  Organisms and ecological groups accumulate evidence to make decisions.
Classic experiments and theoretical studies have explored this process when the
correct choice is fixed during each trial. However, we live in a constantly
changing world. What effect does such impermanence have on classical results
about decision making? To address this question we use sequential analysis to
derive a tractable model of evidence accumulation when the correct option
changes in time. Our analysis shows that ideal observers discount prior
evidence at a rate determined by the volatility of the environment, and the
dynamics of evidence accumulation is governed by the information gained over an
average environmental epoch. A plausible neural implementation of an optimal
observer in a changing environment shows that, in contrast to previous models,
neural populations representing alternate choices are coupled through
excitation. Our work builds a bridge between statistical decision making in
volatile environments and stochastic nonlinear dynamics.
