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.”
For more information about Professor Lotto’s work, please go to:
lottolab
Examples of brightness/lightness illusions can be found
here.
CITATION: Corney D, Lotto RB (2007) What Are Lightness Illusions and Why
Do We See Them? PLoS Comput Biol 3(9): e180
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