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Circuit mechanisms of adaptive learning and choice under uncertainty


Certain characteristics of the real world require that learning and decision-making processes be adjusted constantly. These adjustments could be about what should be learned, how much should be learned, and what information should be used for making decisions. These different types of adjustments would rely on various neural mechanisms and involve multiple brain areas/circuits. Although several brain regions signal different forms of uncertainty (i.e. signals related to expected and unexpected uncertainty, surprise, novelty, etc.), it is unclear if there is regional specialization or instead, these signals are distributed across multiple brain areas. Furthermore, it is poorly understood how different types of uncertainty are encoded and integrated to promote adaptive learning, and what critical computations are performed in each area and their possible interactions.

This workshop will bring together a diverse group of scholars working on different aspects of learning and choice under uncertainty, including both experimental and theoretical approaches, different animal models (human and nonhuman primates, and rodents), recording methods (calcium imaging, single-cell and population electrophysiology, fMRI), and manipulation techniques (lesion, pharmacological inactivation, and DREADDS). Specifically, our speakers will address computational models for investigating uncertainty computations, brain regions involved in these computations, encoding and decoding of different forms of uncertainty signals, and different methods for dissecting the circuits involved in learning and choice under uncertainty.