Athena Akrami (University College London): Statistical learning of prior distributions in rodents, humans and machines

Event Details:

Monday, February 28, 2022
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1:00pm to 2:00pm PST
Event Sponsor
The Stanford Center for Mind, Brain, Computation and Technology
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Athena Akrami, PhD

Group Leader
University College London




The world around us is complex, but at the same time full of meaningful regularities. We can detect, learn and exploit these regularities automatically in an unsupervised manner i.e. without any direct instruction or explicit reward. For example, we effortlessly estimate the average tallness of people in a room, or the boundaries between words in a language. These regularities and prior knowledge, once learned, can affect the way we acquire and interpret new information to build and update our internal model of the world for future decision-making processes. Despite the ubiquity of passively learning from the structured information in the environment, the mechanisms that support learning from real-world experience are largely unknown. By combing sophisticated cognitive tasks in human and rats, neuronal measurements and perturbations in rat and network modelling, we aim to build a multi-level description of how sensory history is utilised in inferring regularities in temporally extended tasks. In this talk, I will specifically focus on a comparative rat and human model to study building and utilising statistical prior distributions in working memory and decision making behaviours.

Curriculum Vitae

Related Papers

Posterior parietal cortex represents sensory history and mediates its effects on behaviour

Efficient inference for time-varying behavior during learning