The Unintentional Nonconformist: Habits Promote Resistance to Social influence

Kama Muta, a relatively new construct, is an emotion of social connection that describes the feeling of being moved to love through five key dimensions. Despite the growing body of research on the beneficial outcomes of Kama Muta, little is known about its antecedents. To fill this gap, this research focuses on the emergence of Kama Muta during social interactions by specifically examining what triggers this emotion in conversations. The theory on Kama Muta suggests it emerges in response to sudden relationship intensification. We propose that, in conversation, this intensification is most likely triggered by high-quality listening. We examined whether high-quality listening, characterized by undivided attention, understanding, acceptance, nonjudgment, and positive intentions, is associated with Kama Muta for both speakers and listeners. Data were collected across three studies (total N = 1,126), employing scenarios (Study 1), recall (Study 2), and live online conversations conducted via Zoom (Study 3). We found general support for our hypotheses. Specifically, both speakers (Studies 1–3) and listeners (Studies 2–3) experiencing high-quality listening reported greater Kama Muta compared to those exposed to lower quality listening. The consistency of these results varied across different dimensions of Kama Muta. This work offers novel insights into a previously unexplored social behavior that can act as an antecedent of Kama Muta and contributes to the listening literature, which has predominantly focused on the effects on speakers. We discuss the theoretical and practical implications of these findings. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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.