Listening to Understand: The Role of High-Quality Listening on Speakers’ Attitude Depolarization During Disagreements
Abstract
Disagreements can polarize attitudes when they evoke defensiveness from the conversation partners. When
a speaker talks, listeners often think about ways to counterargue. This process often fails to depolarize
attitudes and might even backfire (i.e., the Boomerang effect). However, what happens in disagreements if
one conversation partner genuinely listens to the other’s perspective? We hypothesized that when
conversation partners convey high-quality listening—characterized by attention, understanding, and
positive intentions—speakers will feel more socially comfortable and connected to them (i.e., positivity
resonance) and reflect on their attitudes in a less defensive manner (i.e., have self-insight). We further
hypothesized that this process reduces perceived polarization (perceived attitude change, perceived attitude
similarity with the listener) and actual polarization (reduced attitude extremity). Four experiments
manipulated poor, moderate, and high-quality listening using a video vignette (Study 1) and live interactions
(Studies 2–4). The results consistently supported the research hypotheses and a serial mediation model in
which listening influences depolarization through positivity resonance and nondefensive self-reflection.
Most of the effects of the listening manipulation on perceived and actual depolarization generalized across
indicators of attitude strength, specifically attitude certainty and attitude morality. These findings suggest
that high-quality listening can be a valuable tool for bridging attitudinal and ideological divides.
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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An Examination of the Moderating Effect of Core Self-Evaluations and the Mediating Effect of Self-Set Goals on the Primed Goal-Task Performance Relationship
Guy Itzchakov, Gary P. Latham
Goal Setting
An understudied issue in the goal priming literature is why the same prime can provoke different responses in different people. The current research sheds light on this issue by investigating whether an individual difference variable, core self-evaluations (CSE), accounts for different responses from the same prime. Based on the findings of experiments showing that individuals with high CSE have higher performance after consciously setting a task-related goal than individuals with lower CSE, two hypotheses were tested: (1) Individuals who score high on CSE perform better following a subconsciously primed goal for achievement than do individuals who score low on CSE, and (2) this effect is mediated by a self-set goal. Two laboratory experiments (n = 207, 191) and one field experiment (n = 62) provided support for the hypotheses. These findings suggest that personality variables such as the CSE can provide an explanation for the “many effects of the one prime problem”.
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