Guest Lecture by Christopher Summerfield, University of Oxford, UKTitle: Using neural networks to understand human learning
Abstract: Neural networks have been proposed as theories of perception and cognition. However, most studies have focussed on comparing representations in biological and artificial learners once learning is complete. Here, I will describe three projects in which we study the dynamics of learning, and sensitivity to training curricula, in humans and neural networks. In the first project, we understand why humans learning to integrate multiple pieces of information benefit from a 'divide and conquer' strategy, and use a neural network to design a curriculum that successfully accelerates human learning. In the second project, I show how humans and neural networks have remarkably similar patterns of transfer and interference during continual learning. In the final project, I show how humans and transformer networks have very similar sensitivity to the data diet on which they are trained.
The lecture is followed by discussions with light snacks and drinks.
More about Christopher Summerfield at www.psy.ox.ac.uk/people/christopher-summerfield
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