Abstract
Human decisions can be reflexive or planned, being governed respectively by model-free and model-based learning systems. These two systems might differ in their responsiveness to our needs. Hunger drives us to specifically seek food rewards, but here we ask whether it might have more general effects on these two decision systems. On one hand, the model-based system is often considered flexible and context-sensitive, and might therefore be modulated by metabolic needs. On the other hand, the model-free system’s primitive reinforcement mechanisms may have closer ties to biological drives. Here, we tested participants on a well-established two-stage sequential decision-making task that dissociates the contribution of model-based and model-free control. Hunger enhanced overall performance by increasing model-free control, without affecting model-based control. These results demonstrate a generalized effect of hunger on decision-making that enhances reliance on primitive reinforcement learning, which in some situations translates into adaptive benefits.
Highlights
Hunger is an adaptive motivational state that drives us to eat, restoring homeostatic balance (Saper et al, 2002).Rafal Bogacz and Sanjay G
A model-free reinforcement learning strategy predicts actions repeat when reinforced, i.e., a main effect of reward (Fig. 2A), whereas a model-based learning strategy predicts a crossover interaction between reward outcome on the second-stage and the type of transition (Fig. 2B)
The model-free system updates the value of the first-stage chosen stimulus, such that reward promotes staying with the current choice
Summary
The prevalence of obesity and eating disorders is steadily increasing. To counteract problems related to eating, people need to make rational decisions. Appetite may switch us to a different decision mode, making it harder to achieve long-term goals. We show that planned and reinforcement-driven actions are differentially sensitive to hunger. Hunger affected reinforcement-driven actions, and did not affect the planning of actions. Our data shows that people behave differently when they are hungry. We provide a computational model of how the behavioral changes might arise
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