The overarching goal of the proposed research is to understand the specific roles of different brain areas inthe learning of executive function, by empirically testing and fur ther developing a biologically-based computationalmodel of the prefrontal cor tex and associated subcor tical systems (including the basal ganglia andmidbrain dopaminergic nuclei). We focus on two specific issues: (a) the learning of abstract, rule-like representationsin prefrontal cor tical areas, which suppor t flexible behavior by enabling better generalization tonovel circumstances; and (b) the mechanisms of feedback-driven learning, which shape the adaptive modulationof prefrontal executive function representations by the basal ganglia, according to our model. Severalpredictions from this computational modeling framework have already been successfully tested in diversepopulations, including Parkinson's patients and people with ADHD, both of which are thought to involve disordersof the dopaminergic system as it affects the basal ganglia and prefrontal cor tex. Thus, this model hasimpor tant implications for understanding the neural basis of executive function, both in neurologically intactand disordered populations.We propose to test the following hypotheses:
Specific Aim 2. 1: Factors Affecting Learning of Representations in Prefrontal Cortex: First, wetest in both young adults and children a set of predictions from our computational model. For example,blocked training should facilitate the development of abstract, rule-like representations, which inturn suppor t better generalization to novel task contexts. Second, because the degree of abstractionlearning depends on the duration of active maintenance in our model, different regions of PFC may beorganized according to relative degree of abstraction, and corresponding maintenance duration. Weexplore this idea in the model.
Specific Aim 2. 2: Factors Affecting Feedback Learning. We provide a more direct test of dopaminergicmediation of event-related potential signals responsive to feedback information (ERN) by administeringdopamine D2 receptor agonists/antagonists, which should have dissociable effects on thesesignals according to our model. We also test whether mood induction can shift the balance offeedback responsiveness, as measured by ERP's. Finally, we attempt to disentangle multiple factorsinfluencing learning of executive function tasks by using coordinated executive function and negativefeedback learning studies in children.
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