In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to interact with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user's needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots were able to adequately demonstrate these skills, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye-tracking, electroencephalography, or functional near-infrared spectroscopy embedded in interactive human-robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive processes and mechanisms involved in human-human interactions, and highlight the importance of perceiving others as intentional agents to activate these social brain areas. We then discuss how attribution of intentionality can positively affect human-robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human-robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles.

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Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social.

This paper examines how people’s trust and dependence on robot teammates providing decision support varies as a function of different attributes of the robot, such as perceived anthropomorphism, type of support provided by the robot, and its physical presence. We conduct a mixed-design user study with multiple robots to investigate trust, inappropriate reliance, and compliance measures in the context of a time-constrained game. We also examine how the effect of human accountability addresses errors due to over-compliance in the context of human robot interaction (HRI). This study is novel as it involves examining multiple attributes at once, thus enabling us to perform multi-way comparisons between different attributes on trust and compliance with the agent. Results from the 4×4×2×2 study show that behavior and anthropomorphism of the agent are the most significant factors in predicting the trust and compliance with the robot. Furthermore, adding a coalition-building preface, where the agent provides context to why it might make errors while giving advice, leads to an increase in trust for specific behaviors of the agent.CCS CONCEPTS • Human-centered computing $\rightarrow$ User studies; • Computer systems organization $\rightarrow$ Robotics. ACM Reference Format: Manisha Natarajan and Matthew Gombolay. 2020. Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3319502.3374839

Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction

Gaze following occurs automatically in social interactions, but the degree to which gaze is followed depends on whether an agent is perceived to have a mind, making its behavior socially more relevant for the interaction. Mind perception also modulates the attitudes we have towards others, and deter-mines the degree of empathy, prosociality and morality invested in social interactions. Seeing mind in others is not exclusive to human agents, but mind can also be ascribed to nonhuman agents like robots, as long as their appearance and/or behavior allows them to be perceived as intentional beings. Previous studies have shown that human appearance and reliable behavior induce mind perception to robot agents, and positively affect attitudes and performance in human-robot interaction. What has not been investigated so far is whether different triggers of mind perception have an independent or interactive effect on attitudes and performance in human-robot interaction. We examine this question by manipulating agent appearance (human vs. robot) and behavior (reliable vs. random) within the same paradigm and examine how congruent (human/reliable vs. robot/random) versus incongruent (human/random vs. robot/reliable) combinations of these triggers affect performance (i.e., gaze fol-lowing) and attitudes (i.e., agent ratings) in human-robot interaction. The results show that both ap-pearance and behavior affect human-robot interaction but that the two triggers seem to operate in iso-lation, with appearance more strongly impacting attitudes, and behavior more strongly affecting per-formance. The implications of these findings for human-robot interaction are discussed.

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You Look Human, But Act Like a Machine: Agent Appearance and Behavior Modulate Different Aspects of Human-Robot Interaction

Objective:We investigated the effects of exogenous oxytocin on trust, compliance, and team decision making with agents varying in anthropomorphism (computer, avatar, human) and reliability (100%, 5...

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A Little Anthropomorphism Goes a Long Way.

Ascribing mental states to non-human agents has been shown to increase their likeability and lead to better joint-task performance in human-robot interaction (HRI). However, it is currently unclear what physical features non-human agents need to possess in order to trigger mind attribution and whether different aspects of having a mind (e.g., feeling pain, being able to move) need different levels of human-likeness before they are readily ascribed to non-human agents. The current study addresses this issue by modeling how increasing the degree of human-like appearance (on a spectrum from mechanistic to humanoid to human) changes the likelihood by which mind is attributed towards non-human agents. We also test whether different internal states (e.g., being hungry, being alive) need different degrees of humanness before they are ascribed to non-human agents. The results suggest that the relationship between physical appearance and the degree to which mind is attributed to non-human agents is best described as a two-linear model with no change in mind attribution on the spectrum from mechanistic to humanoid robot, but a significant increase in mind attribution as soon as human features are included in the image. There seems to be a qualitative difference in the perception of mindful versus mindless agents given that increasing human-like appearance alone does not increase mind attribution until a certain threshold is reached, that is: agents need to be classified as having a mind first before the addition of more human-like features significantly increases the degree to which mind is attributed to that agent.

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Seeing Minds in Others – Can Agents with Robotic Appearance Have Human-Like Preferences?

Gaze following occurs automatically in social interactions, but the degree to which we follow gaze strongly depends on whether an agent is believed to have a mind and is therefore socially relevant for the interaction. The current paper investigates whether the social relevance of a robot can be manipulated via its physical appearance and whether there is a linear relationship between appearance and gaze following in a counter-predictive gaze cueing paradigm (i.e., target appears with a high likelihood opposite of the gazed-at location). Results show that while robots are capable of inducing gaze following, the degree to which gaze is passively followed does not linearly decrease with physical human-likeness. Rather, the relationship between appearance and gaze following is best described by an inverted u-shaped pattern, with automatic cueing effects (i.e., attending to the cued location) for agents of mixed human-likeness and reversed cueing effects (i.e., attending to the predicted location) for agents of either full human-likeness (100% human) or full robot-likeness (100% robot). The results are interpreted with regard to cognitive resource theory and design implications are discussed.

Agent Appearance Modulates Mind Attribution and Social Attention in Human-Robot Interaction

Humans are historically bad at performing cognitive tasks that require sustained attention. Social facilitation theory states that the real, imagined, or implied presence of other social agents can improve cognitive performance. Typically these agents are other humans, but the current study explores the possibility that robots can trigger social facilitation effects. We hypothesized that more humanlike social stimuli are (1) more likely to be ascribed internal states (e.g., having a mind, having emotions, having preferences), and (2) more likely to induce social facilitation on a vigilance task. Experiment 1 investigates the relationship between physical humanness and attributions of intentionality by comparing ratings of internal states for three agents (human, robot, and nonsocial). In Experiment 2, we examine whether physical humanness results in improved performance on a vigilance task through social facilitation. While Experiment 1 showed the expected positive relationship between human appearance and mind attribution, the degree to which mental states were attributed to an agent did not influence performance on the vigilance task. The implications for social robotics are discussed.

Does the Presence of Social Agents Improve Cognitive Performance on a Vigilance Task

Social robotics strives to create robots that enable social interactions similar to those experienced between two humans with the goal to increase performance in human-robot interaction (HRI). This...

Minimal Physical Features Required for Social Robots

The following information is missing from the Funding section: Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund.

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Molly C. Martini

Papers

Seeing Minds in Others – Can Agents with Robotic Appearance Have Human-Like Preferences?

Agent Appearance Modulates Mind Attribution and Social Attention in Human-Robot Interaction

Does the Presence of Social Agents Improve Cognitive Performance on a Vigilance Task

Minimal Physical Features Required for Social Robots

Correction: Seeing Minds in Others - Can Agents with Robotic Appearance Have Human-Like Preferences?