Predictive processes in the visual system and beyond
Our brains are adept at learning from repeating patterns in our environment. We can easily learn associations between successive events, such as hearing a dog bark and the appearance of a dog. Our expectations are also guided by other types of knowledge. For example, we know that our visual world is stable over time – if we stare at our coffee cup, we don’t expect it to suddenly change into something completely different. We use a variety of neuroimaging techniques, including EEG and fMRI, to investigate how the brain forms and evaluates predictions about visual events. We probe what happens when these predictions are fulfilled, and what happens when they are violated by an unexpected event. We are also interested in how predictive processes relate to other phenomena such as neural adaptation and attention.
Joint modelling of behavioural and neural data to track unfolding decision processes
Making even a simple decision recruits extensive networks that are distributed throughout the brain. For example, when judging whether this text is black or white, information is transmitted through the retina, through the visual system and to a vast network of parietal, frontal and motor areas in the brain. We use electroencephalography (EEG) in combination with computational modelling of behaviour to understand how decisions unfold in the brain, and how we adjust our decision-making strategies following surprising or mentally effortful events.
How our predictions shape our conscious experience
We tend to see what we expect to see. When there are strong expectations to see a certain image, people have a tendency to report seeing that image, even in cases where it didn’t appear. We are investigating how the brain biases our conscious perception toward the predicted and away from the unexpected.
Best practices for performing multivariate pattern analysis (MVPA) on EEG data
The practice of 'decoding' perceptual and cognitive variables based on distributed patterns of neural activity has become wildly popular in cognitive neuroscience. We are systematically investigating key features of these multivariate pattern analysis techniques in order to help ourselves and others to design more optimal experiments and analysis approaches. We are also co-developing a freely-available toolbox for performing these analyses on EEG data with Assoc. Prof. Stefan Bode (see our Code + Data page).
Nutrition and early childhood neurocognitive development
Micronutrients, especially iron, play a critical role in the developing infant brain. However, many infants around the world do not get adequate access to iron or other micronutrients during critical periods of development. We are investigating how iron and micronutrient deficiency affects the developing brain as assessed using behavioural and neural measures of cognition.
If you are interested in contributing to any of these projects, or have another idea you would like to discuss, please get in touch!
We gratefully acknowledge funding from the following sources: