skip to primary navigation skip to content

Dr David Tolhurst

University Senior Lecturer
Tel: +44 (0)1223 333889, Fax: +44 (0)1223 333840, E-mail: djt12@cam.ac.uk

My research interests are in the area of the psychophysics and computational modelling of vision.

Computational and psychophysical investigation of human spatial and chromatic vision
My research is primarily concerned with the ways in which the visual system encodes the spatial, temporal and chromatic information found in the natural visual environment. Our understanding of the coding properties of visual neurons is based primarily on experimental measures of their responses to simplified, "laboratory" stimuli (mostly these are sinusoidal gratings presented singly or in combination). However, we must study vision in natural tasks, in order to understand why the visual system uses particular strategies to encode information. The beauty of sinusoids is that they comprise a well-defined set of stimuli with well-behaved predictive properties. Natural images, on the other hand, vary considerably in content, and are difficult to categorise rigorously into experimental sets. One of the challenges of our research has been to develop stimuli based on photographs of natural scenes that are representative of all scenes, and that challenge our psychophysics and modelling fairly.

The psychophysical studies involve development of techniques for quantifying human perception of small differences in natural scenes, or for understanding the way in which people search for known objects in a scene. We have used magnitude estimation rating methods to measure how well people can distinguish small changes in colour, or focus, or shape or arrangement of objects in digitised photographs. We aim then to develop a computational model of the coding of information in the visual cortex, which will stylise the response behaviour of millions of cortical simple cells or complex cells to help us understand why people can distinguish some natural image changes and not others. We aim to determine which features can be predicted from understanding low-level visual-cortex coding and which features (such as faces or shadows) require understanding of higher-level cognitive processes. Psychophysical study of the perception of differences or changes in natural images leads to an interest in camouflage strategies.

Computational modelling can also be used to ask whether the particular coding shown by the visual cortex has any evolutionary or ecological advantage. For instance, does the particular configuration of orientation-tuning in simple cells represent an energy-efficient or sparse code of visual information? The speculations from such modelling have been bolstered by direct measurement of the ways in which visual-cortex neurons respond to natural images.

Psychophysics with natural images and a visual cortex-based computational model of the psychophysical results
Párraga, C.A., Troscianko, T. & Tolhurst, D.J. (2000). The human visual system is optimised for processing the spatial information in natural visual images. Current Biology, 10, 35-38.

Tolhurst, D.J. & Tadmor, Y. (2000). Discrimination of spectrally-blended natural images: Optimisation of the human visual system for encoding natural images. Perception, 29, 1087-1100.

Párraga, C.A., Troscianko, T. & Tolhurst, D.J. (2005). The effects of amplitude-spectrum statistics on foveal and peripheral discrimination of changes in natural images, and a multi resolution model. Vision Research, 45, 3145-3168.

Tolhurst, D.J., Párraga, C.A., Lovell, P.G., Ripamonti, C. & Troscianko, T. (2005). A multiresolution color model for visual difference prediction. Proceedings of the 2nd Conference of APGV. ACM International Conference Proceeding Series, 95, 135-138.

Lovell, P.G., Párraga, C.A., Ripamonti, C., Troscianko, T. & Tolhurst, D.J. (2006). Evaluation of a multi-scale color model for visual difference prediction. ACM Transactions on Applied Perception, 3, 155-178.

To, M., Lovell, P.G., Troscianko, T. & Tolhurst, D.J. (2008). Summation of perceptual cues in natural visual scenes. Proceedings of the Royal Society B, 275, 2299-2308.

Lovell, P.G., Gilchrist, I.D., Tolhurst, D.J. & Troscianko, T. (2009). Search for gross illumination discrepancies in images of natural objects. Journal of Vision, 9, 37:1-14.

To, M.P.S., Gilchrist, I.D., Troscianko, T., Kho, J.S.B. & Tolhurst, D.J. (2009). Perception of differences in natural-image stimuli: Why is peripheral viewing poorer than foveal? ACM Transactions on Applied Perception, 6, 26:1-9.

To, M.P.S., Lovell, P.G., Troscianko, T. & Tolhurst, D.J. (2010). Perception of suprathreshold naturalistic changes in colored natural images. Journal of Vision, 10, 12:1-22.

Tolhurst, D.J., To, M.P.S., Chirimuuta, M., Troscianko, T., Chua, P.Y. & Lovell, P.G. (2010). Magnitude of perceived change in natural images may be linearly proportional to differences in neuronal firing rates. Seeing & Perceiving, 23, 349-372.

To, M.P.S., Baddeley, R.J., Troscianko, T. & Tolhurst, D.J. (2011). A general rule for sensory cue summation: Evidence from photographic, musical, phonetic and cross-modal stimuli. Proceedings of the Royal Society B, 278, 1365-1372.

To, M.P.S., Gilchrist, I.D., Troscianko, T. & Tolhurst, D.J. (2011). Discrimination of natural scenes in central and peripheral vision. Vision Research, 51, 1686-1698.

And some thoughts on animal camouflage
Troscianko, T., Benton, C.P., Lovell, P.G., Tolhurst, D.J. & Pizlo, Z. (2009). Camouflage and visual perception. Philosophical Transactions of the Royal Society B, 364, 449-461.

Computational studies, asking whether the visual-cortex coding is efficient or optimised for natural images
Willmore, B. & Tolhurst, D.J. (2001). Characterising the sparseness of neural codes. Network, Computation in Neural Systems, 12, 255-270.

Párraga, C.A., Troscianko, T. & Tolhurst, D.J. (2002). Spatio-chromatic properties of natural images and human vision. Current Biology, 12, 483-487.

Chirimuuta, M., Clatworthy, P.L. & Tolhurst, D.J. (2003). Coding of the contrasts in natural images by visual cortex (V1) neurons: A Bayesian approach. Journal of the Optical Society of America A, 20, 1253-1260.

Clatworthy, P.L., Chirimuuta, M., Lauritzen, J.S. & Tolhurst, D.J. (2003). Coding of the contrasts in natural images by populations of neurons in primary visual cortex (V1). Vision Research, 43, 1983-2001.

Caywood, M.S., Willmore, B. & Tolhurst, D.J. (2004). Independent components of color natural scenes resemble V1 neurons in their spatial and color tuning. Journal of Neurophysiology, 91, 2859-2873

Lovell, P.G., Tolhurst, D.J., Párraga, C.A., Baddeley, R., Leonards, U., Troscianko, J. & Troscianko, T. (2005). On the stability of the color-opponent signals under changes of illuminant in natural scenes. Journal of the Optical Society of America A, 22, 2060-2071.

Lauritzen, J.S. & Tolhurst, D.J. (2005). Contrast constancy in natural scenes in shadow or direct light – a proposed role for contrast-normalisation (non-specific suppression) in visual cortex. Network, Computation in Neural Systems, 16, 151-173.

Visual-cortex neurons and their responses to natural images
Smyth, D., Willmore, B., Thompson, I.D., Baker, G.E. & Tolhurst, D.J. (2003). The receptive-field organisation of simple cells in primary visual cortex (V1) of ferrets under natural scene stimulation. Journal of Neuroscience, 23, 4746-4759.

Tolhurst, D.J., Smyth, D. & Thompson, I.D. (2009). The sparseness of neuronal responses in ferret primary visual cortex. Journal of Neuroscience, 29, 2355-2370.

The albino visual system
Akerman, C.J., Tolhurst, D.J., Morgan, J.E., Baker, G.E. & Thompson, I.D. (2003). The relay of visual information to the lateral geniculate nucleus and the visual cortex in albino ferrets. Journal of Comparative Neurology, 461, 217-235.

Hoffmann, M.B., Tolhurst, D.J., Moore, A.T. & Morland, A.B. (2003). Organisation of the visual cortex in human albinism. Journal of Neuroscience, 23, 8921-8930.

Psychophysics with sinusoidal gratings
Chirimuuta, M. & Tolhurst, D.J. (2005). Does a Bayesian model of V1 contrast coding offer a neurophysiological account of human contrast discrimination? Vision Research, 45, 2943-2959.

Chirimuuta, M. & Tolhurst, D.J. (2005). Accuracy of identification of grating contrast by human observers: Bayesian models of V1 contrast processing show correspondence between discrimination and identification performance. Vision Research, 45, 2960-2971.