Professor Zoe Kourtzi
College Position
Fellow in Cognitive Neuroscience
University Position
Professor of Experimental Psychology
Other Positions

Angharad Dodds John Fellow, Fellow of the Alan Turing Institute

Degrees and Honours


Research Interests

I am a cognitive neuroscientist, specialising in the area of lifelong learning and brain plasticity. My work aims to understand the role of learning and experience in enabling humans of all ages to translate sensory experience into complex decisions and adaptive behaviours. In my lab, we combine multimodal brain imaging (structural and functional MRI, EEG, TMS), established behavioural paradigms from cognitive psychology and state-of-the art computational science to understand the link between brain structure, neural function and behaviour. This multidisciplinary approach advances our understanding of the brain mechanisms that promote lifelong learning and has translational applications in educational and clinical practice (i.e. development of diagnostic tools of cognitive health and training programmes in healthy ageing and neurodegenerative disease).

Teaching Interests

Brain Imaging, Leaning and Brain Plasticity, Cognitive and Experimental Psychology

Awards & Prizes

Fellow of the Alan Turing Institute, October 2017 to present
Vision Science Society Young Investigator Award, May 2007
Attempto-Preis, University ofTuebingen, May 2003.

Select Publications

Wang R, Shen Y, Tino P, Welchman AE, Kourtzi Z (2017) Learning predictive statistics: strategies and brain mechanisms. J Neuroscience, 37:8374-8384

Luft CD, Baker R, Goldstone A, Zhang Y, Kourtzi Z. (2016) Learning temporal statistics for sensory predictions in ageing. Journal of Cognitive Neuroscience, 28:418-32.

Kuai S, Levi D, Kourtzi Z (2013). Learning optimizes decision templates in the ventral visual cortex. Current Biology, 23, 1799-804.

Kuai S, Kourtzi Z (2013). Learning to see, but not discriminate visual forms is impaired in aging. Psychological Science, 24, 412-22

 Kourtzi Z, Connor E (2011) Neural representations for object perception: structure, category and adaptive coding. Annual Review of Neuroscience, 34, 45-67.