Optimizing sequential decisions in the drift-diffusion model

TL;DR

This paper models how an ideal observer accumulates evidence across correlated trials, revealing strategies that optimize decision speed and accuracy in environments with temporal correlations.

Contribution

It introduces a probabilistic inference framework for sequential decision-making in correlated environments, extending traditional models to account for trial-to-trial dependencies.

Findings

Incorporating previous trial information biases subsequent decisions.

Optimal decision times vary within a sequence, favoring longer deliberation early on.

The model aligns with observed experimental patterns in decision times and choices.

Abstract

To make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.