Efficient state-space modularization for planning: theory, behavioral and neural signatures
Even in state-spaces of modest size, planning is plagued by the "curse of dimensionality". This problem is particularly acute in human and animal cognition given the limited capacity of working memory, and the time pressures under which planning often occurs in the natural environment. Hierarchically organized modular representations have long been suggested to underlie the capacity of biological systems 1,2 to efficiently and flexibly plan in complex environments. However, the principles underlying efficient modularization remain obscure, making it difficult to identify its behavioral and neural signatures. Here, we develop a normative theory of efficient state-space representations which partitions an environment into distinct modules by minimizing the average (information theoretic) description length of planning within the environment, thereby optimally trading off the complexity of planning across and within modules. We show that such optimal representations provide a unifying account for a diverse range of hitherto unrelated phenomena at multiple levels of behavior and neural representation.
This paper provides a new algorithm for sub-dividing a state space for planning and search. The algorithm is shown to provide qualitative fits to features of human navigation data and neural data from rodents in a simple learning task. 
The paper is very ambitious and develops a computational model of how the state space can be carved up (aggregated?) for planning. This model is applied to some intriguing data on human and rodent spatial navigation and seems to nicely pull together disparate threads from the literature. Unfortunately, the exposition was sufficiently abstract, so that following the thread from the model to the results (simulations) was challenging, leaving unclear exactly how the model was explaining the behaviour and making evaluation difficult. I unpack these and a couple of other issues below.  1. It is not entirely clear how the different ideas introduced in the paper (modularity, centrality, description length) fit together into a single model of behaviour and the brain. From the text, it was not clear to me how the simulations and predictions for the different behavioural tasks were generated. I think the account in the text is just too abstract with insufficient detail of how to get from those three equations to a full model of the behaviour (which they obviously have).  2. Almost no detail is given on the brain simulations (Fig 3C). The results look great, but I have no idea how they were generated from the model as spelled out in the early sections.  These first two issues may be addressed in the supplemental materials (which I did not read), but my understanding is that the core paper needs to stand alone, with the supplementals supplementing the main text, not replacing key aspects of the results.  3. It was not clear to me how their concept of modularization was different than simple state aggregation. That is, the novelty and distinctiveness of the modularization approach was not apparent, and this parallel is even briefly acknowledged in footnote 1, yet the approach is still deemed novel. The paper could still be interesting even if the modularization approach just builds on or even just implements state aggregation with interesting connections to human and animal behaviour, but that was not done here.  4. The optimal modularization presented in Figure 4D seems like it might depend strongly on the “states” that are included in the model. Why are explore, freeze, rest, and groom only behaviours permitted near the start sequence? Unlike some of the experiments with humans on navigation, the behavioral repertoire of the animals is not strongly limited by their location. Animals can engage in these behaviours at any time (though they might not in practice). This needs further justification. Also, the states there are almost all actions, not really states.