Artificial intelligence 'sees' visual illusion
Kes: Oo, useful term: "ambiguous sense data"--I used to get a lot of that when I was low-vision, it was one of the things that often resulted in fun hallucinations.
Via MindHacks
A study
What Are Lightness Illusions and Why Do We See Them?
http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0030180&ct=1
just published in PLoS Computational Biology has reported that an artificial intelligence system trained to make sense of a simulated natural environment
is susceptible to some of the same visual illusions that humans fall for.
More links:
MindHacks post
http://www.mindhacks.com/blog/2007/10/artificial_intellige.html
lottolab.org:
http://www.lottolab.org/
Study write-up from the university's news site.
http://www.ucl.ac.uk/media/library/robotillusions
Introduction from the study "What Are Lightness Illusions and Why Do We See Them?"
block quote begin
Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling
not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural
networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination.
The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition-and as a direct consequence of their experience-the
networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation-although
assimilation (specifically White's illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions,
also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because
(i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in
past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions
must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion:
the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally
different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes.
block quote end
Via MindHacks
A study
What Are Lightness Illusions and Why Do We See Them?
http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0030180&ct=1
just published in PLoS Computational Biology has reported that an artificial intelligence system trained to make sense of a simulated natural environment
is susceptible to some of the same visual illusions that humans fall for.
More links:
MindHacks post
http://www.mindhacks.com/blog/2007/10/artificial_intellige.html
lottolab.org:
http://www.lottolab.org/
Study write-up from the university's news site.
http://www.ucl.ac.uk/media/library/robotillusions
Introduction from the study "What Are Lightness Illusions and Why Do We See Them?"
block quote begin
Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling
not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural
networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination.
The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition-and as a direct consequence of their experience-the
networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation-although
assimilation (specifically White's illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions,
also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because
(i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in
past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions
must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion:
the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally
different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes.
block quote end