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      A functional and perceptual signature of the second visual area in primates

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          Abstract

          There is no generally accepted account of the function of the second visual cortical area (V2), partly because no simple response properties robustly distinguish V2 neurons from those in primary visual cortex (V1). We constructed synthetic stimuli replicating the higher-order statistical dependencies found in natural texture images, and used them to stimulate macaque V1 and V2 neurons. Most V2 cells responded more vigorously to these textures than to control stimuli lacking naturalistic structure; V1 cells did not. fMRI measurements in humans revealed differences between V1 and V2 that paralleled the neuronal measurements. The ability of human observers to detect naturalistic structure in different types of texture was well predicted by the strength of neuronal and fMRI responses in V2 but not in V1. Together, these results reveal a novel and particular role for V2 in the representation of natural image structure.

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          Most cited references51

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          Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex.

          The stages of integration leading from local feature analysis to object recognition were explored in human visual cortex by using the technique of functional magnetic resonance imaging. Here we report evidence for object-related activation. Such activation was located at the lateral-posterior aspect of the occipital lobe, just abutting the posterior aspect of the motion-sensitive area MT/V5, in a region termed the lateral occipital complex (LO). LO showed preferential activation to images of objects, compared to a wide range of texture patterns. This activation was not caused by a global difference in the Fourier spatial frequency content of objects versus texture images, since object images produced enhanced LO activation compared to textures matched in power spectra but randomized in phase. The preferential activation to objects also could not be explained by different patterns of eye movements: similar levels of activation were observed when subjects fixated on the objects and when they scanned the objects with their eyes. Additional manipulations such as spatial frequency filtering and a 4-fold change in visual size did not affect LO activation. These results suggest that the enhanced responses to objects were not a manifestation of low-level visual processing. A striking demonstration that activity in LO is uniquely correlated to object detectability was produced by the "Lincoln" illusion, in which blurring of objects digitized into large blocks paradoxically increases their recognizability. Such blurring led to significant enhancement of LO activation. Despite the preferential activation to objects, LO did not seem to be involved in the final, "semantic," stages of the recognition process. Thus, objects varying widely in their recognizability (e.g., famous faces, common objects, and unfamiliar three-dimensional abstract sculptures) activated it to a similar degree. These results are thus evidence for an intermediate link in the chain of processing stages leading to object recognition in human visual cortex.
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            Metamers of the ventral stream

            The human capacity to recognize complex visual patterns emerges in a sequence of brain areas known as the ventral stream, beginning with primary visual cortex (V1). We develop a population model for mid-ventral processing, in which non-linear combinations of V1 responses are averaged within receptive fields that grow with eccentricity. To test the model, we generate novel forms of visual metamers — stimuli that differ physically, but look the same. We develop a behavioral protocol that uses metameric stimuli to estimate the receptive field sizes in which the model features are represented. Because receptive field sizes change along the ventral stream, the behavioral results can identify the visual area corresponding to the representation. Measurements in human observers implicate V2, providing a new functional account of this area. The model explains deficits of peripheral vision known as “crowding”, and provides a quantitative framework for assessing the capabilities of everyday vision.
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              Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm

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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                15 May 2013
                19 May 2013
                July 2013
                19 November 2013
                : 16
                : 7
                : 974-981
                Affiliations
                Center for Neural Science, Department of Psychology, and Howard Hughes Medical Institute, New York University, New York, NY 10003, USA.
                Author notes
                [*]

                These authors contributed equally to this work

                Correspondence: Anthony Movshon Center for Neural Science New York University 4 Washington Place, Room 809 New York, NY 10003 USA +1 212 998 7880 movshon@ 123456nyu.edu
                Article
                NIHMS470224
                10.1038/nn.3402
                3710454
                23685719
                6f72beb8-5eac-4091-be29-09952c720a8e

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                History
                Funding
                Funded by: National Eye Institute : NEI
                Award ID: R01 EY004440 || EY
                Funded by: Howard Hughes Medical Institute :
                Award ID: || HHMI_
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                Neurosciences
                Neurosciences

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