Toward a Unified Model of Cognitive Control

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DESCRIPTION (provided by applicant): The overarching goal of the proposed research is to understand how the brain performs cognitive control. By cognitive control, we mean the ability of the cognitive system to flexibly control its own behavior in response to task demands or other contingencies, favoring the processing of task-relevant information over other sources of competing information, and mediating task-relevant behavior over habitual or otherwise prepotent responses. There is virtually universal agreement that the prefrontal cortex (PFC) plays a critical role in cognitive control. However, exactly what it does and how it does it, in terms of concrete neural mechanisms, remain considerably more controversial questions. Explicit computational models that incorporate biological mechanisms can provide a powerful means of testing theoretical ideas about the biological basis of cognitive control. However, existing models have each only simulated one or a few phenomena, leaving open questions about their general applicability across the entire domain of cognitive control. We propose to address this limitation by applying a single computational model that includes critical features of the underlying neurobiology (including the basal ganglia (BG) and its interactions with the PFC) to a wide range of benchmark behavioral and neural phenomena. This model includes powerful learning mechanisms that should produce intelligent, controlled behavior without relying on the kind of homunculus that often lies behind theories of cognitive control. Specific Aim 1: Modeling Behavioral and Neural Data. We test the sufficiency of our framework by evaluating whether a single instantiation of the model, trained on a single large corpus of tasks, can simulate a wide range of benchmark data in cognitive control, and we use this model to make a number of testable predictions. Specific Aim 2: Nature and Learning of PFC/BG Representations. We address the fundamental question: how can people quickly adapt to performing novel cognitive tasks, when it takes monkeys months of highly-focused training to learn a single new task? We hypothesize that people develop an extensive repertoire of basic cognitive operations throughout the long developmental period into adulthood, and can rapidly and flexibly combine them to solve novel tasks. Demonstrating this principle in an explicit computational model will have important implications for understanding human intelligence, education, and development.