I Am Aware of My Inconsistencies but Can Tolerate Them: The Effect of High Quality Listening on Speakers’ Attitude Ambivalence

Extensive research has documented people’s desire for social partners who are responsive to their needs and preferences, and that when they perceive that others have been responsive, they and their relationships typically thrive. For these reasons, perceived partner responsiveness is well-positioned as a core organizing theme for the study of sociability in general, and close relationships in particular. Research has less often addressed the downstream consequences of perceived partner responsiveness for cognitive and affective processes. This gap in research is important because relationships provide a central focus and theme for many, if not most, of the behaviors studied by social psychologists. This chapter begins with an overview of the construct of perceived partner responsiveness and its centrality to relationships. We then review programs of research demonstrating how perceived partner responsiveness influences three core social-psychological processes: self-enhancing social cognitions, attitude structure, and emotion regulation. The chapter concludes with a brief overview of how deeper incorporation of relationship processes can enhance the informativeness and completeness of social psychological theories.

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.