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Hans Strasburger - One of the best experts on this subject based on the ideXlab platform.
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inhibition of return in the visual field the eccentricity effect is independent of Cortical Magnification
Experimental Psychology, 2013Co-Authors: Yuan Fang, Yu Tong, Kerstin Schill, Ernst Poppel, Hans StrasburgerAbstract:Inhibition of return (IOR) as an indicator of attentional control is characterized by an eccentricity effect, that is, the more peripheral visual field shows a stronger IOR magnitude relative to the perifoveal visual field. However, it could be argued that this eccentricity effect may not be an attention effect, but due to Cortical Magnification. To test this possibility, we examined this eccentricity effect in two conditions: the same-size condition in which identical stimuli were used at different eccentricities, and the size-scaling condition in which stimuli were scaled according to the Cortical Magnification factor (M-scaling), thus stimuli being larger at the more peripheral locations. The results showed that the magnitude of IOR was significantly stronger in the peripheral relative to the perifoveal visual field, and this eccentricity effect was independent of the manipulation of stimulus size (same-size or size-scaling). These results suggest a robust eccentricity effect of IOR which cannot be eliminated by M-scaling. Underlying neural mechanisms of the eccentricity effect of IOR are discussed with respect to both Cortical and subCortical structures mediating attentional control in the perifoveal and peripheral visual field.
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a generalized Cortical Magnification rule predicts low contrast letter recognition in the visual field
Journal of Vision, 2010Co-Authors: Hans StrasburgerAbstract:Harvey L O Jr (1997). Efficient estimation of sensory thresholds with ML-PEST. Spatial Vision 11(1), 121-128. Strasburger H (1997). R_Contrast: Rapid measurement of recognition contrast thresholds. Spatial Vision 10, 495-498. Strasburger H (2001). Converting between measures of slope of the psychometric function. Perception & Psychophysics 63, 1348-1355. Strasburger H (2001). Invariance of the psychometric function for letter recognition across the visual field. Perception & Psychophysics 63, 1356-1376. Strasburger H & Rentschler I (1996). Contrast-dependent dissociation of visual recognition and detection fields. European Journal of Neuroscience 8, 1787-1791. Strasburger H, Rentschler I & Harvey L O Jr (1994). Cortical Magnification theory fails to predict visual recognition. European Journal of Neuroscience 6, 1583-1588. Strasburger H (2002). Indirektes Sehen. Formerkennung im zentralen und peripheren Gesichtsfeld. Hogrefe: Gottingen, Bern, Toronto, Seattle. A generalized Cortical Magnification rule predicts low-contrast letter recognition in the visual field Hans Strasburger Generation Research Program, Human Studies Center, University of Munchen strasburger@uni-muenchen.de —— www.hans.strasburger.de .
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Cortical Magnification theory fails to predict visual recognition
European Journal of Neuroscience, 1994Co-Authors: Hans Strasburger, Ingo Rentschler, Lewis O HarveyAbstract:The sense of form is poor in indirect view. Yet the Cortical Magnification theory asserts that the disadvantage can be made up by scaling the image size according to the spatial variation in the mapping of the retina onto the cortex. It is thus assumed that all visual information passes through a functionally homogeneous neural circuitry, with the spatial sampling of input signals varying across the visual field. We challenge this notion by showing that character recognition in the visual field cannot be accommodated by any concept of sole size scaling but requires increasing both size and contrast of the target being viewed. This finding is formalized into a hyperbolic law which states that target size multiplied by log contrast is constant across the visual field. We conclude that the scalar Cortical Magnification theory fails for character recognition since the latter depends on multidimensional pattern representations in higher, i.e. striate and prestriate, Cortical areas.
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Contrast thresholds for identification of numeric characters in direct and eccentric view
Perception & Psychophysics, 1991Co-Authors: Hans Strasburger, Lewis O Harvey, Ingo RentschlerAbstract:Aubert and Foerster (1857) are frequently cited for having shown that the lower visual acuity of peripheral vision can be compensated for by increasing stimulus size. This result is seemingly consistent with the concept of Cortical Magnification, and it has been confirmed by many subsequent authors. Yet it is rarely noted that Aubert and Foerster also observed a loss of the “quality of form.” We have studied the recognition of numeric characters in foveal and eccentric vision by determining the contrast required for 67% correct identification. At each eccentricity, the lowest contrast threshold is achieved with a specific stimulus size. But the contrast thresholds for these optimal stimuli are not independent of retinal eccentricity as Cortical Magnification scaling would predict. With high-contrast targets, however, threshold target sizes were consistent with Cortical Magnification out to 6° eccentricity. Beyond 6°, threshold target sizes were larger than Cortical Magnification predicted. We also investigated recognition performance in the presence of neighboring characters (crowding phenomenon). Target character size, distance of flanking characters, and precision of focusing of attention all affect recognition. The influence of these parameters is different in the fovea and in the periphery. Our findings confirm Aubert and Foerster’s original observation of a qualitative difference between foveal and peripheral vision.
Gilles Coppin - One of the best experts on this subject based on the ideXlab platform.
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A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention
Cognitive Computation, 2016Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:Background An emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and Cortical Magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account. Methods We propose a new framework for visual information acquisition and representation that emulates the architecture of the primate visual system by integrating features such as retinal sampling and Cortical Magnification while avoiding spatial deformations and other side effects produced by models that tried to implement these two features. It also explicitly integrates the notion of visual angle, which is rarely taken into account by vision models. We argue that this framework can provide the infrastructure for implementing vision tasks such as object recognition and computational visual attention algorithms. Results To demonstrate the utility of the proposed vision framework, we propose an algorithm for bottom-up saliency prediction implemented using the proposed architecture. We evaluate the performance of the proposed model on the MIT saliency benchmark and show that it attains state-of-the-art performance, while providing some advantages over other models. Conclusion Here is a summary of the main contributions of this paper: (1) Introducing a new bio-inspired framework for visual information acquisition and representation that offers the following properties: (a) Providing a method for taking the distance between an image and the viewer into account. This is done by incorporating a visual angle parameter which is ignored by most visual acquisition models. (b) Reducing the amount of visual information acquired by introducing a new scheme for emulating retinal sampling and Cortical Magnification effects observed in the ventral stream. (2) Providing a concrete application of the proposed framework by using it as a substrate for building a new saliency-based visual attention model, which is shown to attain state-of-the-art performance on the MIT saliency benchmark. (3) Providing an online Git repository that implements the introduced framework that is meant to be developed as a scalable, collaborative project.
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A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention
Cognitive Computation, 2016Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:An emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and Cortical Magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account.
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a model of bottom up visual attention using Cortical Magnification
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:The focus of visual attention has been argued to play a key role in object recognition. Many computational models of visual attention were proposed to estimate locations of eye fixations driven by bottom-up stimuli. Most of these models rely on pyramids consisting of multiple scaled versions of the visual scene. This design aims at capturing the fact that neural cells in higher visual areas tend to have larger receptive fields (RFs). On the other hand, very few models represent multi-scaling resulting from the eccentricity-dependent RF sizes within each visual layer, also known as the Cortical Magnification effect. In this paper, we demonstrate that using a Cortical-Magnification-like mechanism can lead to performant alternatives to pyramidal approaches in the context of attentional modeling. Moreover, we argue that introducing such a mechanism equips the proposed model with additional properties related to overt attention and distance-dependent saliency that are worth exploring.
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ICASSP - A model of bottom-up visual attention using Cortical Magnification
2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:The focus of visual attention has been argued to play a key role in object recognition. Many computational models of visual attention were proposed to estimate locations of eye fixations driven by bottom-up stimuli. Most of these models rely on pyramids consisting of multiple scaled versions of the visual scene. This design aims at capturing the fact that neural cells in higher visual areas tend to have larger receptive fields (RFs). On the other hand, very few models represent multi-scaling resulting from the eccentricity-dependent RF sizes within each visual layer, also known as the Cortical Magnification effect. In this paper, we demonstrate that using a Cortical-Magnification-like mechanism can lead to performant alternatives to pyramidal approaches in the context of attentional modeling. Moreover, we argue that introducing such a mechanism equips the proposed model with additional properties related to overt attention and distance-dependent saliency that are worth exploring.
Ala Aboudib - One of the best experts on this subject based on the ideXlab platform.
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A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention
Cognitive Computation, 2016Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:Background An emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and Cortical Magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account. Methods We propose a new framework for visual information acquisition and representation that emulates the architecture of the primate visual system by integrating features such as retinal sampling and Cortical Magnification while avoiding spatial deformations and other side effects produced by models that tried to implement these two features. It also explicitly integrates the notion of visual angle, which is rarely taken into account by vision models. We argue that this framework can provide the infrastructure for implementing vision tasks such as object recognition and computational visual attention algorithms. Results To demonstrate the utility of the proposed vision framework, we propose an algorithm for bottom-up saliency prediction implemented using the proposed architecture. We evaluate the performance of the proposed model on the MIT saliency benchmark and show that it attains state-of-the-art performance, while providing some advantages over other models. Conclusion Here is a summary of the main contributions of this paper: (1) Introducing a new bio-inspired framework for visual information acquisition and representation that offers the following properties: (a) Providing a method for taking the distance between an image and the viewer into account. This is done by incorporating a visual angle parameter which is ignored by most visual acquisition models. (b) Reducing the amount of visual information acquired by introducing a new scheme for emulating retinal sampling and Cortical Magnification effects observed in the ventral stream. (2) Providing a concrete application of the proposed framework by using it as a substrate for building a new saliency-based visual attention model, which is shown to attain state-of-the-art performance on the MIT saliency benchmark. (3) Providing an online Git repository that implements the introduced framework that is meant to be developed as a scalable, collaborative project.
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A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention
Cognitive Computation, 2016Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:An emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and Cortical Magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account.
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a model of bottom up visual attention using Cortical Magnification
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:The focus of visual attention has been argued to play a key role in object recognition. Many computational models of visual attention were proposed to estimate locations of eye fixations driven by bottom-up stimuli. Most of these models rely on pyramids consisting of multiple scaled versions of the visual scene. This design aims at capturing the fact that neural cells in higher visual areas tend to have larger receptive fields (RFs). On the other hand, very few models represent multi-scaling resulting from the eccentricity-dependent RF sizes within each visual layer, also known as the Cortical Magnification effect. In this paper, we demonstrate that using a Cortical-Magnification-like mechanism can lead to performant alternatives to pyramidal approaches in the context of attentional modeling. Moreover, we argue that introducing such a mechanism equips the proposed model with additional properties related to overt attention and distance-dependent saliency that are worth exploring.
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ICASSP - A model of bottom-up visual attention using Cortical Magnification
2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015Co-Authors: Ala Aboudib, Vincent Gripon, Gilles CoppinAbstract:The focus of visual attention has been argued to play a key role in object recognition. Many computational models of visual attention were proposed to estimate locations of eye fixations driven by bottom-up stimuli. Most of these models rely on pyramids consisting of multiple scaled versions of the visual scene. This design aims at capturing the fact that neural cells in higher visual areas tend to have larger receptive fields (RFs). On the other hand, very few models represent multi-scaling resulting from the eccentricity-dependent RF sizes within each visual layer, also known as the Cortical Magnification effect. In this paper, we demonstrate that using a Cortical-Magnification-like mechanism can lead to performant alternatives to pyramidal approaches in the context of attentional modeling. Moreover, we argue that introducing such a mechanism equips the proposed model with additional properties related to overt attention and distance-dependent saliency that are worth exploring.
Geoffrey M. Boynton - One of the best experts on this subject based on the ideXlab platform.
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Tactile Hyperacuity Thresholds Correlate with Finger Maps in Primary Somatosensory Cortex (S1)
Cerebral Cortex, 2007Co-Authors: Robert Duncan, Geoffrey M. BoyntonAbstract:Behavioral tactile discrimination thresholds were compared with functional magnetic resonance imaging measurements of Cortical finger representations within primary somatosensory cortex (S1) for 10 human subjects to determine whether Cortical Magnification in S1 could account for the variation in tactile hyperacuity thresholds of the fingers. Across 10 subjects, the increase in tactile thresholds from the index finger to the little finger correlated with the decrease in Cortical representation across fingers in S1. Additionally, representations of the fingers within S1, in Brodmann areas 3b and 1, were also correlated with the thresholds. These results suggest that tactile hyperacuity is largely determined by the Cortical representation of the fingers in S1.
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Cortical Magnification within human primary visual cortex correlates with acuity thresholds
Neuron, 2003Co-Authors: Robert O. Duncan, Geoffrey M. BoyntonAbstract:We measured linear Cortical Magnification factors in V1 with fMRI, and we measured visual acuity (Vernier and grating) in the same observers. The Cortical representation of both Vernier and grating acuity thresholds in V1 was found to be roughly constant across all eccentricities. We also found a within-observer correlation between Cortical Magnification and Vernier acuity, further supporting claims that Vernier acuity is limited by Cortical Magnification in V1.
Ben M Harvey - One of the best experts on this subject based on the ideXlab platform.
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radial asymmetries in population receptive field size and Cortical Magnification factor in early visual cortex
NeuroImage, 2018Co-Authors: Maria De Fatima Silva, Serge O Dumoulin, Jan W Brascamp, Sonia Ferreira, Miguel Castelobranco, Ben M HarveyAbstract:Abstract Human visual cortex does not represent the whole visual field with the same detail. Changes in receptive field size, population receptive field (pRF) size and Cortical Magnification factor (CMF) with eccentricity are well established, and associated with changes in visual acuity with eccentricity. Visual acuity also changes across polar angle. However, it remains unclear how RF size, pRF size and CMF change across polar angle. Here, we examine differences in pRF size and CMF across polar angle in V1, V2 and V3 using pRF modeling of human fMRI data. In these visual field maps, we find smaller pRFs and larger CMFs in horizontal (left and right) than vertical (upper and lower) visual field quadrants. Differences increase with eccentricity, approximately in proportion to average pRF size and CMF. Similarly, we find larger CMFs in the lower than upper quadrant, and again differences increase with eccentricity. However, pRF size differences between lower and upper quadrants change direction with eccentricity. Finally, we find slightly smaller pRFs in the left than right quadrants of V2 and V3, though this difference is very small, and we find no differences in V1 and no differences in CMF. Moreover, differences in pRF size and CMF vary gradually with polar angle and are not limited to the meridians or visual field map discontinuities. PRF size and CMF differences do not consistently follow patterns of Cortical curvature, despite the link between Cortical curvature and polar angle in V1. Thus, the early human visual cortex has a radially asymmetric representation of the visual field. These asymmetries may underlie consistent reports of asymmetries in perceptual abilities.
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the relationship between Cortical Magnification factor and population receptive field size in human visual cortex constancies in Cortical architecture
The Journal of Neuroscience, 2011Co-Authors: Ben M Harvey, Serge O DumoulinAbstract:Receptive field (RF) sizes and Cortical Magnification factor (CMF) are fundamental organization properties of the visual cortex. At increasing visual eccentricity, RF sizes increase and CMF decreases. A relationship between RF size and CMF suggests constancies in Cortical architecture, as their product, the Cortical representation of an RF (point image), may be constant. Previous animal neurophysiology studies of this question yield conflicting results. Here, we use fMRI to determine the relationship between the population RF (pRF) and CMF in humans. In average and individual data, the product of CMF and pRF size, the population point image, is near constant, decreasing slightly with eccentricity in V1. Interhemisphere and subject variations in CMF, pRF size, and V1 surface area are correlated, and the population point image varies less than these properties. These results suggest a V1 Cortical processing architecture of approximately constant size between humans. Up the visual hierarchy, to V2, V3, hV4, and LO1, the population point image decreases with eccentricity, and both the absolute values and rate of change increase. PRF sizes increase between visual areas and with eccentricity, but when expressed in V1 Cortical surface area (i.e., corticoCortical pRFs), they are constant across eccentricity in V2/V3. Thus, V2/V3, and to some degree hV4, sample from a constant extent of V1. This may explain population point image changes in later areas. Consequently, the constant factor determining pRF size may not be the relationship to the local CMF, but rather pRF sizes and CMFs in visual areas from which the pRF samples.
