LAUSR

Significance Preferences for foods high in sugar and fat are near universal and major contributors to obesity. Additionally, human food choices are sophisticated and individualistic: we choose by evaluating a food’s nutrients and sensory features and trading them against quantity and cost. To understand the mechanisms behind human-like food choices, we developed an experimental paradigm in which monkeys chose nutrient rewards offered in varying quantities. Resembling human suboptimal eating, the monkeys’ fat and sugar preferences shifted their nutrient balance away from dietary reference points. Formally defined economic values for specific nutrients and food textures explained the monkeys’ preferences and individual differences. Our findings show how human-like preferences derive from biologically critical food components and open up investigations of underlying neural mechanisms. Value is a foundational concept in reinforcement learning and economic choice theory. In these frameworks, individuals choose by assigning values to objects and learn by updating values with experience. These theories have been instrumental for revealing influences of probability, risk, and delay on choices. However, they do not explain how values are shaped by intrinsic properties of the choice objects themselves. Here, we investigated how economic value derives from the biologically critical components of foods: their nutrients and sensory qualities. When monkeys chose nutrient-defined liquids, they consistently preferred fat and sugar to low-nutrient alternatives. Rather than maximizing energy indiscriminately, they seemed to assign subjective values to specific nutrients, flexibly trading them against offered reward amounts. Nutrient–value functions accurately modeled these preferences, predicted choices across contexts, and accounted for individual differences. The monkeys’ preferences shifted their daily nutrient balance away from dietary reference points, contrary to ecological foraging models but resembling human suboptimal eating in free-choice situations. To identify the sensory basis of nutrient values, we developed engineering tools that measured food textures on biological surfaces, mimicking oral conditions. Subjective valuations of two key texture parameters—viscosity and sliding friction—explained the monkeys’ fat preferences, suggesting a texture-sensing mechanism for nutrient values. Extended reinforcement learning and choice models identified candidate neuronal mechanisms for nutrient-sensitive decision-making. These findings indicate that nutrients and food textures constitute critical reward components that shape economic values. Our nutrient-choice paradigm represents a promising tool for studying food–reward mechanisms in primates to better understand human-like eating behavior and obesity.