Dataset Information

Closed-minded cognition: Right-wing authoritarianism is negatively related to belief updating following prediction error.

ABSTRACT: When confronted with information that challenges our beliefs, we must often learn from error in order to successfully navigate the world. Past studies in reinforcement learning and educational psychology have linked prediction error, a measure of surprise, to successful learning from feedback. However, there are substantial individual differences in belief-updating success, and the psychological factors that influence belief updating remain unclear. Here, we identify a novel factor that may predict belief updating: right-wing authoritarianism (RWA), which is characterized by a desire for order, structure, and preservation of social norms. We hypothesized that because people who score high on RWA are motivated to preserve entrenched beliefs, they may often fail to successfully update their beliefs when confronted with new information. Using a novel paradigm, we challenged participants' false beliefs and misconceptions to elicit prediction error. In two studies, we found consistent evidence that high-RWA individuals were less successful at correcting their false beliefs. Relative to low-RWA individuals, high-RWA individuals were less likely to revise beliefs in response to prediction error. We argue that RWA is associated with a relatively closed-minded cognitive style that negatively influences belief updating.

SUBMITTER: Sinclair AH

PROVIDER: S-EPMC7384563 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Closed-minded cognition: Right-wing authoritarianism is negatively related to belief updating following prediction error.

Sinclair Alyssa H AH Stanley Matthew L ML Seli Paul P

Psychonomic bulletin & review 20201201 6

When confronted with information that challenges our beliefs, we must often learn from error in order to successfully navigate the world. Past studies in reinforcement learning and educational psychology have linked prediction error, a measure of surprise, to successful learning from feedback. However, there are substantial individual differences in belief-updating success, and the psychological factors that influence belief updating remain unclear. Here, we identify a novel factor that may pred ...[more]

PMID: 32720084

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Project description:Trust towards unrelated individuals is often conditioned by information about previous social interactions that can be derived from either personal or vicarious experience (e.g., reputation). Intergroup stereotypes can be operationalized as expectations about other groups' traits/attitudes/behaviors that heavily influence our behavioral predictions when interacting with them. In this study we investigated the role of perceived social dimensions of the Stereotype Content Model (SCM)-Warmth (W) and Competence (C)-in affecting trusting behavior towards different European national group members during the Trust Game. Given the well-known role of ideological attitudes in regulating stereotypes, we also measured individual differences in right-wing authoritarianism (RWA). In Experiment 1, we designed an online survey to study one-shot intergroup trust decisions by employing putative members of the European Union states which were also rated along SCM dimensions. We found that low-RWA participants' trusting behavior was driven by perceived warmth (i.e., the dimension signaling the benevolence of social intentions) when interacting with low-C groups. In Experiment 2, we investigated the dynamics of trust in a multiple-round version of the European Trust Game. We found that in low-RWA participants trusting behavior decreased over time when interacting with high-W groups (i.e., expected to reciprocate trust), but did not change when interacting with low-W groups (i.e., expected not to reciprocate trust). Moreover, we found that high-RWA participants' trusting behavior decreased when facing low-W groups but not high-W ones. This suggests that low-RWA individuals employ reputational priors but are also permeable to external evidence when learning about others' trustworthiness. In contrast, high-RWA individuals kept relying on stereotypes despite contextual information. These results confirm the pivotal role played by reputational priors triggered by perceived warmth in shaping social interactions.

Project description:People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments.

Dataset Information

Closed-minded cognition: Right-wing authoritarianism is negatively related to belief updating following prediction error.

Publications

Closed-minded cognition: Right-wing authoritarianism is negatively related to belief updating following prediction error.

Similar Datasets

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