Politically Motivated Causal Evaluations of Economic Performance: Despite being shown identical data, participants differed in their judgments of the graphs along party lines

Politically Motivated Causal Evaluations of Economic Performance. Zachary A. Caddick  Benjamin M. Rottman. Department of Psychology, University of Pittsburgh. http://www.lrdc.pitt.edu/rottman/pubs/38/2019CaddickRottmanCogSci.pdf

Abstract: The current study seeks to extend research on motivated reasoning by examining how prior beliefs influence the interpretation of objective graphs displaying quantitative information. The day before the 2018 midterm election, conservatives and liberals made judgments about four economic indicators displaying real-world data of the US economy. Half of the participants were placed in an 'alien cover story' condition where prior beliefs were reduced under the guise of evaluating a fictional society. The other half of participants in the 'authentic condition' were aware they were being shown real-world data. Despite being shown identical data, participants in the Authentic condition differed in their judgments of the graphs along party lines. The participants in the Alien condition interpreted the data similarly, regardless of politics. There was no evidence of a „backfire‟ effect, and there was some evidence of belief updating when shown objective data.

Keywords: motivated reasoning; politics; biases; reasoning; decision-making

Introduction

Previous research has shown that individuals often reason differently about information depending on whether it is congruent with their prior beliefs. Individuals tend to more easily accept information that is congruent with prior beliefs and desires and discount information that is incongruent with prior beliefs and desires. This process is known as motivated reasoning. In the current research, we studied the influence of political attitudes on how people interpret time series graphs of the economy. This research is at the intersection of two fields: causal reasoning about time series data, and motivated reasoning.

Motivated Reasoning and Causal Reasoning: Similarities and Differences

The fields of motivated reasoning and causal reasoning have long been intimately connected in certain ways, yet also distant in other ways. The current research aims to advance both of these fields, and to advance research on the intersection of the two.

In one aspect, these two fields have studied similar questions about the role of prior beliefs and desires on the acceptance or rejection of new information. On the causal reasoning side, there has been considerable research into how people incorporate new information with prior causal beliefs (e.g., Alloy & Tabachnik, 1984). Furthermore, many of the particular topics that have been studied in the field of motivated reasoning have had to do with causal or at least predictive relations. For example, in a seminal work on motivated reasoning, Kunda (1987) found that people tend to believe that other people who have attributes similar to themselves are less likely to get divorced than people with dissimilar attributes. Note how in this study, the attribute is as a potential cause or predictor of the effect (divorce). Other research on motivated reasoning that is less directly related to causation still often studies acceptance of causalscientific explanations, for example, about global warming (Campbell & Kay, 2014).

On the other hand, there are also important differences between these fields. First, causal learning has traditionally been focused on the rational (Bayesian) updating of beliefs given new information, whereas motivated reasoning has focused on affective reasons for failing to update beliefs. A second difference, more relevant to the current research, is that most research on causal reasoning has focused on the inferential process - how a learner infers a cause-effect relationship from a set of data. In contrast, research on motivated reasoning does not involve inference. Instead, participants are typically presented with a fact or a set of facts, and the question is whether participants accept or reject the facts (e.g., Ranney & Clark, 2016).

One recent study on motivated reasoning has investigated inference from data, similar to causal reasoning research. Kahan, Peters, Dawson, and Slovic (2017) presented participants with quantitative information in 2x2 contingency tables about the number of cities that did or did not ban handguns in public and whether there was an increase or decrease in crime, and participants were asked to infer the relation between gun bans and crime. Despite being presented with quantitative data, participants were more likely to make correct inferences when the data supported their prior attitudes about guns. The current research is in a similar vein–it investigates the role of political attitudes on inferences about economic trends.