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Faculty of Science, Engineering and Computing.
Penrhyn Road
Kingston upon Thames
Surrey KT1 2EE

Tel: +44 (0)20 8417 9000

Dr Jan Lauritzen

School/Department: Life Sciences, Pharmacy & Chemistry
Position: Senior Lecturer


Born in Strasbourg, France, Jan was educated in France, Germany and the UK.

He graduated in neuroscience from St. Catharine's College Cambridge in 1997, followed by an MPhil on computational modeling of contrast processing in the mammalian visual cortex.

Jan completed the research for his PhD (Contrast Normalisation and the Visual Coding of Contrasts in Natural Images) in 2001 in the Department of Physiology at Cambridge.

He worked as a Lecturer in Vision Science in the School of Biomedical Sciences at the University of Ulster from 2001-2008, contributing to teaching of the Optometry degree programme and other courses across the school, and as a member of the Vision Science Research Group.

Jan is currently a Senior Lecturer in the School of Life Sciences at Kingston University.

Educational and Professional Qualifications

  • 1998 - 2001 PhD Biological Sciences, University of Cambridge
  • 1997 - 1998 MPhil Biological Sciences, University of Cambridge
  • 1994 - 1997 MA(Cantab) Natural Sciences, University of Cambridge

Research Interests

Dr Lauritzen's research focuses on human psychophysics of early visual processes in the retina, primary visual cortex and extrastriate areas, and computer modelling of those processes. In collaborations, Dr Lauritzen also has a wider multidisciplinary interest in computer modelling of physiological processes in general and the design of psychophysical protocols for sensory physiology and experimental psychology.

Computer modelling:

The early steps in vision are mediated by cells with relatively simple receptive fields that have been well described. However, computer models using such receptive fields nevertheless provide poor predictions of psychophysical performance in tasks thought to be mediated exclusively or predominantly in the retina and primary visual cortex (V1), especially when the test image is complex, such as a natural image. Computer modelling of the connections between cells in V1 involved in contrast gain control mechanisms and responses from outside the classical receptive field has been used to improve prediction of psychophysical results in contrast discrimination tasks involving natural images.

A similar computer model has also been used to demonstrate that contrast gain control can adequately account for differences in the contrast in natural images produced by different lighting conditions (diffuse and direct) , thus providing a form of 'contrast constancy'.

Human psychophysics

Contrast discrimination tasks, where human observers are detect a simple contrast stimulus before a masking background, provide well-understood, predictable thresholds when the mask is also simple. However as the mask becomes more complex, thresholds become rapidly less predictable, and for natural or pseudo-natural images results can be counter-intuitive. For example, masking experiments on natural images have shown that, when an image is filtered to remove the contrast that should optimally mask a particular contrast channel, thresholds often increase (i.e. the task becomes harder). Intracortical interactions are thought responsible, and computer models are being used to determine the nature of these interactions, in view of predicting psychophysical thresholds correctly for different types of stimuli with a single model.

Current research focuses on designing psychophysical techniques to separate different aspects of visual processing in a variety of conditions affecting vision, for example identifying optical, retinal and cortical contributions to poor visual performance in Down syndrome, and investigating the effect of colour on the visual processing of temporally and spatially structured stimuli in amblyopia and dyslexia.

Teaching Area

Neurophysiology, especially sensory processing. General physiology. Research methods and statistics. Pharmacology.


    +Professional Experience



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