# Optimal models of decision-making in dynamic environments

## TL;DR

This paper reviews recent theoretical models of optimal decision-making in dynamic environments, highlighting how animals and humans adapt their strategies to environmental changes and achieve near-optimal performance in 2AFC tasks.

## Contribution

It provides a comprehensive review of computational models for decision-making in changing environments and compares these models with experimental behavioral data.

## Key findings

- Animals and humans can perform near-optimally in dynamic 2AFC tasks.
- Models effectively capture how decision strategies adapt to environmental changes.
- Performance analysis helps understand the neural basis of adaptive decision-making.

## Abstract

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.