title: Developing PFC representations using reinforcement learning.
abstract: 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.
authors: Department of Psychology, University of Denver, 2155 S. Race St., Denver, CO 80208, United States. Electronic address: jeremy.reynolds@psy.du.edu.
authors: Jeremy R
authors: JR
authors: Reynolds
authors: Department of Psychology, University of Colorado, United States.
authors: Randall C
authors: RC
authors: O'Reilly
mesh_terms: D000704:Analysis of Variance; D003198:Computer Simulation; D006801:Humans; D008960:Models, Psychological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D017397:Prefrontal Cortex / Q000502:physiology*; D012054:Reinforcement, Psychology