Li Gong’s research while affiliated with Palo Alto University and other places

Research on human–human communication has identified that people apply different constraints in communication with each other. Application of such constraints as social concerns over feeling, imposition, and disapproval and task concerns over clarity and effectiveness has also been found to be influenced by people's self-construal, being independent or interdependent. Do these constraints and individual difference in self-construal matter in communication with humanoid robots? This study uses the theoretical framework of communication constraints to compare whether or not people of different self-construals apply social-oriented and task-oriented constraints differently to humanoid social robot targets. A total of 161 students from the University of Hawaii at Manoa participated in the study. The participants completed a questionnaire that determined their concern for the five communication constraints (feelings, nonimposition, disapproval, clarity, and effectiveness) in situations involving robots, as well as scales measuring self-construal. The results show interdependent self-construal related significantly with the concerns over avoiding hurting the humanoid's feelings, avoiding inconveniencing the humanoid robot, and avoiding being disliked by the humanoid robot. On the other hand, independent self-construal related significantly with the concern over clarity in communicating with the humanoid robot. However, self-construal did not influence one's concern of effectiveness (a task-oriented constraint) in interaction with humanoid robots. The results of the research offer new insight into the linkage between self-construal, a cultural concept at the individual level, and how human–robot communication is psychologically structured and constrained.

Ethnic identity tends to predict same-ethnicity preference. However, very little research has tested the impact of ethnic identity across messages that bear distinct relevance with different groups. In Experiment 1, Asian American participants read 4 pieces of disease-related information presented by 2 Asian and 2 White agents on a computer. Both strong and weak ethnic identifiers gave higher credibility ratings of the agents when the agents' ethnicity matched with ethnic relevance of the information. Experiment 2 assessed ethnicity preference by allowing participants to choose agents for 4 healthy-food Websites and also found the agent–information matching pattern. Ethnic identity did not have an effect. The findings are discussed within a framework that contrasts a social-identity orientation and an external-content orientation.

As humanoid social robots are developed rapidly in recent years and experimented in social situations, comparing them to humans provides insights into practical as well as philosophical concerns. This study uses the theoretical framework of communication constraints, derived in human–human communication research, to compare whether people apply social-oriented constraints and task-oriented constraints differently to human targets versus humanoid social robot targets. A total of 230 students from the University of Hawaii at Manoa participated in the study. The participants completed a questionnaire, which determined their concern for the five communication constraints (feelings, non-imposition, disapproval, clarity, and effectiveness) in situations involving humans or robots. The results show people were more concerned with avoiding hurting the human’s feelings, avoiding inconveniencing the human interactive partner, and avoiding being disliked by the human and less concerned with avoiding hurting the robot’s feelings, avoiding inconveniencing the robot partner, and avoiding being disliked by the robot. But people did not differ in their concerns of the two task-oriented constraints (clarity and effectiveness) in response to humans versus humanoid robots. The results of the research suggest that people are more likely to emphasize the social-oriented constraints in communication with humans.

Humanoid social robots are predicted to interact with humans in various domains of social life as robot technology keeps advancing. One area for understanding the impact of robots on human society is interracial relations. Would robots constitute a nonhuman outgroup to trigger human ingroup favoritism which will confine the boundary of racial prejudice? A study (N = 105) assessed Whites’ rank-ordered preferences for 15 White, Black and robot computer-synthesized characters. Explicit racial prejudice positively predicted White versus Black character preferences for liking and as one’s avatar, virtual friend, and virtual tutor. The implicit racial prejudice, measured with the Implicit Association Test (IAT), provided additional predictive utility for virtual friend. Among the 64 participants who reported minimal interest in robots, explicit racial prejudice negatively predicted preferences for Black over robot characters, showing a pattern that individuals with high prejudice preferred robot characters over Black ones. The results suggest alarming strength of racial prejudice and cast doubt on the notion of all-human ingroup favoritism in comparison to robots.

Testing the assumption that more anthropomorphic (human-like) computer representations elicit more social responses from people, a between-participants experiment (N = 168) manipulated 12 computer agents to represent four levels of anthropomorphism: low, medium, high, and real human images. Social responses were assessed with users’ social judgment and homophily perception of the agents, conformity in a choice dilemma task, and competency and trustworthiness ratings of the agents. Linear polynomial trend analyses revealed significant linear trends for almost all the measures. As the agent became more anthropomorphic to being human, it received more social responses from users.

Literatures on ethnic identity and acculturation have supported orthogonal conceptualization and separate measurements of ethnic identity and identification with the majority group or the larger society. The author further argues to conceptually differentiate identification with the majority group and identification with the larger society. A study with Asian American students (n=91) and African American students (n=115) in a large US Midwestern public university revealed no correlation between ethnic identity and identification with White Americans for Asian Americans and a small negative correlation at marginal significance for African Americans. These results support the orthogonal model. While identification with White Americans positively predicted national identity in regression analyses for both samples, ethnic identity also added a unique positive main effect, and additionally through an interaction effect suppressed the strength of identification with White Americans, for predicting national identity among American-born Asian Americans. Bicultural identity integrating ethnicity and nationality was suggested as the identity mechanism explaining this result. Thus, identification with the majority group and national identity were empirically shown to be different concepts, and ethnic identity can contribute to national identity. Suggestions for future research include regional comparative studies and deeper analysis of bicultural integration.

Computer-generated anthropomorphic characters are a growing type of communicator that is deployed in digital communication environments. An essential theoretical question is how people identify humanlike but clearly artificial, hence humanoid, entities in comparison to natural human ones. This identity categorization inquiry was approached under the framework of consistency and tested through examining inconsistency effects from mismatching categories. Study 1 (N = 80), incorporating a self-disclosure task, tested participants’ responses to a talking-face agent, which varied in four combinations of human versus humanoid faces and voices. In line with the literature on inconsistency, the pairing of a human face with a humanoid voice or a humanoid face with a human voice led to longer processing time in making judgment of the agent and less trust than the pairing of a face and a voice from either the human or the humanoid category. Female users particularly showed negative attitudes toward inconsistently paired talking faces. Study 2 (N = 80), using a task that stressed comprehension demand, replicated the inconsistency effects on judging time and females’ negative attitudes but not for comprehension-related outcomes. Voice clarity overshadowed the consistency concern for comprehension-related responses. The overall inconsistency effects suggest that people treat humanoid entities in a different category from natural human ones.