Stochastic models of evidence accumulation in changing environments

TL;DR

This paper develops a stochastic model for evidence accumulation in changing environments, revealing how decision-making processes adapt to environmental volatility and proposing neural mechanisms for optimal evidence integration.

Contribution

It introduces a new model of evidence accumulation that accounts for environmental changes and suggests neural coupling mechanisms for optimal decision-making.

Findings

Evidence discounting rate depends on environmental volatility

Accumulation dynamics are governed by information over average epochs

Neural populations are coupled through excitation in the model

Abstract

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.