Deep Listening Training to Bridge Divides: Fostering Attitudinal Change through Intimacy and Self‐Insight
Abstract
Deep, high‐quality listening that offers a nonjudgmental approach, understanding, and careful attention when speakers share disparate views can have the power to bridge divides and change speakers' attitudes. However, can people be trained to provide such listening while disagreeing with what they hear, and if so, are the effects of the listening training sufficient for creating perceptible change during disagreements? This study, conducted with delegates (N=320) representing 86 countries experimentally tested a “deep” (otherwise termed “high quality“) listening training against a randomly assigned subgroup of attendees who served as a “waitlist” control. During a conversation with another participant on a subject about which they strongly disagreed, participants who had completed a 6‐h training over 3 weeks in high‐quality listening demonstrated improvements in their observed listening behaviors, reported higher levels of interactional intimacy with conversation partners, appeared to increase their self‐insight and subsequently, showed evidence of attitude change. Among the first studies to test semi‐causal outcomes of high‐quality listening training between attendees with diverse and contrary attitudes in a real‐world, cross‐national setting; we discuss the potential and limitations for listening training to support positive relations and an open mind in the context of discourse, disagreement and polarization.
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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Harry T. Reis and Guy Itzchakov
Listening