Learning Strategies are Associated with Distinct Neural Signatures

PASADENA, Calif.—The process of learning requires the sophisticated ability to constantly update our expectations of future rewards so we may make accurate predictions about those rewards in the face of a changing environment. Although exactly how the brain orchestrates this process remains unclear, a new study by researchers at the California Institute of Technology (Caltech) suggests that a combination of two distinct learning strategies guides our behavior. The two learning systems and the neural correlate of their respective prediction error. The model-free system (green) learns the expected rewards of stimuli using a reward prediction error; the model-based system (red) learns a model of the environment using a state prediction error. The systems are combined using an exponential weighting function (blue line) to reach a decision about the available stimuli. One accepted learning strategy, called model-free learning, relies on trial-and-error comparisons between the reward we expect in a given situation and the reward we actually get. The result of this comparison is the generation of a "reward prediction error," which corresponds to that difference.