The asymmetric learning rates of murine exploratory behavior in sparse reward environments.

Goal-oriented behaviors of animals can be modeled by reinforcement learning algorithms. Such algorithms predict future outcomes of selected actions utilizing action values and updating those values in response to the positive and negative outcomes. In many models of animal behavior, the action values are updated symmetrically based on a common learning rate, that is, in the same way for both positive and negative outcomes.