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4 strongly (b = −.172, SE = .075, p = .021) than statistical beliefs (β = .340, SE = .036, p < .001). Thus, results were no different from those conducted using Bayesian estimation. Moderating Effects Social Desirability. Although our within-person design ruled out the possibility that the difference between generic and statistical beliefs was due to differences between participants, we nevertheless explored whether the relationship of these beliefs with social judgments was moderated by participants’ tendency to provide socially desirable responses. Social desirability did not directly predict social judgments (β = .017 [−.030, .064], p = .244), nor did it moderate the relationship of statistical beliefs (β = .029 [−.011, .068], p = .077) or generic beliefs (β = −.025 [−.065, .016], p = .118) with social judgments. Participant Age. Because working memory and fluid reasoning abilities typically decline with age (e.g., Salthouse, 2015), we also investigated whether older (vs. younger) participants rely more on generic beliefs and less on statistical beliefs in their social judgments. Participant age significantly moderated the link between generic beliefs (β = .083 [.044, .123], p < .001), but not statistical beliefs (β = –.016 [–.052, .020], p = .193), and social judgments. Generic beliefs were more predictive of social judgments for older (+1 SD) participants (b = .641 [.583, .699], p < .001) relative to younger (–1 SD) participants (b = .474 [.420, .527], p < .001). Although the present data cannot differentiate between ageing vs. cohort effects, these results are nevertheless broadly consistent with the argument that generic beliefs are central to stereotype structure because they are cognitively simple. STUDY 2 Method Participants Participants received $1.00 for this study, and the following studies, due to inclusion of additional measures. Nineteen participants were excluded for failing the attention check

5 (excluded participants’ average prevalence estimate for “White people are humans” = 47.6%; included participants’ average > 99.9%). Stereotype Elicitation Thirty-three participants on MTurk were instructed to “list as many stereotypes about people and groups of people that come to mind,” regardless of whether they personally believed them to be true. Two coders independently read the items and generated a summary stereotype for any stereotype that was listed by at least three participants (e.g., the stereotype “women are weak” summarized participants’ stereotypes that women are “delicate,” “fragile,” and “weak”). Of all 581 stereotypes listed, 353 were listed by at least three participants. Disagreements were resolved via discussion, including a third party when needed. The final list of 30 items (see right side of Table 1 in the main text) comprised 18 items that were identical between coders, 8 items that addressed the same theme but with different phrasings (e.g., “Jewish people are rich” vs. “Jewish people are wealthy”; discrepancies resolved by discussion), and 4 items generated by only one of the two coders that were included after discussion. In Study 2, we retained participants’ terminology of “Black people” and “White people” in the final list of stereotypes rather than using “African Americans” and “Caucasians” (as in the psychological literature reviewed in Study 1). We chose this new language to reflect the language used by participants: No participants referred to “African Americans” and only one of the 33 referred to “Caucasians.” As in Study 1, our final list of stereotypes included counter-stereotypic filler items (e.g., “men are soft-spoken”) and an attention-check item (“White people are humans”). Also as before, participants completed a 1-min distracter task between measures. Presentation order of the measures was randomized; within each measure, item order was randomized. Supplementary Table 2 lists the means and standard deviations for each of the

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