Computational Methods for Predicting and Understanding Food Judgment.
Psychol Sci
; 33(4): 579-594, 2022 04.
Article
em En
| MEDLINE
| ID: mdl-35298316
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Rotulagem de Alimentos
/
Julgamento
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article