Andrew Meso

Dr Andrew Meso

  • 01202 962551
  • ameso at bournemouth dot ac dot uk
  • Senior Lecturer (Academic) in Psychology
  • Poole House P251 (Above Dylans), Talbot Campus, Fern Barrow, Poole, BH12 5BB
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During my university studies, I toured a few colleges of the University of London starting with Physics (BSc) and Medical Physics (MSc) before moving towards Computational Neuroscience and Experimental Psychology (PhD). My earliest research looked at how the brain makes sense of a dynamic, ambiguous visual world.

The academic tour then went global, breaking through the frontiers of Greater London. I worked on similar ideas as a post doc in Labs studying human vision using physiological, psychological and engineering approaches. The first of these was a Vision Research Lab at McGill University in the Québec city of Montreal (2009-2011) where I survived two serious winters while using psychophysics tasks to infer the hidden [inaccessible] hierarchical computational steps in visual processing. After that, I worked in more temperate Mediterranean climes at a Neuroscience Institute of the French CNRS in the port city of Marseille (2011-2015). There I learnt various eye tracking techniques and built on my mathematical and computational modelling and data analysis skills...



I'm fascinated by how effortlessly we use our eyes to make sense of the complex and dynamic world. My background spanning several scientific disciplines means that I find myself asking not just how vision works (the mechanisms), but also why the system is the way it is (psychology/philosophical underpinnings) and thinking about what new experiments might reveal current unknowns. My expertise is in the use of visual psychophysics [human behaviour experiments involving choices which are quantified] and eye movement recordings, both used in conjunction with computational or mathematical tools to analyse and model findings. I typically use simplified stimulation which trades off ecologic validity/’naturalness’ for simplicity and control in the form of explicitly defined sets of stimulus parameters.

I am interested in how we process moving scenes very quickly to extract object speeds and directions, how we accurately put together information that should go together in space and in time and in Gestalt grouping processes which seem to enhance vision e.g. in the presence of bilateral symmetry in scenes. What are the causes of variability across individuals’ performance and what role does the interaction of body physiology play in this variability? My specific current research includes:

[A]. Integration & segregation: how does the brain very quickly decide which parts of a moving scene to combine and which parts to process as separate entities? Do individual differences in the efficiency of these mechanisms serving integration/segregation provide a window into typical/atypical development and cognitive function? [eg...


Journal Articles

  • Li, Q., Meso, A., Logothetis, N.K. and Keliris, G.A., 2019. Scene regularity interacts with individual biases to modulate perceptual stability. Frontiers in Neuroscience.
  • Vacher, J., Meso, A.I., Perrinet, L.U. and Peyré, G., 2018. Bayesian modeling of motion perception using dynamical stochastic textures. Neural Computation, 30 (12), 3355-3392.
  • Medathati, N.V.K., Rankin, J., Meso, A.I., Kornprobst, P. and Masson, G.S., 2017. Recurrent network dynamics reconciles visual motion segmentation and integration. Scientific Reports, 7 (1).
  • Medathati, N.V.K., Rankin, J., Meso, A.I., Kornprobst, P. and Masson, G.S., 2017. Recurrent network dynamics reconciles visual motion segmentation and integration. Scientific Reports, 7.
  • Gekas, N., Meso, A.I., Masson, G.S. and Mamassian, P., 2017. A Normalization Mechanism for Estimating Visual Motion across Speeds and Scales. Current Biology.
  • Meso, A., Rankin, J., Faugeras, O., Kornprobst, P. and Masson, G., 2016. The relative contribution of noise and adaptation to competition during tri-stable motion perception. Journal of Vision.
  • Meso, A.I., Montagnini, A., Bell, J. and Masson, G.S., 2016. Looking for symmetry: fixational eye movements are biased by image mirror symmetry. Journal of Neurophysiology.
  • Meso, A.I. and Masson, G.S., 2015. Dynamic resolution of ambiguity during tri-stable motion perception. Vision Research, 107, 113-123.
  • Meso, A.I. and Chemla, S., 2015. Perceptual fields reveal previously hidden dynamics of human visual motion sensitivity. Journal of Neurophysiology, 114 (3), 1360-1363.
  • Bell, J., Manson, A., Edwards, M. and Meso, A.I., 2015. Numerosity and density judgments: Biases for area but not for volume. Journal of Vision, 15 (2).
  • Meso, A.I. and Simoncini, C., 2014. Towards an understanding of the roles of visual areas MT and MST in computing speed. Frontiers in Computational Neuroscience, 8 (AUG).
  • Bell, J., Sampasivam, S., McGovern, D.P., Meso, A.I. and Kingdom, F.A.A., 2014. Contour inflections are adaptable features. Journal of Vision, 14 (7).
  • Rankin, J., Meso, A.I., Masson, G.S., Faugeras, O. and Kornprobst, P., 2014. Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration. Journal of Computational Neuroscience, 36 (2), 193-213.
  • Meso, A.I., Durant, S. and Zanker, J.M., 2013. Perceptual separation of transparent motion components: The interaction of motion, luminance and shape cues. Experimental Brain Research, 230 (1), 71-86.
  • Meso, A.I. and Hess, R.F., 2012. Evidence for multiple extra-striate mechanisms behind perception of visual motion gradients. Vision Research, 64, 42-48.
  • Meso, A.I. and Hess, R.F., 2011. A visual field dependent architecture for second order motion processing. Neuroscience Letters, 503 (2), 77-82.
  • Meso, A.I. and Hess, R.F., 2011. Orientation gradient detection exhibits variable coupling between first- and second-stage filtering mechanisms. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 28 (8), 1721-1731.
  • Meso, A.I. and Hess, R.F., 2010. Visual motion gradient sensitivity shows scale invariant spatial frequency and speed tuning properties. Vision Research, 50 (15), 1475-1485.
  • Meso, A.I. and Zanker, J.M., 2009. Perceiving motion transparency in the absence of component direction differences. Vision Research, 49 (17), 2187-2200.
  • Meso, A.I. and Zanker, J.M., 2009. Speed encoding in correlation motion detectors as a consequence of spatial structure. Biological Cybernetics, 100 (5), 361-370.


  • Vacher, J., Meso, A.I., Perrinet, L. and Peyre, G., 2015. Biologically inspired dynamic textures for probing motion perception. 1918-1926.
  • Meso, A. and Zanker, J.M., 2008. Separating global motion components in transparent visual stimuli - A phenomenological analysis. 308-317.


  • Towards a physiological understanding of cyclical changes to human visual function (Royal Society, 01 Apr 2017). Awarded

Public Engagement & Outreach Activities

  • Bournemouth University Active Vision Workshop (June 2016)

Conference Presentations

  • Society for Neuroscience Annual Meeting 2018, Modelling the dynamic interactions of spatiotemporal channels during human ocular following, 03 Nov 2018, San Diego, California
  • European Conference on Visual Perception 2017, Smooth pursuit and saccades work to maintain tracking during naturalistic ball bouncing, 27 Aug 2017, Berlin, Germany
  • Society for Neuroscience Annual Meeting 2016, Repulsion of perceived visual motion direction as an emergent property of deciding to unify or segregate sources, 12 Nov 2016, San Diego, California
  • European Conference on Visual Perception 2016, Enhanced sensitivity to scene symmetry as a consequence of saccadic spatio-temporal sampling, 28 Aug 2016, Barcelona


  • Applied Vision Association (UK), Member (2007-),
  • Institute of Physics (UK), Corporate Member,
  • Society for Neuroscience, Member (2013-),
The data on this page was last updated at 08:28 on June 26, 2019.