Digitally Inoculating Viewers Against Visual Misinformation With a Perceptual Training

错误信息阻碍了人们在许多领域做出明智决定的能力，例如政治、医疗保健、采购或投资。错误信息可能是偶然的，也可能是故意的。误导性图表是一种特别危险的错误信息，因为它们可以使错误信息更可信，更快地到达观众手中。为了打击图中的错误信息，需要考虑图理解的两个方面：概念推理和感知。先前的研究主要集中在图的概念推理上。然而，由于感知是自动的，它特别容易在误导性的图表中产生错误信息。这个项目的重点是感知。研究人员将开发一种感知训练方法，帮助观众从误导性的图表中提取正确的信息。感知训练方法将作为web浏览器插件提供。当观众在网上看到误导性的图表时，它将提供反馈。研究人员将使用机器学习算法来设计感知训练方法。该项目将促进对图形理解中感知的科学理解。它还将开发用于教育目的的机器学习算法。该项目将为解决错误信息问题提供新的工具。错误信息对社会构成严重威胁。误导图形是一种视觉上的错误信息，可以迅速向观众传达错误信息。虽然现有的视觉错误信息干预针对的是概念过程，但感知过程也起着重要作用。感知过程是自动的，容易产生偏见。视觉上的错误信息往往针对知觉而不是概念处理。因此，这个项目直接针对感知过程。研究人员将开发一种感知训练方法，教观众从误导的图表中提取正确的信息，这样他们就能对视觉上的错误信息“免疫”。感知训练方法将作为web浏览器插件提供，并将有两个组件。首先，在安装浏览器插件后，观众将接受2分钟的集中培训，这将作为防止误导性图表的初始“疫苗”。其次，当浏览者在网络上遇到误导性的图表时，浏览器插件将通过反馈来提供间隔训练，这可以作为他们免疫力的“助推器”。研究人员将使用机器学习算法来决定感知训练应该提供哪种类型的反馈，以及提供这种反馈的频率。当参与者浏览网页时，两个随机实验将评估感知训练方法的组成部分。该项目将促进对感知学习的科学理解，机器学习算法的教育应用，并将开发打击错误信息的新方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Misinformation impedes people’s ability to make informed decisions in many areas, for example politics, health care, purchasing, or investing. Misinformation can be created by accident or intentionally. Misleading graphs are a particularly dangerous form of misinformation because they can make false information more believable and reach viewers faster. To combat misinformation in graphs, one needs to consider two aspects of graph comprehension: conceptual reasoning and perception. Prior research has focused on conceptual reasoning about graphs. Yet, because perception is automatic it is especially prone to false information in misleading graphs. This project focuses on perception. The investigators will develop a perceptual training method that helps viewers to extract correct information from misleading graphs. The perceptual training method will be provided as a web browser plugin. It will provide feedback as viewers see misleading graphs on the web. The investigators will use machine learning algorithms to design the perceptual training method. The project will advance scientific understanding of perception in graph comprehension. It will also develop machine learning algorithms for educational purposes. The project will provide new tools for addressing issues of misinformation. Misinformation poses a severe risk to society. Misleading graphs are a type of visual misinformation that can quickly convey false information to viewers. While existing interventions for visual misinformation target conceptual processes, perceptual processes also play an important role. Perceptual processes are automatic and prone to biases. Visual misinformation often targets perceptual over conceptual processing. Therefore, this project directly targets perceptual processes. Investigators will develop a perceptual training method that will teach viewers to extract correct information from misleading graphs so that they become “immune” against visual misinformation. The perceptual training method will be delivered as a web browser plugin and will have two components. First, upon installing the browser plugin, viewers will receive a 2-minute massed training that will serve as the initial “vaccine” against misleading graphs. Second, the browser plugin will deliver a spaced training by giving feedback when viewers encounter misleading graphs on the web, which serves as a “booster” for their immunity. The investigators will use machine learning algorithms to decide which type of feedback the perceptual training should offer and how often such feedback should be provided. Two randomized experiments will evaluate components of the perceptual training method while participants browse the web. This project will advance scientific understanding of perceptual learning, educational applications of machine learning algorithms, and will develop novel approaches to combat misinformation.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.