Computational Methods for Predicting and Understanding Food Judgment

Abstract

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 (r2 = .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.