A hierarchical-drift diffusion model of the roles of hunger, caloric density and valence in food selection. (1st July 2019)

• Record Type: Journal Article
• Authors: Garlasco, P.; Osimo, S.A.; Rumiati, R.I.; Parma, V.
• Is Part Of: Appetite. Volume 138(2019)
• Page Start: 52
• Page End: 59
• Publication Date: 2019-07-01
• Subjects: Decision-making -- Food choice -- Drift-diffusion model -- Bayesian inference -- Hunger; Food habits -- Periodicals; Appetite -- Periodicals; Appetite disorders -- Periodicals; Electronic journals; 306.4613
• DOI: 10.1016/j.appet.2019.03.009
• Languages: English
• ISSNs: 0195-6663

Abstract

Abstract: Decisions based on affectively relevant stimuli, such as food items, hardly follow strictly rational rules. Being hungry, the food's caloric density, and the subjective valence attributed to various foods are known factors that modulate food choices. Yet, how these factors relatively and altogether contribute to the food choice process is still unknown. In this study, we showed 16 healthy young adults low- and high-calorie food when hungry or fed, and we asked them to evaluate the valence of each visually-presented food. To compute the relative influence of hunger, caloric density and valence on food choice, we applied a hierarchical drift diffusion model (HDDM). Results indicated that hunger, caloric density and valence affected how fast participants accumulated information in favor of the chosen item over the other. When fed, participants were faster in choosing low-calorie foods and foods with a higher valence. Conversely, when hungry, participants were faster in choosing high-calorie foods, including food items with lower subjective valence. All in all, these findings confirm the complex nature of food choices and the usefulness of nuanced computational models to address the multifaceted nature of decision-making and value assessment processes affecting food selection.