Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that male and female mice achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes. Furthermore, neural and behavioral reward sensitivity were positively correlated across sessions. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain. Decision making relies on memory to store information about which actions to produce in which situations. This memory has limited capacity, which means that some information will be lost. The signatures of this information loss can be found in patterns of behavioral bias and randomness. However, relatively little is known about the neural mechanisms which ensure that actions achieve the highest possible reward given the limited capacity of decision memory. In this paper, we show that the neuromodulator dopamine is sensitive to the costs of memory, as predicted by a computational model of capacity-limited learning.