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Metacognitive control of reinforcement learning and causal inference

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A central goal of both artificial intelligence and neuroscience is to understand human cognitive processes that are flexible enough to perform a wide range of tasks. In this regard, it is becoming widely recognized that having several different learning and decision systems may be the optimal design for the human brain that operates under constraints on performance, time, and energy. Over the past decade special attention has been given to advancing our understanding of how the brain employs multiple separable systems to learn about the world and how they ultimately come to drive coherent behaviour.

Based upon a combination of functional magnetic resonance imaging (fMRI) and computational modeling, I will discuss a theoretical account of how the human lateral prefrontal cortex (LPFC) influences the brain’s various learning systems, placing the LPFC as the brain’s “meta-controller”. I will present evidence suggesting that the inferior lateral PFC allocate control to learning systems associated with either goal-directed (model-based), or habitual (model-free) learning. I will also show evidence supporting the view that the ventrolateral PFC plays a crucial role in switching between incremental and one-shot causal learning. Finally, I will describe recent experiments using a non-invasive brain stimulation technique aimed at developing a deeper appreciation of the LPFC ’s role in metacognitive control. These findings may help to explain how and why control processes breakdown in various psychiatric disorders, including obsessive-compulsive disorders and addiction. In turn, a deeper insight into these mechanistic anomalies may permit further development of algorithms for restoring stability to prefrontal systems.

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