We learn from experience to make better decisions, often by adjusting our expectations to match past outcomes. In a dynamic world, this adjustment process must itself be adaptive, because changes can occur that render past outcomes irrelevant to future expectations. For example, historical yields from a fruit tree that has since died should no longer affect future expectations. A history of stable stock prices can become irrelevant after a major change in corporate leadership. I will talk about ongoing work in my lab that has begun to identify the neural mechanisms responsible for making effective decisions in these kinds of dynamic environments. This work is based on four complementary approaches: 1) the development of ideal-observer models that describe how to recognize and respond appropriately to environmental change-points under certain conditions, which we have systematically reduced to a simple analytic form that makes specific predictions about the underlying neural computations; 2) human psychophysics that allows us to quantify how well human performance matches predictions of our models; 3) measurements of pupil diameter that allow us to test specific hypotheses about the relationship between the computations described in our models and functions of the pupil-linked arousal system; and 4) measurements of neural activity in the brainstem noradrenergic nucleus locus coeruleus, which is associated with non-luminance-mediated changes in pupil diameter, and one of its primary cortical targets, the anterior cingulate cortex (ACC), of monkeys performing change-point tasks. This work represents a novel field of study of how individuals make effective decisions in a dynamic world.