Functional neural interactions during adaptive reward learning: An functional magnetic resonance imaging study

A key feature of learning in humans is flexibility in adjusting the weight of new information to update predictions. This flexibility can be computationally captured by changing the learning rate in a reinforcement-learning model. Key components of reinforcement learning-such as prediction error (delta), learning rate (alpha), and reward feedback (r)-have been mapped to various brain areas. However, questions regarding the functional integration patterns in the human brain under the modulation of learning factors, and their interactions, remain unanswered. To investigate these phenomena, we first applied a reinforcement-learning model with an adaptive learning rate and functional magnetic resonance imaging to simulate the individual's reward-learning behavior. Psychophysiological interaction (PPI) analysis was then used to examine the functional interactions of the whole brain under the experimental condition of reward (r), the integration between reward and learning rate (alpha x r), and the integration between the prediction error and learning rate (alpha x delta) in a reward-learning task. The behavior statistical analyses indicated that the model estimates of alpha and delta captured the participants' learning behavioral patterns of getting high reward, by changing alpha and delta for different difficulties and after getting different reward feedback. The PPI analysis results showed that motor-related regions (including the supplement motor area, precentral gyrus, and thalamus) contributed to cognitive control processing regions (including the middle temporal gyrus, anterior and middle cingulate gyrus, and inferior frontal gyrus) by alpha x r. Finally, alpha x delta modulated the interaction between subregions of the striatum.