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 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.

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. 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.

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 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.