MICHAEL MACK, Psychology Department, University of Toronto
Title: The computations, representations, and neural machinery of category learning
Ebbinghaus Empire Series 2016-2017
Abstract: The learning brain faces a complex challenge: new information must be first evaluated and then integrated into flexible knowledge structures, which will later support novel decisions. Characterizing the dynamic coordination of learning's component processes is the key to understanding how we best learn and requires an approach grounded in psychological theory and neural mechanism. In this talk, I will present a set of studies that employ computational cognitive neuroscience methods to understand the learning brain. First, I will focus on the role of selective attention in learning by showing how our ability to focus on goal-relevant information dynamically shapes neural representations of category knowledge. Second, I will take a methodological detour to explore how a purely data-driven, atheoretical approach to fMRI analysis can uncover meaningful codes in neural representations (don't worry, I'll bring a theoretically-guided learning model back into the story to validate the findings). Third, I will provide a brief glimpse of an in-progress study that aims to characterize the fundamental computations underlying knowledge formation during new learning. Collectively, these findings support a neurocomputational framework for category learning that highlights the mutual interaction of selective attention and knowledge formation. I will conclude with a brief discussion of ongoing and planned research that leverages this framework to best characterize the learning brain.
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