New science publication on why we see illusions
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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 useful
and consistent 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."