Attitudes

Exploring the connecting potential of AI: Integrating human interpersonal listening and parasocial support into human-computer interactions

Abstract

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.
Guy Itzchakov, Gary P. Latham
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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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Guy Itzchakov, Harry T. Reis
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Responsiveness
Can perceived responsiveness, the belief that meaningful others attend to and react supportively to a core defining feature of the self, shape the structure of attitudes? We predicted that perceived responsiveness fosters open-mindedness, which, in turn, allows people to be simultaneously aware of opposing evaluations of an attitude object. We also hypothesized that this process will result in behavior intentions to consider multiple perspectives about the topic. Furthermore, we predicted that perceived responsiveness will enable people to tolerate accessible opposing evaluations without feeling discomfort. We found consistent support for our hypotheses in four laboratory experiments (Studies 1–3, 5) and a diary study (Study 4). Moreover, we found that perceived responsiveness reduces the perception that one’s initial attitude is correct and valid. These findings indicate that attitude structure and behavior intentions can be changed by an interpersonal variable, unrelated to the attitude itself.
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