Year: 2023
Type: dissertation
Abstract: A central debate in social psychology concerns whether implicit measures capture conscious or unconscious mental content. Supporting the notion that cognitions reflected in implicit evaluations are consciously accessible, recent research has documented that people are able to accurately predict the pattern of their results on Implicit Association Tests (IATs). At the same time, the same participants systematically underestimated and mislabeled their test results and research has documented that people often react with surprise to their IAT results. This suggests that there is a lot that people do not know about their automatic cognitions. To reconcile these competing findings, this dissertation presents a framework which proposes that when and how people gain awareness of their automatic cognitions is determined by different concepts of awareness. Specifically, the framework distinguishes between introspective awareness (the ability to sense and report on one’s own automatic cognitions) and social calibration (the act of labeling those cognitions in accordance with conventions in the reference sample). I present three lines of research that are in line with the propositions of the framework and establish the main premises by (1) replicating the fundamental findings on which the framework is based, (2) examining why people report surprise at IAT results, even though they are able to report on them, and (3) applying the proposed concepts of awareness to a new attitudinal domain. I conclude that the cognitions reflected in implicit evaluations are neither conscious nor unconscious but that they often reside in a preconscious state until people pay attention to them, and that people often lack knowledge about the societal meaning of their automatic cognitions. This new nuanced perspective has important implications for theories of implicit social cognition and can ultimately help gain a better understanding of how much people know about themselves.
Author: Goedderz, Alexandra
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7006227022