Rats optimally accumulate and discount evidence in a dynamic environment

TL;DR

This study demonstrates that rats can adaptively and nearly optimally discount evidence over time in a dynamic environment, with their accumulation timescale influenced by environmental statistics and sensory noise.

Contribution

The paper introduces a behavioral paradigm showing rats adapt their evidence accumulation timescale in a dynamic setting, aligning closely with optimal inference predictions.

Findings

Rats nearly optimally discount evidence based on environment and sensory noise.

Changing environment dynamics shifts rats' evidence accumulation timescale.

Theoretical predictions match observed timing of changes of mind.

Abstract

How choices are made within noisy environments is a central question in the neuroscience of decision making. Previous work has characterized temporal accumulation of evidence for decision-making in static environments. However, real-world decision-making involves environments with statistics that change over time. This requires discounting old evidence that may no longer inform the current state of the world. Here we designed a rat behavioral task with a dynamic environment, to probe whether rodents can optimally discount evidence by adapting the timescale over which they accumulate it. Extending existing results about optimal inference in a dynamic environment, we show that the optimal timescale for evidence discounting depends on both the stimulus statistics and noise in sensory processing. We found that when both of these components were taken into account, rats accumulated and temporally discounted evidence almost optimally. Furthermore, we found that by changing the dynamics of the environment, experimenters could control the rats' accumulation timescale, switching them from accumulating over short timescales to accumulating over long timescales and back. The theoretical framework also makes quantitative predictions regarding the timing of changes of mind in the dynamic environment. This study establishes a quantitative behavioral framework to control and investigate neural mechanisms underlying the adaptive nature of evidence accumulation timescales and changes of mind.