First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Optimal decision-making with time-varying evidence reliability proposes a model of evidence accumulation (in humans and other animals) in a new, more flexible setting, whereby the evidence reliability can change on a short time scale, i.e. within a single trial. The authors propose a simple, but flexible model of evidence dynamics, derive the corresponding optimal policy and thoroughly discuss how it changes with each of the model's parameters. The meaning and role of every parameter is well explained and the authors provide additional intuitions for their results. The Supplementary Material is clear and compact and seems to provide all the necessary details of the algorithms used.

Previous theoretical and experimental work on optimal decision-making was restricted to the artificial setting of a reliability of the momentary sensory evidence that remained constant within single trials. The work presented here describes the computation and characterization of optimal decision-making in the more realistic case of an evidence reliability that varies across time even within a trial. It shows that, in this case, the optimal behavior is determined by a bound in the decision maker's belief that depends only on the current, but not the past, reliability. We furthermore demonstrate that simpler heuristics fail to match the optimal performance for certain characteristics of the process that determines the time-course of this reliability, causing a drop in reward rate by more than 50%.

Previous theoretical and experimental work on optimal decision-making was restricted to the artificial setting of a reliability of the momentary sensory evidence that remained constant within single trials. The work presented here describes the computation and characterization of optimal decision-making in the more realistic case of an evidence reliability that varies across time even within a trial. It shows that, in this case, the optimal behavior is determined by a bound in the decision maker's belief that depends only on the current, but not the past, reliability. We furthermore demonstrate that simpler heuristics fail to match the optimal performance for certain characteristics of the process that determines the time-course of this reliability, causing a drop in reward rate by more than 50%.