Guest Lecture by Prof. Daniel M. Wolpert (Columbia University)
Abstract: Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. I will present a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. Although this model was developed for motor learning the principles underlying the model are domain general. Our results suggest that contextual inference is a key principle underlying how a diverse set of experiences is reflected in behavior.
Find out more about Daniel M. Wolpert here.
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