By making a neural-network computer model that can be fooled by optical illusions like humans, the researchers advanced knowledge of the human visual system and may help improve artificial vision
Is that circle green or gray? Are the center lines straight or tilted?
Optical illusions can be fun to experience and debate, but understanding how human brains perceive these different phenomena remains an active area of scientific research. For one class of optical illusions, called contextual phenomena, those perceptions are known to depend on context.
For example, the color you think a central circle is depends on the color of the surrounding ring. Sometimes the outer color makes the inner color appear more similar, such as a neighboring green ring making a blue ring appear turquoise -- but sometimes the outer color makes the inner color appear less similar, such as a pink ring making a grey circle appear greenish.
A team of Brown University computer vision experts went back to square one to understand the neural mechanisms of these contextual phenomena. Their study was published on Sept. 20 in Psychological Review.
"There's growing consensus that optical illusions are not a bug but a feature," said Thomas Serre, an associate professor of cognitive, linguistic and psychological sciences at Brown and the paper's senior author. "I think they're a feature. They may represent edge cases for our visual system, but our vision is so powerful in day-to-day life and in recognizing objects."
For the study, the team lead by Serre, who is affiliated with Brown's Carney Institute for Brain Science, started with a computational model constrained by anatomical and neurophysiological data of the visual cortex. The model aimed to capture how neighboring cortical neurons send messages to each other and adjust one another's responses when presented with complex stimuli such as contextual optical illusions.
One innovation the team included in their model was a specific pattern of hypothesized feedback connections between neurons, said Serre. These feedback connections are able to increase or decrease -- excite or inhibit -- the response of a central neuron, depending on the visual context.
These feedback connections are not present in most deep learning algorithms. Deep learning is a powerful kind of artificial intelligence that is able to learn complex patterns in data, such as recognizing images and parsing normal speech, and depends on multiple layers of artificial neural networks working together. However, most deep learning algorithms only include feedforward connections between layers, not Serre's innovative feedback connections between neurons within a layer.
Once the model was constructed, the team presented it a variety of context-dependent illusions. The researchers "tuned" the strength of the feedback excitatory or inhibitory connections so that model neurons responded in a way consistent with neurophysiology data from the primate visual cortex.
Then they tested the model on a variety of contextual illusions and again found the model perceived the illusions like humans.
In order to test if they made the model needlessly complex, they lesioned the model -- selectively removing some of the connections. When the model was missing some of the connections, the data didn't match the human perception data as accurately.
"Our model is the simplest model that is both necessary and sufficient to explain the behavior of the visual cortex in regard to contextual illusions," Serre said. "This was really textbook computational neuroscience work -- we started with a model to explain neurophysiology data and ended with predictions for human psychophysics data."
In addition to providing a unifying explanation for how humans see a class of optical illusions, Serre is building on this model with the goal of improving artificial vision.
State-of-the-art artificial vision algorithms, such as those used to tag faces or recognize stop signs, have trouble seeing context, he noted. By including horizontal connections tuned by context-dependent optical illusions, he hopes to address this weakness.
Perhaps visual deep learning programs that take context into account will be harder to fool. A certain sticker, when stuck on a stop sign can trick an artificial vision system into thinking it is a 65-mile-per-hour speed limit sign, which is dangerous, Serre said.
The research team included Brown graduate student David Mély and postdoctoral scholar Drew Linsley. The research was supported by the National Science Foundation (IIS-1252951) and DARPA (YFA N66001-14-1-4037).
Plant identification increased tenfold with Flora Incognita App in March
03.04.2020 | Technische Universität Ilmenau
AI finds 2D materials in the blink of an eye
02.04.2020 | Institute of Industrial Science, The University of Tokyo
Drops of water falling on or sliding over surfaces may leave behind traces of electrical charge, causing the drops to charge themselves. Scientists at the Max Planck Institute for Polymer Research (MPI-P) in Mainz have now begun a detailed investigation into this phenomenon that accompanies us in every-day life. They developed a method to quantify the charge generation and additionally created a theoretical model to aid understanding. According to the scientists, the observed effect could be a source of generated power and an important building block for understanding frictional electricity.
Water drops sliding over non-conducting surfaces can be found everywhere in our lives: From the dripping of a coffee machine, to a rinse in the shower, to an...
90 million-year-old forest soil provides unexpected evidence for exceptionally warm climate near the South Pole in the Cretaceous
An international team of researchers led by geoscientists from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) have now...
The bacteria that cause tuberculosis need iron to survive. Researchers at the University of Zurich have now solved the first detailed structure of the transport protein responsible for the iron supply. When the iron transport into the bacteria is inhibited, the pathogen can no longer grow. This opens novel ways to develop targeted tuberculosis drugs.
One of the most devastating pathogens that lives inside human cells is Mycobacterium tuberculosis, the bacillus that causes tuberculosis. According to the...
An international team with the participation of Prof. Dr. Michael Kues from the Cluster of Excellence PhoenixD at Leibniz University Hannover has developed a new method for generating quantum-entangled photons in a spectral range of light that was previously inaccessible. The discovery can make the encryption of satellite-based communications much more secure in the future.
A 15-member research team from the UK, Germany and Japan has developed a new method for generating and detecting quantum-entangled photons at a wavelength of...
Together with their colleagues from the University of Würzburg, physicists from the group of Professor Alexander Szameit at the University of Rostock have devised a “funnel” for photons. Their discovery was recently published in the renowned journal Science and holds great promise for novel ultra-sensitive detectors as well as innovative applications in telecommunications and information processing.
The quantum-optical properties of light and its interaction with matter has fascinated the Rostock professor Alexander Szameit since College.
02.04.2020 | Event News
26.03.2020 | Event News
23.03.2020 | Event News
03.04.2020 | Materials Sciences
03.04.2020 | Life Sciences
03.04.2020 | Life Sciences