How do people perceive listeners?
Abstract
Listening is essential in shaping social interactions,
relationships and communication. While listening research
has generated significant insights on how speakers benefit
from good listening, one fundamental question has been
largely overlooked: how do people perceive listeners?
This gap is crucial for understanding how perceptions of
listeners impact relational dynamics. In three studies (two
preregistered; total N = 1509), we assessed the attributes
and behaviours associated with good and bad listeners, and
whether the favourability of these attributes and behaviours
impact downstream consequences. In Study 1, participants
identified an acquaintance they judged as a good or bad
listener. Good listeners were rated higher in positive listening
attributes and behaviours, which mediated their perceived
warmth, competence and values. Study 2 replicated this using
a reverse correlation technique: one sample generated faces
of a good or bad listener, which were then evaluated by a
second, naïve sample. Consistent with Study 1, good listener
faces were rated higher in positive listening attributes and
behaviours, mediating perceptions of warmth, competence,
humility and values. Study 3 extended Study 2 by showing
that the effects were not due to a general positivity bias,
demonstrating the significant interpersonal consequences of
being perceived as a good or bad listener.
Harmony in Political Discourse? The Impact of High-Quality Listening on Speakers' Perceptions Following Political Conversations
Guy Itzchakov, Niv Navon, Jarret T. Crawford, Netta Weinstein, Kenneth G. DeMarree
Listening
Conversations with people who hold opposite partisan attitudes can elicit defensiveness, reinforce extreme attitudes, and undermine relationships with those with opposing views. However, this might not be the case when speakers experience high-quality (attentive, 2 understanding, and non-judgmental) listening from their conversation partners. We hypothesized that high-quality listening will increase speakers’ positive views toward, and their willingness to further interact with, others who hold politically opposed attitudes, and that these effects will be mediated by greater state openness. We conducted three experiments using different modalities to manipulate listening. In Study 1 (N = 379), participants recalled a conversation with an opposing political party member, with listening quality described as high-quality, low-quality, or control. Study 2 (N = 269) used imagined interactions, with participants reading vignettes describing either high-quality listening or a control condition. In Study 3 (preregistered; N = 741), participants watched a video of a listener modeling high-quality or moderate-quality listening and imagined themselves engaging in a similar interaction. Across studies, we found that high-quality listening consistently increased speakers’ state openness to politically opposed others, but did not change political attitudes. We found inconsistent evidence for speakers’ increased willingness to engage in future interactions (meta-analytic effect: 𝑑 = 0.20, p = .015). However, the indirect effect of listening on positive attitudes and willingness for future interactions through increased openness was consistently significant.
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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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