount of responsibility and trust placed on an agent, and the extent to which an agent serves as a source of social influence on the self. These consequences have implications for disciplines outside of psychology including human–computer interaction, business (marketing and finance), and law. Concluding discussion addresses how understanding anthropom...

/pdf/who-sees-human-the-stability-and-importance-of-individual-3bik5acfst.pdf

Who Sees Human?: The Stability and Importance of Individual Differences in Anthropomorphism

As robots become increasingly common in a wide variety of domains—from military and scientific applications to entertainment and home use—there is an increasing need to define and assess the trust humans have when interacting with robots. In human interaction with robots and automation, previous work has discovered that humans often have a tendency to either overuse automation, especially in cases of high workload, or underuse automation, both of which can make negative outcomes more likely. Frthermore, this is not limited to naive users, but experienced ones as well. Robotics brings a new dimension to previous work in trust in automation, as they are envisioned by many to work as teammates with their operators in increasingly complex tasks. In this chapter, our goal is to highlight previous work in trust in automation and human-robot interaction and draw conclusions and recommendations based on the existing literature. We believe that, while significant progress has been made in recent years, especially in quantifying and modeling trust, there are still several places where more investigation is needed.

The role of trust in human-robot interaction

Anthropomorphic robots, or robots with human-like appearance features such as eyes, hands, or faces, have drawn considerable attention in recent years. To date, what makes a robot appear human-like has been driven by designers» and researchers» intuitions, because a systematic understanding of the range, variety, and relationships among constituent features of anthropomorphic robots is lacking. To fill this gap, we introduce the ABOT (Anthropomorphic roBOT) Database---a collection of 200 images of real-world robots with one or more human-like appearance features (http://www.abotdatabase.info). Harnessing this database, Study 1 uncovered four distinct appearance dimensions (i.e., bundles of features) that characterize a wide spectrum of anthropomorphic robots and Study 2 identified the dimensions and specific features that were most predictive of robots» perceived human-likeness. With data from both studies, we then created an online estimation tool to help researchers predict how human-like a new robot will be perceived given the presence of various appearance features. The present research sheds new light on what makes a robot look human, and makes publicly accessible a powerful new tool for future research on robots» human-likeness.

https://dl.acm.org/doi/pdf/10.1145/3171221.3171268

What is Human-like?: Decomposing Robots' Human-like Appearance Using the Anthropomorphic roBOT (ABOT) Database

A transition in robotics from tools to teammates is underway, but, because it is in an early state, experience with intelligent robots and agents is limited. As such, human mental models of intelligent robots are primitive, easily influenced by superficial characteristics, and often incomplete or inaccurate. This paper investigates the factors that influence mental models of robots, and explores solutions for the formation of accurate and useful mental models with a specific focus on military applications. Humans must possess a clear and accurate understanding of how robots communicate and operate, particularly in military settings where intelligent, autonomous robotic agents are desired. Complete and accurate mental models in these hazardous and critical applications will reduce the inherent danger of automation disuse or misuse. Implications for training and developing appropriate trust are also discussed.

From Tools to Teammates: Toward the Development of Appropriate Mental Models for Intelligent Robots

Gone are the days of robots solely operating in isolation, without direct interaction with people. Rather, robots are increasingly being deployed in environments and roles that require complex social interaction with humans. The implementation of human-robot teams continues to increase as technology develops in tandem with the state of human-robot interaction (HRI) research. Trust, a major component of human interaction, is an important facet of HRI. However, the ideas of trust repair and trust violations are understudied in the HRI literature. Trust repair is the activity of rebuilding trust after one party breaks the trust of another. These trust breaks are referred to as trust violations. Just as with humans, trust violations with robots are inevitable; as a result, a clear understanding of the process of HRI trust repair must be developed in order to ensure that a human-robot team can continue to perform well after a trust violation. Previous research on human-automation trust and human-human trust can serve as starting places for exploring trust repair in HRI. Although existing models of human-automation and human-human trust are helpful, they do not account for some of the complexities of building and maintaining trust in unique relationships between humans and robots. The purpose of this article is to provide a foundation for exploring human-robot trust repair by drawing upon prior work in the human-robot, human-automation, and human-human trust literature, concluding with recommendations for advancing this body of work.

Toward an Understanding of Trust Repair in Human-Robot Interaction: Current Research and Future Directions

Participants were introduced to one of three robots-a bipedal Robosapien, a treaded vehicle, and a wheeled vehicle. They then used voice commands to guide this entity through a maze from a remote destination. Feedback was given via an arrow that showed the entity either responding to the voice commands or ignoring them. The same feedback was given in all conditions. However, participant ratings of mood and their attributions for the robots' abilities and functions differed. These results suggest that interactions with non-human intelligent entities are largely guided by pre-existing schemas. Additionally, individual differences in perceived control over a caregiving situation were predictive of responses to the robot, further supporting the idea that schemas for certain types of human-human interactions are activated by synthetic agents.

Those A-Maze-Ing Robots: Attributions of Ability are Based on Form, not Behavior:

Participants rated machine “faces” which varied in terms of eye size, eye shape, distance between eyes, and relationship to background color (white on black or black on white). Ratings were made for aggression, friendliness, intelligence, trustworthiness, and degree of animation. In addition, reaction time was collected for all ratings. Large, round, and close-set eyes were perceived most negatively across ratings. Aggression ratings were predicted by simple variables, whereas trustworthiness ratings were predicted by interactions among variables. Some judgments of form require the assessment of specific features, whereas others rely on a “gestalt” assessment of many features simultaneously. Humans attribute personality characteristics to minimal features, suggesting that form of intelligent artifacts is important in predicting human interactions with that item.

When Function Follows Form: Anthropomorphism of Artifact “Faces”:

A 208-item scale was developed to measure self-reported anthropomorphic tendencies during interactions with various non-human entities. The potential targets of anthropomorphism included technology-laden machines such as computers, other objects such as backpacks, living things such as houseplants, and abstract entities such as a god or higher power. Scale items assessed the degree to which participants agreed with statements regarding the perceived attributes of the entities, speech directed toward the entities and the treatment of the entities. A factor analysis suggested that the scale measures four independent types of anthropomorphism: “extreme” anthropomorphic tendencies, anthropomorphism of a god or higher power, anthropomorphism of pets, and “negative” anthropomorphism. Further analyses indicated that anthropomorphic tendencies were self-reported when pertaining to pets and a god or higher power. However, participants tended not to self-report inappropriate “negativeâ” anthropomorphism toward comp...

Developing and Anthropomorphic Tendencies Scale

Participants rated robotic forms on three scales: perceived aggression, intelligence, and animation. The robot bodies varied along five dimensions: Types of edges (beveled or squared), method of movement (wheels, legs, spider legs, or treads), number of movement generators (2 or 4), body position (upright or down), and presence of arms (present or absent). Across ratings, movement method and presence of arms were the strongest predictors of participant perceptions. Legs and arms, both human characteristics, were associated with more positive attributions. Minimal affective characteristics, as displayed by the body design, are important in user perceptions of use and ability.

Anthropomorphism of Robotic Forms: A Response to Affordances?

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Those A-Maze-Ing Robots: Attributions of Ability are Based on Form, not Behavior:

When Function Follows Form: Anthropomorphism of Artifact “Faces”:

Developing and Anthropomorphic Tendencies Scale

Anthropomorphism of Robotic Forms: A Response to Affordances?