4.5 Article

Developing PFC representations using reinforcement learning

Journal

COGNITION
Volume 113, Issue 3, Pages 281-292

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.cognition.2009.05.015

Keywords

PFC; Representation; Reinforcement learning; Functional organization

Funding

  1. NIMH NIH HHS [F32 MH075300, R01 MH069597-01, P50 MH079485-020002, P50 MH079485, R01 MH069597-05, R01 MH069597, F32 MH075300-01A2, P50 MH079485-029003, 1 F32 MH075300-01A2] Funding Source: Medline

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From both functional and biological considerations, it is widely believed that action production, planning, and goal-oriented behaviors supported by the frontal cortex are organized hierarchically [Fuster (1991); Koechlin, E., Ody, C., & Kouneiher, F. (2003). Neuroscience: The architecture of cognitive control in the human prefrontal cortex. Science, 424, 1181-1184; Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure Of behavior. New York: Holt]. However, the nature of the different levels of the hierarchy remains unclear, and little attention has been paid to the origins of such a hierarchy. We address these issues through biologically-inspired computational models that develop representations through reinforcement learning. We explore several different factors in these models that might plausibly give rise to a hierarchical organization of representations within the PFC, including an initial connectivity hierarchy within PFC, a hierarchical set of connections between PFC and subcortical structures controlling it, and differential synaptic plasticity schedules. Simulation results indicate that architectural constraints contribute to the segregation of different types of representations, and that this segregation facilitates learning. These findings are consistent with the idea that there is a functional hierarchy in PFC, as captured in our earlier computational models of PFC function and a growing body of empirical data. (C) 2009 Elsevier B.V. All rights reserved.

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