Feeling torn and fearing rue: Attitude ambivalence and anticipated regret as antecedents of biased information seeking
Abstract
Theoretical work on attitudinal ambivalence suggests that anticipated regret may play a role in causing
awareness of contradictions that subsequently induce a feeling of an evaluative conflict. In the present paper we empirically examined how the anticipation of regret relates to the association between the simultaneous pre-
sence of contradictory cognitions and emotions (objective ambivalence), and the evaluative conflict associated with it (subjective ambivalence), in the context of decision-making. Across three studies (Ns = 204,127,244), manipulating both objective ambivalence and regret, we consistently found that when a dichotomous ambiva-
lent choice had to be made, (objectively) ambivalent attitude holders for whom feelings of anticipated regret were made salient reported higher levels of subjective-attitude ambivalence than participants in the other
conditions. Moreover, in Studies 2 and 3 we found that the effect of anticipated regret on subjective ambivalence
had consequences on information processing. Specifically, anticipating regret made ambivalent participants
search for attitude-congruent information. This effect was mediated by the increase in subjective ambivalence. This work provides the first empirical evidence for the role of regret in the association between objective-and-
subjective attitude ambivalence, and its consequences.
Avoiding harm, benefits of interpersonal listening, and social equilibrium adjustment: An applied psychology approach to side effects of organizational interventions
Guy Itzchakov, Justin B. Keeler, Walter J. Sowden, Walter Slipetz, and Kent S. Faught
Listening
Creating positive change in the direction intended is the goal of organizational interventions. Watts et al. (2021) raise this issue of “side effects,” which include changes that are unintended and often in the opposite direction of the organizational intervention. With our expertise in applied psychology, military psychiatry/neuroscience, organizational behavior, and corporate safety, we argue for three additional factors for consideration: avoiding harm, the benefits of high-quality interpersonal listening, and a discussion of side effects as a natural part of the change process. We offer these as a means of extending the conversation begun by Watts et al.
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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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