Perceiving others as responsive lessens prejudice: The mediating roles of intellectual humility and attitude ambivalence
Abstract
Can perceived responsiveness, the extent to which an individual feels understood, validated, and cared for by
close others, reduce prejudiced attitudes? We hypothesized that perceived responsiveness by meaningful other
people would increase recipients’ intellectual humility and attitude ambivalence and that these changes would
reduce prejudice. Five studies (total N = 3362), four of which were preregistered, manipulated perceived
responsiveness by a specific person (Studies 1–3, 5) or measured the effects of perceived responsiveness by the
closest social network of the recipient (Study 4). All studies supported the hypotheses. Specifically, Studies 1 and
2 found that perceived responsiveness increased intellectual humility and attitude ambivalence and reduced
prejudice toward a group from a pre-determined list. Study 3 replicated these findings when participants freely
chose the social group. In Study 4, perceived responsiveness from individuals’ closest social networks predicted
the dependent variables a few days afterward, controlling for positive and negative affect and social desirability.
Finally, in Study 5, we added a condition of positive social interaction to rule out the possibility that the prior
findings were due to recalling an affectively positive experience. The effect of perceived responsiveness on
prejudice reduction (i.e., increased attitude favorability toward the social group) was not moderated by attitude
certainty (Study 2), anxious or avoidant attachment style (Study 2), or attitude morality (Study 3). This work
suggests that fostering perceived responsiveness can serve as a strategy for mitigating prejudice and promoting
more open-minded attitudes.
High-Quality Listening Supports Speakers’ Autonomy and Self-Esteem when Discussing Prejudice
Guy Itzchakov, Netta Weinstein
Listening
We examined how the experience of high-quality listening (attentive, empathic, and nonjudgmental) impacts speakers’ basic psychological needs and state self-esteem when discussing the difficult topic of a prejudiced attitude. Specifically, we hypothesized that when speakers discuss a prejudiced attitude with high-quality listeners, they experience higher autonomy, relatedness, and self-esteem than speakers who share their prejudiced attitudes while experiencing moderate listening. We predicted that autonomy needs satisfaction would mediate the effect of listening on speakers’ self-esteem even when relatedness, a well-documented predictor of self-esteem, is controlled for in mediation models. Two experiments that manipulated listening through in-person interactions with high-quality or moderate listeners supported these hypotheses. Theoretical and practical implications are discussed, with a focus on the role of experiencing high-quality listening for speakers’ state self-esteem during difficult conversations.
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Exploring the connecting potential of AI: Integrating human interpersonal listening and parasocial support into human-computer interactions
Netta Weinstein, Guy Itzchakov, Michael R. Maniaci
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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