PhD MSc BA
Professor of Cognitive Neuroscience
- ERC Consolidator Investigator
- Staff Scientist, Deepmind
- Fellow of Wadham College
Human learning and decision-making
My work is concerned with understanding how humans learn and make decisions. We study learning in adults using computer-based tasks. We are interested in how humans acquire new concepts or patterns in data, and how they use this information to make decisions in novel settings. We simulate learning processes using computational models, including deep neural nettworks, that are tasked with similar challenges. We study the brains of humans during learning and decision-making using noninvasive methods such as fMRI and EEG.
Dissociable prior influences of signal probability and relevance on visual contrast sensitivity.
Wyart V. et al, (2012), Proc Natl Acad Sci U S A, 109, 3593 - 3598
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.
Higgins I. et al, (2021), Nat Commun, 12
A Normative Account of Confirmation Bias During Reinforcement Learning.
Lefebvre G. et al, (2021), Neural Comput, 1 - 31
Normative Principles for Decision-Making in Natural Environments.
Summerfield C. and Parpart P., (2021), Annu Rev Psychol
Correction for Dumbalska et al., A map of decoy influence in human multialternative choice.
(2021), Proc Natl Acad Sci U S A, 118
Neural state space alignment for magnitude generalization in humans and recurrent networks.
Sheahan H. et al, (2021), Neuron, 109, 1214 - 1226.e8