Rethinking Memory Systems for Statistical Learning

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Turk-Browne, Nicholas
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Dogma states that memory can be divided into distinct types, based on whether conscious or not, one-shot or incremental, autobiographical or factual, sensory or motor, etc. These distinctions have been supported by dissociations in brain localization, task performance, developmental trajectories, and pharmacological interventions, among other techniques. A natural consequence is the assumption of a one-to-one mapping between brain systems and memory behaviors. Aside from theoretical concerns and dissociation logic, there have also now been several empirical demonstrations of where these boundaries break down, from contributions of the hippocampus to reward learning and motor behavior to rapid episodic-like learning in frontal cortex. These considerations suggest that behavior is overdetermined by multiple brain systems and that the dependence on any particular brain system reflects the specific computations required for that behavior. As a case study, I will describe a series of neuroimaging, neuropsychological, and computational studies implicating the hippocampal system in statistical learning, a function more traditionally ascribed to cortical systems. I will end by considering some open questions that arise from this perspective, including about how memory systems support predictive coding and change over development.
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73:03 minutes
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