Armed with a new computer model, scientists have taken a step closer to uncovering the mind-boggling secrets of optical illusions that trick the brain into seeing the wrong colors when processing images.
“Synchronized contrast illusions” are a large-scale group of deceptive illustrations that trick people into thinking that certain parts of an image are different colors from one another, when they are actually the same color. The effect is based on the photographer changing the brightness or color in the background, in order to change our perception of the objects in the foreground. For example, in the image above, the smaller band in the middle of the image is a single color of gray but appears to be gradated to different shades because the background is brighter at one end and darker at the other. Another example is They are Monker Whiteshown in the image below, where 12 balls appear red, purple, and green but are actually the same shade of beige.
Scientists have widely known why these illusions work for more than a century, but in all that time, experts have been unable to agree on exactly how they deceived. brain. There are two possible explanations. The first is that the illusion is created from the bottom up, starting with low-level neural activity that requires no prior exposure to this type of illusion. The second is top-down, which means it requires higher brain function and plays with what your brain previously learned about the brightness and color of light over time.
In a new study published June 15 in the journal Computational Biologya pair of researchers used a new computer model that simulates human vision to try to settle the controversy once and for all.
Related: A new kind of optical illusion tricks the brain into seeing dazzling rays
The model, known as the “limited spatial bandwidth model,” uses computer code to mimic how the network of brain cells, or neurons, that first receive data from the eye begins to decode the image before sending that data to other, “higher-level” brain regions. to be fully processed. The model divides the image into sections, measures the brightness of each section, and then combines those assessments into a single report that can be sent to the brain, similar to what happens with human vision.
The beauty of this model is that the code only allows individual sections to be processed at the same speed that human neurons can evaluate, so the model is limited to matching our own visual constraints, according to the study co-author. Joleon TroshiankoA visual ecologist at the University of Exeter in the UK told Live Science. “This aspect of the model is particularly new – no one seems to have thought about the impact of limited bandwidth on visual processing,” he added. Specifically, the new model takes into account how quickly a neuron can “fire,” or send a message to other neurons in its network.
The researchers used their new model for the analysis More than 50 contrasting illusions at once To see if the program will also mistakenly identify certain parts of the images as different colors, as a human would. (The report’s authors note that it’s unclear exactly how many simultaneous contrast illusions exist, but there are likely hundreds.)
During these experiments, Trocianco said, the model was constantly tricked into identifying the wrong colors. “My collaborator [Daniel Osorio] He kept emailing me with new illusions, saying he didn’t think it would work with this one,” he added, “but to our surprise and delight, I generally expected the illusion in almost all cases.”
Since the model was also “illusioned” by these illusions without the equivalent complex processing power of the human brain, it suggests that neither high-level visual processing nor prior experiences are required for these illusions to work. This seems to confirm the bottom-up hypothesis that only basic-level neural processing is responsible for deceiving the images, the authors conclude.
“In essence, many illusions previously thought to depend on complex visual processing, or at least visual processing that requires feedback loops, can be explained by something as simple as a single layer of neurons,” Trocianko said.
The findings support similar findings from a 2020 study published in the journal Vision Research. In that study, children who were born with cataracts but who had successfully undergone cataract removal surgery were deceived from photographs shortly after their sight was restored, despite the lack of prior visual experiences to provide context for the images.
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