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Background: The neurobiology of bulimia nervosa (BN) is poorly understood. Recent animal literature suggests that binge eating is associated with altered brain dopamine (DA) reward function. In this study, we wanted to investigate DA-related brain reward learning in BN., Methods: Ill BN (n = 20, age: mean = 25.2, SD = 5.3 years) and healthy control women (CW) (n = 23, age: mean = 27.2, SD = 6.4 years) underwent functional magnetic resonance brain imaging together with application of a DA-related reward learning paradigm, the temporal difference (TD) model. That task involves association learning between conditioned visual and unconditioned taste stimuli, as well as unexpected violation of those learned associations. Study participants also completed the Sensitivity to Reward and Punishment Questionnaire., Results: Bulimia nervosa individuals showed reduced brain response compared with CW for unexpected receipt and omission of taste stimuli, as well as reduced brain regression response to the TD computer model generated reward values, in insula, ventral putamen, amygdala, and orbitofrontal cortex. Those results were qualitatively similar in BN individuals who were nondepressed and unmedicated. Binge/purge frequency in BN inversely predicted reduced TD model response. Bulimia nervosa individuals showed significantly higher Sensitivity to Reward and Punishment compared with CW., Conclusions: This is the first study that relates reduced brain DA responses in BN to the altered learning of associations between arbitrary visual stimuli and taste rewards. This attenuated response is related to frequency of binge/purge episodes in BN. The brain DA neurotransmitter system could be an important treatment target for BN., (Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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.

A feature of human cognition is the ability to monitor and adjust one's own behavior under changing circumstances. A dynamic balance between controlled and rapid responding is needed to adapt to a fluctuating environment. We suggest that cognitive control may include, among other things, two distinct processes. Incongruent stimuli may drive top-down facilitation of task-relevant responses to bias performance toward exploitation vs. exploration. Task or response switches may generally slow responses to bias toward accuracy vs. speed and exploration vs. exploitation. Behavioral results from a task switching study demonstrate these two distinct processes as revealed by higher-order sequential effects. A computational model implements the two conflict-control mechanisms, which allow it to capture many complex and novel sequential effects. Lesion studies with the model demonstrate that the model is unable to capture these effects without the conflict-control loops and show how each monitoring component modulates cognitive control. The results suggest numerous testable predictions regarding the neural substrates of cognitive control.