Evidence accumulation and change rate inference in dynamic environments

TL;DR

This paper presents an ideal observer model that infers both the current state and change rate of a dynamic environment, simplifying complex computations with a neural plausibility approach.

Contribution

It introduces a novel model for joint inference of environmental state and change rate, using a moment closure approximation for computational efficiency.

Findings

The model accurately infers environmental state and change rate.

The approximation simplifies computations without losing accuracy.

Neural network implementation via rate-correlation plasticity is demonstrated.

Abstract

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible changepoint counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments, and map this computation to reduced models which perform inference using plausible neural mechanisms.