After rebuttal: I focused my comments and attention on the utility of this method on providing behavioral representations, whereas R3 and the authors drew my attention to the novelty of the separation loss, and their specific intent to primarily model _individual decision-making processes_, and not behavior more generally. The text could use clarification on this point in several places. I still do think that existing PGM-based approaches with subject-level random variables are a fair baseline to compare against, since they do create latent embeddings of behavior on a per-subject basis (with e.g. I find the originality low in the context of that prior work, including Emily Fox's work on speaker diarization and behavioral modeling, Matthew Johnson's recent work on structured latent space variational autoencoders, and others. Given the input of the model is a bag of low-dimensional sequences, probabilistic graphical models are an appropriate baseline here.