I use fMRI, psychophysics and deep neural network modelling to understand how visual processing in healthy human brains allows us to understand the surfaces, materials, shapes, and objects present in an image. I’m interested in neural representation, object recognition, and (ultimately) consciousness. I am also fascinated by the history and philosophy of our attempts to understand perception..

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Object recognition in humans and deep neural networks

In the past few years, artificial neural networks have finally rivalled human performance in difficult tasks such as recognising objects in cluttered natural images. My current research focusses on whether these 'engineering solutions' might solve the problems of vision in similar ways to human brains, and how we can create more biologically realistic models.

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face perception

Much of my PhD research addressed the question of whether faces are encoded in the brain as individual 'exemplars,' or in terms of how they differ from a special 'norm' such as the average face. My interest in face perception has continued into my postdoc, where I am currently comparing computational models of perceived facial similarity.

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adaptation aftereffects

In my PhD research, I used psycho-physics and population code modelling to explore what visual aftereffects can tell us about the neural encoding of spatial patterns throughout the visual hierarchy, from simple visual stimuli like tilted lines, through inter-mediate shapes, to complex stimuli like faces.