Evidence accumulation in changing environments: The price of optimality

To make decisions in a constantly changing world, organisms must account for environmental volatility and discount old information when making decisions based on such accumulated evidence. We introduce Bayesian inference models of decision making, and derive an ideal observer model for inferring the present state of the environment along with the environment's rate of change. Such models can be derived when the evidence stream is persistent and noisy (e.g., random dot displays) or when evidence is pulsatile (e.g., clicks provided to the ears). Moment closure allows us to obtain a low-dimensional system that performs comparable inference. These computations can be implemented by a neural network model whose connections are updated according to an activity-dependent plasticity rule. We discuss the predictions of our model in light of recent experimental data exploring evidence accumulation strategies implemented by humans and rats performing decision-making tasks in changing environments. The model can be extended in a number of ways to incorporate multiple streams of evidence, such as change point signals.