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Abstract How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID‐19 cases? The current research tests an understudied cognitive mechanism in which people may agree about whatactuallyhappened (e.g., that a public health policy was implemented and COVID‐19 cases declined), but can be made to disagree about the counterfactual, or whatwould havehappened otherwise (e.g., whether COVID‐19 cases would have declined naturally without intervention) via comparison cases. Across two preregistered studies (totalN= 480), participants reasoned about the implementation of a public policy that was followed by an immediate decline in novel virus cases. Study 1 shows that people's judgments about the causal impact of the policy could be pushed in opposite directions by emphasizing comparison cases that imply different counterfactual outcomes. Study 2 finds that people recognize they can use such information to influence others. Specifically, in service of persuading others to support or reject a public health policy, people systematically showed comparison cases implying the counterfactual outcome that aligned with their position. These findings were robust across samples of U.S. college students and politically and socioeconomically diverse U.S. adults. Together, these studies suggest that implied counterfactuals are a powerful tool that individuals can use to manufacture others’ causal judgments and warrant further investigation as a mechanism contributing to belief polarization.
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Computational Methods for Predicting and Understanding Food Judgment
People make subjective judgments about the healthiness of different foods every day, and these judgments in turn influence their food choices and health outcomes. Despite the importance of such judgments, there are few quantitative theories about their psychological underpinnings. This article introduces a novel computational approach that can approximate people’s knowledge representations for thousands of common foods. We used these representations to predict how both lay decision-makers (the general population) and experts judge the healthiness of individual foods. We also applied our method to predict the impact of behavioral interventions, such as the provision of front-of-pack nutrient and calorie information. Across multiple studies with data from 846 adults, our models achieved very high accuracy rates ( r 2 = .65–.77) and significantly outperformed competing models based on factual nutritional content. These results illustrate how new computational methods applied to established psychological theory can be used to better predict, understand, and influence health behavior.
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- Award ID(s):
- 1847794
- PAR ID:
- 10394340
- Date Published:
- Journal Name:
- Psychological Science
- Volume:
- 33
- Issue:
- 4
- ISSN:
- 0956-7976
- Page Range / eLocation ID:
- 579 to 594
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/09567976211043426
