eLife assessment
This important work by Veneditto and colleagues developed a new modeling approach, called a mixture-of-agent hidden Markov model (MoA-HMM), in which choice behaviors are modeled as transitions between discrete states defined by different weighting of several reinforcement learning and decision strategies. The authors apply this approach to their previous data collected from rats performing the two-step task, and show that this method provides better fits to the data than previous methods, and predicts fluctuations in neural and other behavioral data. The reviewers found this study to be overall convincing, and the method is of general interest to the field.