CUED Publications database

Efficient state-space modularization for planning: Theory, behavioral and neural signatures

McNamee, D and Wolpert, D and Lengyel, M (2016) Efficient state-space modularization for planning: Theory, behavioral and neural signatures. In: UNSPECIFIED pp. 4518-4526..

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© 2016 NIPS Foundation - All Rights Reserved. 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 systems1,2to 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:26
Last Modified: 21 Jun 2018 02:21