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Xuan Zhao and Nicholas Epley, “Insufficiently Complimentary?: Underestimating the Positive Impact of Compliments Creates a Barrier to Expressing Them,” under review at Journal of Experimental Psychology: General.
Compliments increase the well-being of both expressers and recipients, yet people report giving fewer compliments than they should give, or would like to give. Seven experiments suggest that a reluctance to express genuine compliments may stem from underestimating the positive impact that compliments will have on recipients. Participants in three experiments wrote genuine compliments and then predicted how happy and awkward those compliments would make recipients feel. Participants consistently underestimated how positive the recipients would feel while overestimating how awkward recipients would feel (Experiments 1, 2, 6). These miscalibrated expectations are driven partly by an egocentric bias in which expressers primarily focus on how competent—compared to how warm—their compliments will be perceived by recipients (Experiments 1 and 2), creating an empathy gap between those who imagine how a compliment will be received compared to those who actually receive one (Experiments 3a and 3b). Because people’s interest in expressing a compliment is at least partly driven by their expectations of the recipient’s reaction, undervaluing a compliment creates a barrier to expressing them (Experiments 4 and 5). As a result, informing participants about a compliment’s surprisingly positive impact encouraged them to express more compliments (Experiment 6). We believe our findings reflect a more general tendency to underestimate the positive impact of their prosocial actions on others, leading people to be less prosocial than would be optimal for both their own and others’ well-being.
Xuan Zhao, Elizabeth Phillips, and Bertram F. Malle, “How People Infer a Humanlike Mind from a Robot Body,” under review at PNAS.
Robots are entering a wide range of society’s private and public settings, often with a strikingly humanlike appearance and emulating a humanlike mind. But what constitutes humanlikeness—in both body and mind—has been conceptually and methodologically unclear. In three studies based on a collection of 251 real-world robots, we report the first programmatic, bottom-up investigation of what constitutes a robot’s humanlike body, how people reason about robot minds, and critically, how specific dimensions of physical appearance are intricately yet systematically related to specific dimensions of inferred mind. Our results challenge three widespread assumptions about robot humanlikeness. First, we show that humanlike appearance is not a unitary construct; instead, three separate appearance dimensions—Body-Manipulators, Face, and Surface—each consist of a unique constellation of human appearance features and jointly constitute a robot’s humanlike body. Second, we find that the widely adopted two-dimensional structure of mind perception (i.e., agency and experience) does not capture systematic variations of inferences about robot minds; rather, a three-dimensional structure, encompassing mental capacities related to Affect, Social-Moral Cognition, and Reality Interaction, emerges from people’s inferences across a wide range of robots. Third, humanlike appearance does not uniformly lead to a global impression of a humanlike mind; instead, people specifically infer robots’ affective and moral capacities from facial and surface appearance, and their reality interaction capacities from body-manipulator appearance. Our findings reveal how physical appearance gives rise to the impression of a mind, even for a robot.
Xuan Zhao and Bertram F. Malle, “Seeing Through a Robot’s Eyes: Spontaneous Perspective Taking Toward Humanlike Machines,” under review at Psychological Science.
As robots rapidly enter society, how does human social cognition respond to their novel presence? Focusing on one foundational social-cognitive capacity—visual perspective taking—six studies reveal that people spontaneously adopt a robot’s unique perspective and do so with patterns of variation that mirror perspective taking toward humans. As with human agents, visual perspective taking of robots is enhanced when they display goal-directed actions (gaze and reaching vs. mere presence) and when the actions dynamically unfold over time (video vs. photograph). Importantly, perspective taking increases when the robot looks strikingly humanlike (an android) but is absent when the robot looks machine-like. This appearance-driven perspective taking is not due to inferences about the agent’s mind, because it persists when the agent obviously lacks a mind (e.g., a mannequin). Thus, the sight of robots’ superficial human resemblance may trigger and modulate social-cognitive responses in human observers originally evolved for human interaction.
Shirly Bluvstein*, Xuan Zhao*, Alexandra Barasch, and Juliana Schroeder, “Hello! How May I Helo You?”: How (Corrected) Errors Humanize a Communicator,” under review at OBHDP. [*equal authorship]
Today more than ever before, online writing (e.g., emails, texts, and social media posts) has become a primary means of communication. Because written communication lacks human nonverbal cues (e.g., voice), people frequently struggle to distinguish whether they are interacting with a human or chatbot online. The current research suggests a novel way to humanize writers: typographical errors (“typos”). Across four experiments (N = 1,253) that used ambiguous conversational counterparts (e.g., customer service agents that might be bots), communicators who made and subsequently corrected a typo, rather than making no typo or not correcting a typo, appeared more humanlike. Respondents consequently believed that the communicator was warmer and were more likely to disclose personal information to the communicator. These findings provide insight into when people are willing to share their personal data online. We discuss theoretical implications for humanization and practical implications for Internet privacy and building trust in organizations.
Xin Zhao, Xuan Zhao, Tamar Kushnir, and Hyowon Gweon, “Leaving a Choice for Others: Children’s Social Evaluations of Considerate Actions,” under review at Child Development.
Humans live in an interdependent world where even actions that are primarily self-serving (i.e., intended to fulfill one’s own needs) can have direct or indirect consequences for others. Thus, it seems critical that one be able to read these nuanced social signals and evaluate actions that are primarily self-serving based on the consequences those actions have for others. Over three studies (N = 566 children between ages 4 and 6 and N = 222 adults, from the U.S. and China), we investigated the mentalistic nature, developmental origins, and cultural dependency of such evaluations. We found that, by age 6 but not younger, both U.S. and Chinese children positively evaluate someone who takes something for themselves (a self-serving action) in a way that leaves a choice for another agent over someone who leaves no choice. We also found that these evaluations reflect a genuine understanding of the agent’s considerate intention, rather than a mere preference for item diversity. Furthermore, in light of the similar developmental patterns across cultures, we conclude that evaluations for others’ considerateness in self-serving actions may rely on the critical development of social-cognitive capacities between 4 and 6 years old independent from cultural influences.
Xuan Zhao and Nicholas Epley, “Others are surprisingly happy to help: How underestimating prosociality creates a misplaced barrier to help seeking,” in preparation for Psychological Science.
At some point, even the best of us need help. Yet people may struggle asking for help, partly out of the concern that others are unwilling and unhappy to do so. Five experiments conducted in field, laboratory, and online settings demonstrate that this concern is misplaced: Potential help-seekers systematically underestimated how willing strangers would be to help, how prosocially motivated the helpers were, and how happy the helpers felt after helping, while overestimating how much helpers felt coerced and inconvenienced by helping. In one experiment, participants underestimated how willing, interested, and happy others would be to take a picture of them in a park, while also overestimating how inconvenienced others would feel. Those in need of help seem to underestimate others’ prosociality—a mistake that creates a barrier to asking for help that improve outcomes for both those in need and for those who would be surprisingly happy to help.
Please see a full list of previous publications and working papers in my CV.
Do people spontaneously take a robot's perspective? Find out in my 6-minute talk at the "Research Matters!" event at Brown University.
Or click here to watch my 2-minute pitch for the SPSP annual grant competition on how feeling another other person's heartbeat increases prosocial behavior (which led to a live "interrogation" on the main stage at the 2017 SPSP annual convention and, eventually, the first prize).
I enjoy collaborating with people from diverse backgrounds. Below are a list of people that I have written papers with (or that I currently owe a paper to).
Bertram Malle (Brown University, Department of Cognitive, Linguistic & Psychological Sciences)
Nicholas Epley (University of Chicago, Booth School of Business)
Jane Risen (University of Chicago, Booth School of Business)
Hyowon Gweon (Stanford University, Department of Psychology)
Malte Jung (Cornell University, Department of Information Science)
Juliana Schroeder (University of California Berkeley, Hass School of Business)
Alixandra Barasch (New York University, Stern School of Business)
Guy Hoffman (Cornell University, Sibley School of Mechanical and Aerospace Engineering)
Heather Caruso (University of California Los Angeles, Anderson School of Management)
Elizabeth Phillips (U.S. Air Force of Academy, Department of Behavioral Sciences and Leadership)
Oriel FeldmanHall (Brown University, Department of Cognitive, Linguistic & Psychological Sciences)
Desmond Ong (National University of Singapore, Department of Information Systems and Analytics School of Computing)
Tamar Kushnir (Cornell University, Department of Human Development)
Alice (Xin) Zhao (Eastern China Normal University, Department of Educational Psychology)
Jamy Li (University of Twente, Department of Human-Media Interaction)
Wendy Ju (Cornell University, Jacobs Technion-Cornell Institute)
Roseanna Sommers (University of Chicago, Law School)
Corey Cusimano (Princeton University, Department of Psychology)
Jean Costa (Cornell University, Department of Information Science)
Shirly Bluvstein (New York University, Stern School of Business)