Reviews: Efficient inference for time-varying behavior during learning

The paper presents a new estimator for a dynamic logistic regression model used to characterize human or animal behavior in the context of binary decisions tasks. Despite the very large number of parameters such a model can be estimated robustly and with tolerable computational cost by exploiting the structure of the posterior covariance. The estimator is then applied to a delayed auditory discrimination task in humans and animals. The results show signatures of learning and history interference in the rodent compared to human. Additionally the model fit manages to predict behavior in test data.