Overcoming ideology-consistent biases: does it help to make things easier?

Abstract

In 2 experiments, we attempted to reduce belief-consistent biases in interpretations of a polarized problem by making information easier to interpret. In the experiments, participants solved numerical problems that were either framed in a politically polarized (the effects of Muslim prayer rooms on support for Islamic extremism) or a neutral setting (the effects of a skin cream on skin rash). In both studies, the problems were presented twice, with the second presentation accompanied with an aid to facilitate problem-solving. In Experiment 1, this aid came in the form of an informative text on how to calculate the numbers to solve the problem. In Experiment 2, the aid provided participants with the first calculus necessary to solve the problem: transforming frequencies to percentages. Overall, results demonstrated belief-consistent responses in the polarized scenario when participants attempted to solve the first problem (higher accuracy when the correct conclusion was in line with participants’ ideology). Information on how to calculate the problem (Experiment 1) only slightly reduced the biased responses, whereas the added percentages (Experiment 2) led to a substantial reduction of the bias. Thus, we demonstrate that the facilitation of complex information on a polarized topic reduces biases in favor of rational reasoning. People sometimes hold beliefs that contradict the best available evidence. While some of these beliefs are trivial, they can also concern serious issues. For example, disbelieving the efficacy of COVID-19 vaccines, or the threat (and cause) of climate change, may not only impact one’s own life but also the lives of others. One explanation for the rejection of well-based facts (‘knowledge resistance’) is motivated reasoning. A vast body of research demonstrates that people often selectively seek out, attend to, and accept information that is consistent with their beliefs, and conversely, downplay, distort, and discredit information that contrasts these beliefs (e.g., Baker et al., 2020; Epsley & Gilovich, 2016; Hameleers & van der Meer, 2020; Kunda, 1999; Lord et al., 1979; Taber & Lodge, 2006). However, people do not conclude whatever they want to in a given situation regardless of the actual information available. Indeed, the ability to correctly perceive and interpret the world from observation is what allows us to learn and acquire knowledge, and in the extreme case, to survive. Our motivation to view the world in ways that confirm our beliefs thus competes with a motivation to be accurate. A critical question for anyone attempting to communicate important information is to understand what factors may affect people’s tendency to use any of these 2 strategies. Carefully scrutinizing information can be costly in terms of time and effort, and the importance of understanding a situation correctly can be expected to be balanced against the effort needed to do so (e.g., Kunda, 1990). When it comes to interpretations of complex information, such as estimating distributions from ranges (Dieckmann et al., 2017), or drawing conclusions about the relation between 2 variables from a 2 × 2 table (Kahan et al., 2017), a strong desire to be accurate should be required to motivate the effort to understand properly. If such motivation is lacking, people may turn to their general understanding of the world, and if the target information concerns a topic they have previous beliefs about, they will use these beliefs as heuristics for understanding. However, if the conclusion from a set of information is more directly evident, requiring little, or no effort to understand, then one should expect people’s interpretations to be less influenced by their previous beliefs even when the need to be accurate is low. Hence, a possible strategy to reduce people’s tendency for biased understanding of new information when accuracy motivation is low could be to try to reduce the complexity of the target information. In 2 experiments, we evaluate this hypothesis by examining if we can reduce people’s biased interpretations of politically polarized numerical data by reducing information complexity. Specifically, we reduce the number of calculations needed to arrive at the correct interpretation for a set of numerical data. If motivated biases can be reduced—or even eliminated—by facilitating interpretations of information, efforts to disambiguate publicly conveyed information could be an important step toward curing knowledge resistance.