In three experiments using 977 participants, we investigated whether people would show belief bias by letting their prior beliefs on politically charged topics unduly influence their reasoning when updating beliefs based on evidence. Participants saw data from fictional studies and made judgments of how strongly COVID-19 mitigation measures influenced the number of COVID-19 cases (political problems) or a medicine influenced number of headaches (neutral problems). Based on rational Bayesian models using strong versus weak priors to represent biased beliefs about causal strength, we predicted that people who strongly supported the use of mitigation measures (mainly liberals) would overestimate causal strength on political problems relative to neutral problems while those who strongly opposed mitigation measures (mainly conservatives) would underestimate strength on political problems. Results suggested that belief bias is driven more by specific beliefs relevant to the reasoning context than by general attitudinal factors like political ideology. In Experiments 1 and 2, liberals and conservatives who strongly supported mitigation measures overestimated strength on political problems. In Experiment 3, conservatives who strongly opposed the use of mitigation measures underestimated causal strength on political problems and conservatives who supported mitigation measures made higher strength judgments on political problems than those who opposed these measures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

中文翻译：

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关于 COVID-19 的因果推理中的偏见信念更新。

在使用 977 名参与者的三个实验中，我们调查了人们是否会在根据证据更新信念时让他们先前对政治话题的信念过度影响他们的推理，从而表现出信念偏见。参与者从虚构研究中看到数据，并判断 COVID-19 缓解措施对 COVID-19 病例数量（政治问题）或药物对头痛数量（中性问题）的影响程度。基于理性贝叶斯模型，使用强与弱先验来表示对因果强度的偏见信念，我们预测，相对于中立问题，强烈支持使用缓解措施的人（主要是自由派）会高估政治问题的因果强度，而强烈反对缓解措施的人（主要是保守派）会低估政治问题的强度。结果表明，信念偏见更多地是由与推理背景相关的特定信念驱动的，而不是由政治意识形态等一般态度因素驱动的。在实验 1 和 2 中，强烈支持缓解措施的自由派和保守派高估了政治问题的力量。在实验 3 中，强烈反对使用缓解措施的保守派低估了政治问题的因果强度，支持缓解措施的保守派对政治问题的判断力比反对这些措施的人高。（PsycInfo 数据库记录 (c) 2022 APA，保留所有权利）。