Nutrient-sensitive reinforcement learning in monkeys

Abstract

Animals make adaptive food choices to acquire nutrients that are essential for survival. In reinforcement learning (RL), animals choose by assigning values to options and update these values with new experiences. This framework has been instrumental for identifying fundamental learning and decision variables, and their neural substrates. However, canonical RL models do not explain how learning depends on biologically critical intrinsic reward components, such as nutrients, and related homeostatic regulation. Here, we investigated this question in monkeys making choices for nutrient-defined food rewards under varying reward probabilities. We found that the nutrient composition of rewards strongly influenced monkeys' choices and learning. The animals preferred rewards high in nutrient content and showed individual preferences for specific nutrients (sugar, fat). These nutrient preferences affected how the animals adapted to changing reward probabilities: the monkeys learned faster from preferred nutrient rewards and chose them frequently even when they were associated with lower reward probability. Although more recently experienced rewards generally had a stronger influence on monkeys' choices, the impact of reward history depended on the rewards' specific nutrient composition. A nutrient-sensitive RL model captured these processes. It updated the value of individual sugar and fat components of expected rewards from experience and integrated them into scalar values that explained the monkeys' choices. Our findings indicate that nutrients constitute important reward components that influence subjective valuation, learning and choice. Incorporating nutrient-value functions into RL models may enhance their biological validity and help reveal unrecognized nutrient-specific learning and decision computations.