Partisans overestimate the negative affect that results from exposure to opposing views; the affective forecasting error derives from underestimation of agreement

Selective exposure partly relies on faulty affective forecasts. Charles A. Dorison, Julia A. Minson, Todd Rogers. Cognition, https://doi.org/10.1016/j.cognition.2019.02.010

Highlights
• Partisans overestimate the negative affect that results from exposure to opposing views.
• The affective forecasting error derives from underestimation of agreement.
• Faulty affective forecasts partially underpin selective exposure.

Abstract: People preferentially consume information that aligns with their prior beliefs, contributing to polarization and undermining democracy. Five studies (collective N = 2455) demonstrate that such “selective exposure” partly stems from faulty affective forecasts. Specifically, political partisans systematically overestimate the strength of negative affect that results from exposure to opposing views. In turn, these incorrect forecasts drive information consumption choices. Clinton voters overestimated the negative affect they would experience from watching President Trump’s Inaugural Address (Study 1) and from reading statements written by Trump voters (Study 2). Democrats and Republicans overestimated the negative affect they would experience from listening to speeches by opposing-party senators (Study 3). People’s tendency to underestimate the extent to which they agree with opponents’ views drove the affective forecasting error. Finally, correcting biased affective forecasts reduced selective exposure by 24–34% (Studies 4 and 5).

Keywords: Selective exposureAffective forecastingFalse polarizationEmotion

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However, despite the benefits of holding accurate beliefs, the phenomenon of selective exposure to agreeing information has been well documented in social psychology (Frey, 1986), political science (Iyengar & Hahn, 2009; Sears & Freedman, 1967), and communications (Stroud, 2008). For example, one of the earliest studies on selective exposure demonstrated that mothers were more likely to listen to arguments that supported their beliefs regarding hereditary and environmental factors in childrearing than arguments that contradicted their beliefs (Adams, 1961). More recently, in the domain of political communication, conservatives in an experiment preferred to read articles from the conservative site Fox News, whereas liberals preferred to read articles from more liberal sources such as CNN and NPR (Iyengar & Hahn, 2009). These effects persist even with financial incentives on the line (Frimer, Skitka, & Motyl, 2017). Recent research has also examined how presentation order and structure moderate this phenomenon (Fischer et al., 2011; Jonas, Schulz-Hardt, Frey, & Thelen, 2001).

Implicit Association Test showed a self-other asymmetry, that people perceived a desirable IAT result to be more valid when it applied to themselves than to others, & the opposite held for undesirable IAT results

Mendonça, C., Mata, A., & Vohs, K. D. (2019). Self-other asymmetries in the perceived validity of the implicit association test. Journal of Experimental Psychology: Applied, http://dx.doi.org/10.1037/xap0000214

Abstract: The Implicit Association Test (IAT) is the most popular instrument in implicit social cognition, with some scholars and practitioners calling for its use in applied settings. Yet, little is known about how people perceive the test’s validity as a measure of their true attitudes toward members of other groups. Four experiments manipulated the desirability of the IAT’s result and whether that result referred to one’s own attitudes or other people’s. Results showed a self-other asymmetry, such that people perceived a desirable IAT result to be more valid when it applied to themselves than to others, whereas the opposite held for undesirable IAT results. A fifth experiment demonstrated that these self-other differences influence how people react to the idea of using the IAT as a personnel selection tool. Experiment 6 tested whether the self-other effect was driven by motivation or expectations, finding evidence for motivated reasoning. All told, the current findings suggest potential barriers to implementing the IAT in applied settings.

Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than an average human driver is not acceptably safe performance for most

Safer than the average human driver (who is less safe than me)? Examining a popular safety benchmark for self-driving cars. Michael A. Nees. Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.02.002

Highlights
•    The criterion of being safer than a human driver has become pervasive in the discourse on vehicle automation.
•    Most drivers perceive themselves to be safer than the average driver (the better-than-average effect).
•    This study replicated the better than average effect and showed that most drivers stated a desire for self-driving cars that are safer than their own perceived ability to drive safely.
•    Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than a human driver (on average) may not represent acceptably safe performance of self-driving cars for most drivers.

Abstract: Although the level of safety required before drivers will accept self-driving cars is not clear, the criterion of being safer than a human driver has become pervasive in the discourse on vehicle automation. This criterion actually means “safer than the average human driver,” because it is necessarily defined with respect to population-level data. At the level of individual risk assessment, a body of research has shown that most drivers perceive themselves to be safer than the average driver (the better-than-average effect). Using an online sample of U.S. drivers, this study replicated the better than average effect and showed that most drivers stated a desire for self-driving cars that are safer than their own perceived ability to drive safely before they would: (1) feel reasonably safe riding in a self-driving vehicle; (2) buy a self-driving vehicle, all other things (cost, etc.) being equal; and (3) allow self-driving vehicles on public roads. Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than a human driver (on average) may not represent acceptably safe performance of selfdriving cars for most drivers.