Hosted by the Center for Theoretical and Computational Neuroscience (CTCN) and the Cognitive, Computational and Systems Neuroscience Pathway (CCSN)

Abstract: There is now overwhelming evidence that rats and other mammals make their decisions at the start of task trials and spend relatively little time thereafter. This is consistent with the Rubicon theory (Heckhausen and Gollwitzer, 1987) that postulates two qualitatively distinct phases of mental life: goal selection vs. goal engaged. Among the many other implications of this theory, we procrastinate because once goal engaged, we will be committed to finishing or else suffer disappointment. The goal-selection process is therefore especially conservative, and it is thus relatively difficult to actually cross the Rubicon into the goal-engaged state. I have been developing a large-scale, systems-neuroscience computational model of the mechanisms that drive this and related goal-driven dynamics in the brain, which can shape learning and online processing in powerful ways. It requires the coordinated activity of many brain systems and neuromodulatory pathways to establish this strong future-oriented bias in learning and processing, and it is thus unlikely that these dynamics could simply emerge from generic error-driven learning mechanisms. Indeed, current deep neural networks powered by such mechanisms notably lack evidence of goal-driven, self-motivated behavior. Thus, it may be necessary to reverse engineer the millions of years of evolution that shaped the mammalian brain to understand how goal-driven learning works, and how it all too often breaks down in a wide range of mental disorders that plague humanity.

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