  Nature is in constant flux, so animals must account for changes in their
environment when making decisions. How animals learn the timescale of such
changes and adapt their decision strategies accordingly is not well understood.
Recent psychophysical experiments have shown humans and other animals can
achieve near-optimal performance at two alternative forced choice (2AFC) tasks
in dynamically changing environments. Characterization of performance requires
the derivation and analysis of computational models of optimal decision-making
policies on such tasks. We review recent theoretical work in this area, and
discuss how models compare with subjects' behavior in tasks where the correct
choice or evidence quality changes in dynamic, but predictable, ways.
