UC Davis

From intelligence in romantic partners to empathy in political leaders, people can readily think about what they like in other people. These abstract ideas about liking can be useful in many situations, but do they always align with people’s concrete experiences of liking? In this dissertation, I distinguish between ideas and experiences in interpersonal evaluation, and I argue that this distinction is useful because they can predict different kinds of decisions and help explain social psychological phenomena that appear paradoxical. In Chapter 1, I situate ideas versus experiences in a broader theoretical framework on attributes, and I conducted a series of studies showing that (1) ideas about liking for personal attributes can be affected by incidental features of the context, and that (2) ideas about liking versus experiences of liking predict different outcomes in the context of romantic attraction. In Chapter 2, I zero in on empathy and consider why people like the idea of empathy but not always those who show it. Across seven experiments, I found evidence that empathy has evaluative consequences for the empathizer beyond the empathic dyad. Findings from these experiments suggest that although people are often encouraged to empathize with disliked others, they are not always favored for doing so. In Chapter 3, I present a discussion and tutorial on a statistical technique central to my empirical work on ideas versus experiences, structural equation modeling (SEM). Specifically, I conducted a simulation study showing key factors that influence statistical power to detect true effects in SEM, and I introduce a free online app that helps researchers conduct power analysis for SEM. Taken together, these three chapters offer theoretical, empirical, and methodological advances for the study of ideas versus experiences in interpersonal evaluations. Unpacking the distinct roles that ideas versus experiences play can help us understand the apparent disconnect between what people think they like and what drives their liking across many domains.