- Human Information Processing Lab (Prof. Chris Summerfield) Research Group
PhD MSc BA
Professor of Cognitive Neuroscience
- ERC Starter Investigator
- Fellow of Wadham College
Neural and computational bases of human decision-making
My work is concerned with understanding the neural and comptutational mechanisms that underlie human perception and cognition. The main function of the nervous system is to select an appropriate action in response to incoming sensory information. In my lab, we attempt to understand how this occurs by using simple judgment tasks in which participants view a visual stimulus and classify it into one of two categories (i.e. is this grating tilted leftwards or rightwards? Is this face male or female?). We begin by measuring behaviour (choices and response times), eye movments and pupil diameter, and performing computer simulations to try to provide a mechanistic account of how information is transformed en route from sensation to action. Subsequently, we record brain activity using electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), which can help validate the computational model and identify how it is implemented in the neural circuitry of the human brain.
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
Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making.
Kurzawa N. et al, (2016), Neural Comput, 1 - 26
Visual prediction error spreads across object features in human visual cortex.
Jiang 江界峰 J. et al, (2016), J Neurosci
Encoding of Stimulus Probability in Macaque Inferior Temporal Cortex.
Bell AH. et al, (2016), Curr Biol, 26, 2280 - 2290
Reply to Davis-Stober et al.: Violations of rationality in a psychophysical task are not aggregation artifacts.
Tsetsos K. et al, (2016), Proc Natl Acad Sci U S A, 113, E4764 - E4766
Feature-Based Attention and Feature-Based Expectation.
Summerfield C. and Egner T., (2016), Trends Cogn Sci, 20, 401 - 404