Contribution of amygdala to dynamic model arbitration under uncertainty.

Intrinsic uncertainty in the reward environment requires the brain to run multiple models simultaneously to predict outcomes based on preceding cues or actions, commonly referred to as stimulus- and action-based learning. Ultimately, the brain also must adopt appropriate choice behavior using reliability of these models. Here, we combined multiple experimental and computational approaches to quantify concurrent learning in monkeys performing tasks with different levels of uncertainty about the model of the environment. By comparing behavior in control monkeys and monkeys with bilateral lesions to the amygdala or ventral striatum, we found evidence for dynamic, competitive interaction between stimulus-based and action-based learning, and for a distinct role of the amygdala. Specifically, we demonstrate that the amygdala adjusts the initial balance between the two learning systems, thereby altering the interaction between arbitration and learning that shapes the time course of both learning and choice behaviors. This novel role of the amygdala can account for existing contradictory observations and provides testable predictions for future studies into circuit-level mechanisms of flexible learning and choice under uncertainty.