Homeostatically regulated reinforcement learning models can explain animal foraging behavior.

Studies using computational models that include homeostasis explain why animals change their choices based on internal needs. For example, a thirsty animal will prioritize water-related rewards over food. These models better predict real animal behavior than those ignoring internal states, showing how the brain integrates body signals into learning processes.