UPDATED | Behavioral Social Neuroscience Seminar

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 both the way the human brain actually works and the optimal design for an artificial intelligence that operates under constraints on 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 behavior. 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 first 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 non-invasive brain stimulation techniques and large-scale simulation of nonlinear state-space models aimed at developing a deeper appreciation of the lPFC's role in cognitive control of learning. 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 neuromorphic algorithms for restoring stability to prefrontal systems, as well as commercial expert systems that make precise predictions about humans' suboptimal behavior.