Complex cells reduce Müller
Posted: Tue Dec 17, 2024 4:20 am
By dissociating our sensory perceptions from the physical features of a stimulus, visual illusions provide neuroscientists with a unique opportunity to study the neural mechanisms underlying our sensory experiences (Eagleman, 2001; Panagiotaropoulos et al., 2012). The salient perceptions that visual illusions create, coupled with the fact that they arise from internal processing, constantly stimulate researchers to search for the mechanism and location within the brain where illusions originate. However, illusions have proven to be as difficult to explain as any other perceptual phenomenon.
The physiological origins of some illusions have been investigated in animals, some of which are known to perceive them in a similar way to humans (Tudusciuc and Nieder, 2010). This research germany email list shows that perceptual phenomena such as visual masking, flash suppression, filling in, motion-induced depth and cyclopean perception (random dot stereograms) are present at early stages of visual processing in structures such as the thalamus and the primary and secondary visual cortices (Carney et al., 1989; Macknik et al., 2000; von der Heydt et al., 2000; Grinvald and Hildesheim, 2004; Wilke et al., 2009).
The Müller–Lyer illusion (MLI) is a simple and well-studied geometric illusion that in its classical form consists of two horizontal line segments that are perceived as having different lengths depending on whether they have arrowheads or arrowheads at their ends (Figures 1B–E). In an effort to understand the neural mechanisms behind the illusion, previous work by Zeman et al. (2013) demonstrated that the MLI is present in the artificial multilayer network HMAX, which is a model that incorporates many features of the primate visual system (Serre et al., 2005). The authors first trained the network to categorize images of short and long horizontal axes, presented in configurations that do not evoke the illusion in humans. After this training, they asked the network to classify the axis lengths of images containing the classical MLI.
The results show that the HMAX network showed a bias in classifying horizontal axes, classifying those with arrowheads as shorter than they actually were. Interestingly, the magnitude of the bias was similar to that measured in humans, and this effect was also modulated by the angle of the fins, with smaller angles (closer to the horizontal axis) producing a larger bias. Importantly, the authors showed that the final classification layer,
The physiological origins of some illusions have been investigated in animals, some of which are known to perceive them in a similar way to humans (Tudusciuc and Nieder, 2010). This research germany email list shows that perceptual phenomena such as visual masking, flash suppression, filling in, motion-induced depth and cyclopean perception (random dot stereograms) are present at early stages of visual processing in structures such as the thalamus and the primary and secondary visual cortices (Carney et al., 1989; Macknik et al., 2000; von der Heydt et al., 2000; Grinvald and Hildesheim, 2004; Wilke et al., 2009).
The Müller–Lyer illusion (MLI) is a simple and well-studied geometric illusion that in its classical form consists of two horizontal line segments that are perceived as having different lengths depending on whether they have arrowheads or arrowheads at their ends (Figures 1B–E). In an effort to understand the neural mechanisms behind the illusion, previous work by Zeman et al. (2013) demonstrated that the MLI is present in the artificial multilayer network HMAX, which is a model that incorporates many features of the primate visual system (Serre et al., 2005). The authors first trained the network to categorize images of short and long horizontal axes, presented in configurations that do not evoke the illusion in humans. After this training, they asked the network to classify the axis lengths of images containing the classical MLI.
The results show that the HMAX network showed a bias in classifying horizontal axes, classifying those with arrowheads as shorter than they actually were. Interestingly, the magnitude of the bias was similar to that measured in humans, and this effect was also modulated by the angle of the fins, with smaller angles (closer to the horizontal axis) producing a larger bias. Importantly, the authors showed that the final classification layer,