  How choices are made within noisy environments is a central question in the
neuroscience of decision making. Previous work has characterized temporal
accumulation of evidence for decision-making in static environments. However,
real-world decision-making involves environments with statistics that change
over time. This requires discounting old evidence that may no longer inform the
current state of the world. Here we designed a rat behavioral task with a
dynamic environment, to probe whether rodents can optimally discount evidence
by adapting the timescale over which they accumulate it. Extending existing
results about optimal inference in a dynamic environment, we show that the
optimal timescale for evidence discounting depends on both the stimulus
statistics and noise in sensory processing. We found that when both of these
components were taken into account, rats accumulated and temporally discounted
evidence almost optimally. Furthermore, we found that by changing the dynamics
of the environment, experimenters could control the rats' accumulation
timescale, switching them from accumulating over short timescales to
accumulating over long timescales and back. The theoretical framework also
makes quantitative predictions regarding the timing of changes of mind in the
dynamic environment. This study establishes a quantitative behavioral framework
to control and investigate neural mechanisms underlying the adaptive nature of
evidence accumulation timescales and changes of mind.
