Listening

Can high quality listening predict lower speakers' prejudiced attitudes?

Abstract

Theorizing from humanistic and motivational literature suggests attitude change may occur because high-quality listening facilitates the insight needed to explore and integrate potentially threatening information relevant to the self. By extension, self-insight may enable attitude change as a result of conversations about prejudice. We tested whether high-quality listening would predict attitudes related to speakers' prejudices and whether self-insight would mediate this effect. Study 1 (preregistered) examined scripted conversations characterized by high, regular, and poor listening quality. In Study 2, we manipulated high versus regular listening quality in the laboratory as speakers talked about their prejudiced attitudes. Finally, Study 3 (preregistered) used a more robust measure of prejudiced attitudes to testing whether perceived social acceptance could be an alternative explanation to Study 2 findings. Across these studies, the exploratory (pilot study and Study 2) and confirmatory (Studies 1 & 3) findings were in line with expectations that high, versus regular and poor, quality listening facilitated lower prejudiced attitudes because it increased self-insight. A meta-analysis of the studies (N = 952) showed that the average effect sizes for high-quality listening (vs. comparison conditions) on self-insight, openness to change and prejudiced attitudes were, ds = 1.19, 0.46, 0.32 95%CIs [0.73, 1.51], [0.29, 0.63] [0.12, 0.53], respectively. These results suggest that when having conversations about prejudice, high-quality listening modestly shapes prejudice following conversations about it, and underscores the importance of self-insight and openness to change in this process.
Guy Itzchakov, Harry T. Reis, Netta Weinstein
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Listening
Social psychologists have a longstanding interest in the mechanisms responsible for the beneficial effects of positive social connections. This article reviews and integrates two emerging but to this point disparate lines of work that focus on these mechanisms: high-quality listening and perceived partner responsiveness. We also review research investigating the downstream consequences of high-quality listening and perceived partner responsiveness: the how and why of understanding the process by which these downstream benefits are obtained. High-quality listening and perceived partner responsiveness, though not isomorphic, are related constructs in that they both incorporate several key interpersonal processes, such as understanding, positive regard, and expressions of caring for another person. We develop a theoretical model for representing how listening embodies one form of interactive behavior that can promote (or hinder) perceived partner responsiveness and its downstream affective, cognitive, and behavioral effects. Finally, we discuss our model’s implications for various social-psychological domains, such as social cognition, self-evaluation, constructive disagreements, and interpersonal relationships.
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Netta Weinstein, Guy Itzchakov, Michael R. Maniaci
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Attitudes
Conversational artificial intelligence (AI) can be harnessed to provide supportive parasocial interactions that rival or even exceed social support from human interactions. High-quality listening in human conversations fosters social connection that heals interpersonal wounds and lessens loneliness. While AI can furnish advice, listening involves the speakers’ perceptions of positive intention, a quality that AI can only simulate. Can such deep-seated support be provided by AI? This research examined two previously siloed areas of knowledge: the healing capabilities of human interpersonal listening, and the potential for AI to produce parasocial experiences of connection. Three experiments (N = 668) addressed this question through manipulating conversational AI listening to test effects on perceived listening, psychological needs, and state loneliness. We show that when prompted, AI could provide high-quality listening, characterized by careful attention and a positive environment for self-expression. More so, AI’s high-quality listening was perceived as better than participants’ average human interaction (Studies 1–3). Receiving high-quality listening predicted greater relatedness (Study 3) and autonomy (Studies 2 and 3) need satisfaction after participants discussed rejection (Study 2–3), loneliness (Study 3), and isolating attitudes (Study 3). Despite this, we did not observe downstream lessening of loneliness typically observed in human interactions, even for those who were high in trait loneliness (Study 3). These findings clearly contrast with research on human interactions and hint at the potential power, but also the limits, of AI in replicating supportive human interactions.
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