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Virtual robots duped by illusions help to explain human vision

Publication date: Sep 28, 2007 8:38:52 AM

A study by researchers at UCL (University College London) explains why humans see illusions by showing that virtual robots trained to ‘see’ correctly also – as a consequence – make the same visual mistakes that we do. The study, published in the latest edition of PLoS Computational Biology, shows that illusions are an inevitable consequence of evolving useful behaviour in a complex world.

Illusions are defined in the Oxford English Dictionary as “something that deceives or deludes by producing a false impression.” Visual illusions, such as the ‘Hermann Grid Illusion’, trick the viewer into misinterpreting – in this case – shades of grey. The study’s senior author, Dr. Beau Lotto, UCL Institute of Ophthalmology, said: “Sometimes the best way to understand how the visual brain works is to understand why sometimes it does not. Thus lightness illusions have been the focus of scientists, philosophers and artists interested in how the mind works for centuries. And yet why we see them is still unclear.”

To address the question of why humans see illusions, researchers at the UCL Institute of Ophthalmology used artificial neural networks, effectively virtual toy robots with miniature virtual brains, to model, not human vision as such, but human visual ecology. Dr David Corney in Dr. Lotto’s lab trained the virtual robots to predict the reflectance (shades of grey) of surfaces in different 3D scenes not unlike those found in nature. Although the robots could interpret most of the scenes effectively, and differentiate between surfaces correctly, they also – as a consequence – exhibited the same lightness illusions that humans see.

Dr. Lotto said: “In short, they not only get it right like we do, but they also get it wrong like we do too. This provides causal evidence that illusions represent not the world as it is, but what proved useful to see in one’s past interactions with the sources of retinal images. The virtual robots in this study were driven solely by the statistics of their training history and used these statistics as the basis of their correct and subsequent incorrect decisions. Similarly, we believe the human brain generates perceptions of the world in the same way, by encoding the statistical relationships between images and scenes in our past visual experience and uses this as the basis for behaving usefully and consistently towards the sources of visual images.”

Although the artificial neural networks used in the research are much less complex than the human visual system, this simplicity helped the researchers to identify and further understand what they believe is a fundamental principle behind why we see illusion: the statistics of our past visual experiences. As the brain does not have direct contact with the world, but only an image of the world on the retina which is ambiguous, it has to call on the statistics of how it behaved in the past to understand how to behave in the future. Dr Corney said: “Every scene is ambiguous, to us, to animals and to robots. Our eyes and brains have evolved to let us behave effectively and so survive. So when presented with any image of the world, what we see is what would have been useful to see in the past. Illusions are uncommon and so misinterpreting an image rarely matters.”

Dr. Lotto added: “The study also suggests the first biologically-based definition of what an illusion is: the condition in which the actual source of a stimulus differs from its most likely source. When we see an illusion we are seeing the most likely source of the image given history. Since resolving ambiguous sensory information is a challenge faced by all visual systems, including the virtual robots in this study, it is likely that illusions must be experienced by all visual animals regardless of their particular neural machinery. Visual illusions have been central to the science and philosophy of human consciousness for centuries and this research demonstrates that how we respond to them can give vital information about the processes behind vision.”


Notes to Editors

1. For more information, to obtain high-resolution images, or to arrange an interview with Professor Lotto, please contact Ruth Metcalfe in the UCL Media Relations Office on tel: +44 (0)20 7679 9739, mobile: +44 (0)7990 675 947, out of hours: +44 (0)7917 271 364, e-mail:

2. For more information about Professor Lotto’s work, please go to:

3. Examples of brightness/lightness illusions can be found at:

4. Copies of the paper ‘What are lightness illusions and why do we see them?’ can be obtained from Ruth Metcalfe in the UCL Media Relations Office