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      Out-Group Mating Threat and Disease Threat Increase Implicit Negative Attitudes Toward the Out-Group Among Men

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          We investigated if perceiving an out-group as a threat to one's mating opportunities enhanced the implicit negative attitudes toward that out-group. In addition, we examined the moderating effect of disease threat on the relationship between an out-group mating threat and implicit negative attitudes toward that out-group. In Experiment 1, an out-group mating threat led to stronger implicit negative out-group attitudes as measured by the Implicit Association Test, but only for men with high chronic perceived vulnerability to disease. No such effects were found among women. In Experiment 2, men in the out-group mating threat condition who were primed with disease prevalence showed significantly stronger implicit negative attitudes toward the out-group than controls. Findings are discussed with reference to the functional approach to prejudice and sex-specific motivational reactions to different out-group threats.

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          Most cited references 22

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          Measuring individual differences in implicit cognition: the implicit association test.

          An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions oblige highly associated categories (e.g., flower + pleasant) to share a response key, performance is faster than when less associated categories (e.g., insect & pleasant) share a key. This performance difference implicitly measures differential association of the 2 concepts with the attribute. In 3 experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black + pleasant vs. White + pleasant for self-described unprejudiced White subjects).
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            Intergroup bias.

            This chapter reviews the extensive literature on bias in favor of in-groups at the expense of out-groups. We focus on five issues and identify areas for future research: (a) measurement and conceptual issues (especially in-group favoritism vs. out-group derogation, and explicit vs. implicit measures of bias); (b) modern theories of bias highlighting motivational explanations (social identity, optimal distinctiveness, uncertainty reduction, social dominance, terror management); (c) key moderators of bias, especially those that exacerbate bias (identification, group size, status and power, threat, positive-negative asymmetry, personality and individual differences); (d) reduction of bias (individual vs. intergroup approaches, especially models of social categorization); and (e) the link between intergroup bias and more corrosive forms of social hostility.
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              Understanding and using the implicit association test: I. An improved scoring algorithm.

              In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT (A.G. Greenwald, D.E. McGhee, & J.L.K. Schwartz, 1998). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.

                Author and article information

                Front Psychol
                Front. Psychology
                Frontiers in Psychology
                Frontiers Research Foundation
                04 May 2011
                : 2
                1simpleDepartment of Social Psychology, University of Groningen Groningen, Netherlands
                Author notes

                Edited by: Debra Lieberman, University of Miami, USA

                Reviewed by: Joshua M. Tybur, University of New Mexico, USA; Paul J. Watson, University of New Mexico, USA

                *Correspondence: Abraham P. Buunk, Faculty of Social and Behavioral Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, Netherlands. e-mail: a.p.buunk@

                This article was submitted to Frontiers in Evolutionary Psychology, a specialty of Frontiers in Psychology.

                Copyright © 2011 Klavina, Buunk and Pollet.

                This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 54, Pages: 8, Words: 7260
                Original Research


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