The role of biased searching through memory in motivated social evaluation

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2019-05

Authors

Rigney, Anastasia Elena

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Abstract

People do not always perceive their social world dispassionately; they often engage in motivated social evaluation. That is, people often do not evaluate themselves or other people objectively, but rather in a way that conforms to how they want to see the social target (i.e., a desired directional conclusion). For example, research shows that people tend to see themselves and liked others in an unrealistically positive light (Kruger, 1999; Tajfel & Turner, 2004; Taylor & Brown, 1988). Several researchers have posited biased searches through memory as an underlying mechanism supporting the phenomenon of seeing people in a certain light (Showers & Cantor, 1985; Kunda, 1990, Dunning, 2015). That is, it has been suggested that when aiming to paint a social target in a certain light people search through their memories or beliefs in ways that help them find information to support their desired directional conclusion. However, the methods used in existing research have made it difficult to understand if social evaluations that have been labeled as motivated actually reflect people striving for desired directional conclusions and what role biased memory searches may play. The proposed dissertation research addresses two overarching questions to understand the role of biased searches through memory in social evaluation. Research Question 1: What is the role of a) biased searches through memory and b) directional conclusions in the greater reported memory for positive self-relevant feedback (compared to negative self-relevant feedback; Studies 1, 2, & 3)? Research Question 2: Does biased searching through memory operate similarly when aiming to paint someone in a particular light (regardless of the directional conclusion) or only in a flattering light (Study 4)? A combination of experimental, neuroimaging (i.e., Event Related Potential), and computational modeling (i.e., Signal Detection Theory and Drift Diffusion Model) methods are used to address these questions.

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