What to Say and How to Say It: the Interplay of Self-Disclosure Depth, Similarity, and Interpersonal Liking in Initial Social Interactions
Abstract
We often initiate social relationships with others through revelations of personal information, or self-disclosure. Self-disclosure is heavily involved in shaping interpersonal liking, but there are disparate and sometimes contradictory findings in the literature regarding the causal relationship between them. Moreover, a lack of careful control in experimental designs in many existing studies failed to eliminate important confounding factors that might provide alternative explanations for the disclosure-liking relationship. Here, we examined the relationships between self-disclosure and interpersonal liking during initial social interactions, while carefully controlling for a potential confounding factor, similarity between the social partners.
Across the first five experiments, I independently manipulated disclosers’ self-disclosure depth, i.e., how personal and intimate the disclosures are, and their self-disclosed similarity with their social partners. High self-disclosed similarity was consistently found to lead to greater initial liking of a discloser. In comparison, the experiments failed to find support for the idea that people favor those who self-disclose more deeply, as suggested in the literature. In Experiment 6, I manipulated initial liking within a set of social partners and successfully replicated another disclosure-liking relationship identified in the literature, namely, the effect that people self-disclose to a greater extent to those whom they like. It was also found that, contrary to the expectation, participants’ risk-taking tendencies negatively predicted their self-disclosure depth to others. In Experiment 7, I extended the investigation to an emerging and novel social context and examined how self-disclosed similarity from an Artificially Intelligent (AI) agent influenced people’s perceptions of and responses to the agent. A significant interaction between the perceived identity of the partner (i.e., AI versus human) and level of self-disclosed similarity was found. The results were interpreted in light of the “uncanny valley effect”, which suggests that a high level of human realism displayed by an automatic agent could elicit unpleasant or “eerie” feelings.
Through this series of experiments, I iteratively developed the paradigm to more closely mimic real-world social disclosures. The findings help disentangle the causal relationship between self-disclosure and initial liking and provide insights into some of the subtleties and processes underlying relationship formation.