Researchers in the School are using a variety of computational models to understand the mind and brain based on frameworks including neural networks, machine learning, predictive coding and Bayesian inference. These models are tested using behavioural and brain imaging data. Researchers at the university are involved in a variety of work and research areas:
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Tom FitzGerald is challenging and extending predictive coding accounts of brain function using nonlinear and non-Gaussian Bayesian models.
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Will Penny is developing models of human decision making based on statistical machine learning.
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Tom Sambrook studies how we learn from pleasant and aversive events using Reinforcement Learning.
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Fraser Smith uses machine learning to test predictive coding models of activity in early sensory cortex.
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John Spencer develops Dynamic Field Theory models of perceptual decision making.