Reviews: Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation

This paper builds on a successful line of research from Yu and colleagues on change point detection. The paper presents some interesting theoretical results linking the Bayes-optimal solution to computationally efficient and neurally plausible approximations. The paper also presents a cursory analysis of empirical data using the approximations. The paper is well-written and technically rigorous. There were a number of important useful insights from the theoretical analysis.