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Randomly Generating Stereotypes: Can We Understand Implicit Attitudes with Random Generation?
Abstract
Societal expectations have been found to determine which social roles (e.g., jobs) people should occupy. Previously, however, these beliefs have been mainly explored using implicit measures such as sequential priming tasks where responding to expected (vs. conflicting) information is facilitated. We applied a random generation paradigm where participants said aloud the first names of hypothetical people working in various professions. This revealed that more female (male) first names were uttered for the female-typical (male-typical) occupations reflecting the societal gender stereotypes and the environmental statistics. Furthermore, the proportion of female and male names generated for each profession predicted participants' performance in a sequential priming task (prime = professions from random generation task, target = female vs. male face) better than the environmental statistics or participants' explicit gender ratio estimates of these jobs. Collectively, these findings offer a new method for exploring the internal representations elicited by cultural expectations.
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