Project Profile

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.