Developing PFC representations using reinforcement learning.
Developing PFC representations using reinforcement learning.
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DOI：
10.1016/j.cognition.2009.05.015
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发表时间：
2009-12
期刊：
影响因子：
3.4
通讯作者：
O'Reilly RC
中科院分区：
文献类型：
--
作者：
Reynolds JR;O'Reilly RC
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, 1990,, &) 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.
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