This seminar will review some recent work which uses stochastic models to investigate decision-making in humans and animals.  In particular, I will present models of learning and decision-making in situations where (approximate) Bayesian methods appear to be essential: attempting reinforcement learning when state uncertainty is present, and attempting to balance exploration and exploitation using uncertainty in action values.  Consequences and properties of such decision-making processes are investigated using simple stochastic process techniques.