I know that I know nothing: Can puncturing the illusion of explanatory depth overcome the relationship between attitudinal dissimilarity and prejudice?

Abstract

I know that I know nothingVoelkel, J.G.; Brandt, Mark; Colombo, Matteo General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. People are prejudiced toward groups they perceive as having a worldview dissimilar from their own. This link between perceived attitudinal dissimilarity and prejudice is so stable that it has been described as a psychological law. The current research tests whether reducing people's (over-)confidence in their own understanding of policies by puncturing their illusion of explanatory depth in the political domain will reduce the link between perceived attitudinal dissimilarity and prejudice. In an initial pre-registered experiment (N = 296), we did not find support for our hypothesis, but exploratory analyses indicated that the hypothesized effect occurred for political moderates (but not for people who identified as strong liberals/conservatives). However, despite successfully manipulating people's understanding of policies, in the main study (N = 492) we did not replicate the result of the initial experiment. We suggest potential explanations for our results and discuss future directions for research on breaking the link between attitudinal dissimilarity and prejudice.ARTICLE HISTORY

Partisan ideological attitudes: Liberals are tolerant; the intelligent are intolerant.

“…However, extremity is a most important factor that needs to be considered (and controlled for) in modeling ideological intolerance because it is associated with increased dissimilarity from the outgroup, and as a result with stronger negative attitudes. Indeed, the association between dissimilarity and negative attitudes was even called a basic psychological “law” (Byrne & Nelson, 1965; Voelkel, Brandt, & Colombo, 2018). Thus, we hypothesize that ideological intolerance increases with extremity (Hypothesis 4) and, therefore, add extremity as an independent variable to our intolerance models 4…”

Inclusion Reduces Political Prejudice

“…We measured momentary prejudice toward the political outgroup with three items (disliking, social distance, perceived immorality). The first two items are common measures of prejudice (Correll, Judd, Park, & Wittenbrink, 2010), whereas the third item, perceived immorality, is an important dimension of social perception (Goodwin, Piazza, & Rozin, 2014;Skitka et al, 2005) and forms a reliable scale with disliking and social distance (Voelkel, Brandt, & Colombo, 2018). Disliking and perceived immorality were measured with slider ratings (defaults at 50): "Right now, how cold or warm do you feel towards [liberals/conservatives]?"…”

Improving decision making through visual knowledge calibration

“…The breadth of experiments conducted on the IOED has shown that it not only affects technical or natural domains, but also concepts from other domains, such as political policies and positions, voting issues, historical events (Alter et al, 2010;Fernbach et al, 2013;Gaviria and Corredor, 2021;Roeder, 2016;Sloman and Vives, 2022;Vitriol and Marsh, 2018;Voelkel et al, 2018), mental disorders and treatments (Zeveney and Marsh, 2016), science based behavioral recommendations for health improvement and climate protection (Bromme et al, 2016), as well as explanations for artifical intelligence (Chromik et al, 2021).…”