Collaborative Research: Using Causal Explanations and Computation to Understand Misplaced Beliefs

因果解释提供了事件发生的原因的答案。对因果解释的信念（例如，投资新技术将使你可用于退休的钱增加）指导未来的行为（例如，投资加密货币）。但人们可以--而且经常相信--对世界的因果解释是错误的。理解一个不正确的因果解释的本质是什么使它可信，对于教人们拒绝对事件的不正确解释至关重要。在这项工作中，PI调查了是什么让人们对事件的不正确因果解释产生了吸引力，以及是什么鼓励了人们接受这些错位的信念。持有不正确的因果解释可能会产生严重的破坏性影响，例如人们追求无效的健康治疗或投资于没有回报的财务策略。因此，重要的是要更好地了解是什么使不正确的因果解释吸引人，以便可以部署策略来抵制它们的采用。在这项工作中，PI进行了一系列的研究，以提供一个很好的理解是什么性质的因果解释，使它吸引人。在研究中，PI探索了许多不同的解释因果要素。利用他们的结果，PI然后做一个初步的尝试，以减少不正确的因果解释的认可。这项研究对科学有更广泛的影响，让学生参与具有很强翻译成分的研究。这样的研究有助于学生将科学与现实世界联系起来，提高他们对科学和批判性思维的兴趣。此外，拟议中的工作将对科学素养产生更广泛的影响，因为科学解释中的内容可能会使他们或多或少地被相信。PI使用因果解释文献中的心理学方法来研究广泛的错位和不正确的因果解释的感知。这些方法包括让人们阅读解释，并对解释的说服力、满意度和可信度进行评价。此外，这些研究的参与者对解释的因果结构做出判断，例如解释包括多少因果因素，解释有多复杂，以及解释可以解释多少事件。PI使用大量的在线参与者样本，以确保具有许多不同信仰的人被纳入研究。在每项研究中，PI都让参与者对不正确的因果解释进行评级（例如，“吃糖是2型糖尿病的主要原因”）以及相同事件的基于事实的因果解释（例如，“2型糖尿病有多种原因，包括超重和遗传倾向”）。这种比较允许隔离什么是独特的错位的解释。使用机器学习，研究人员调查了事实和错误信息解释的特征结构的程度，而不仅仅是它们被感知的程度（例如，因果结构的复杂性），这可以允许在这些解释类型之间进行更自动的区分。最后，PI使用他们的研究结果创建了一组行为研究，在这些研究中，他们改变了解释的呈现方式，以探索呈现对解释认可的影响程度。具体来说，PI创造了新的因果解释，这些解释操纵了在早期实验中最能预测支持不正确因果信念的因果元素（例如，复杂性，因果因素的数量）。我们的目标是看看通过改变这些重要的因果因素，是否可以减少对不正确信念的认可。通过这些研究，我们可以更普遍地了解如何防止吸收不正确的信息，有利于以事实为基础的解释。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Causal explanations provide answers to why an event happened. Beliefs in causal explanations (for example, investing in new technologies will cause your available money for retirement to increase) guide which behaviors to engage in for the future (e.g., invest in crypto-currency). But people can – and often do – believe causal explanations of the world that are wrong. Understanding what in the nature of an incorrect causal explanation makes it believable is critically important for teaching people to reject incorrect explanations of events. In this work, the PIs investigate what makes incorrect causal explanations of events appealing to people and what encourages the adoption of these misplaced beliefs. Holding incorrect causal explanations can have critically damaging effects, such as people pursuing health treatments that are ineffective or investing in financial strategies that do not pay out. It is therefore important to better understand what makes incorrect causal explanations appealing so strategies can be deployed to counteract their adoption. In the work, the PIs conduct a series of studies to provide a strong understanding of what in the nature of a causal explanation makes it appealing. Across studies, the PIs explore many different causal elements of explanations. Using their results, the PIs then make a preliminary attempt to reduce endorsement of incorrect causal explanations. This research has a broader impact on science by involving students in research that has a strong translational component. Such research helps students connect science to the real-world, growing their interest in science and critical thinking at large. Additionally, the proposed work will have broader impacts on science literacy by isolating what in scientific explanations may make them more or less likely to be believed. The PIs use psychological methods from the causal explanation literature to study perceptions of a wide range of misplaced and incorrect causal explanations. These methods include having people read explanations and rate how compelling, satisfying, and believable the explanations are. In addition, participants in these studies make judgments about the causal structure of the explanations, such as how many causal factors the explanations include, how complex the explanations are, and how many events the explanations can explain. The PIs use large samples of online participants to ensure that people with many different beliefs are being included in the studies. In each study, the PIs have participants rate incorrect causal explanations (e.g., “eating sugar is the main cause of type 2 diabetes”) as well as fact-based causal explanations of the same events (e.g., “type 2 diabetes has multiple causes, including being overweight and having a genetic predisposition”). This comparison allows for isolation of what is unique about misplaced explanations. Using machine learning, the researchers investigate the degree to which there are characteristic structures of factual and misinformation explanations, beyond how they are perceived (e.g., the complexity of causal structure), which may allow for more automatic differentiation between these explanation types. Finally, the PIs use their findings to create a set of behavioral studies where they alter how explanations are presented to explore the degree to which presentation impacts endorsement of the explanations. Specifically, the PIs create new causal explanations that manipulate the causal elements that were most predictive of endorsement for incorrect causal beliefs in earlier experiments (e.g., complexity, number of causal factors). The goal is to see if by changing these important causal elements, endorsement of incorrect beliefs can be reduced. Through these studies we can learn more generally how to prevent the uptake of incorrect information in favor of fact-based explanations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.