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

1] Center for Neural Science, New York University, New York, New York, USA [2] [3].
Nature Neuroscience (Impact Factor: 16.1). 05/2013; 16(7). DOI: 10.1038/nn.3402
Source: PubMed


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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Available from: Eero P. Simoncelli, Feb 11, 2014
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    • "For example, the periodic activation seen in V3 may reflect periodic output spiking from V2, which was driven by periodical refined readout of V1 neurons. This idea is consistent with a recent finding that V2 neurons are more tuned to natural image statistics than V1[69]. These results are important for the validation and refinement of current models of visual perception. "
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    ABSTRACT: Primate visual systems process natural images in a hierarchical manner: at the early stage, neurons are tuned to local image features, while neurons in high-level areas are tuned to abstract object categories. Standard models of visual processing assume that the transition of tuning from image features to object categories emerges gradually along the visual hierarchy. Direct tests of such models remain difficult due to confounding alteration in low-level image properties when contrasting distinct object categories. When such contrast is performed in a classic functional localizer method, the desired activation in high-level visual areas is typically accompanied with activation in early visual areas. Here we used a novel image-modulation method called SWIFT (semantic wavelet-induced frequency-tagging), a variant of frequency-tagging techniques. Natural images modulated by SWIFT reveal object semantics periodically while keeping low-level properties constant. Using functional magnetic resonance imaging (fMRI), we indeed found that faces and scenes modulated with SWIFT periodically activated the prototypical category-selective areas while they elicited sustained and constant responses in early visual areas. SWIFT and the localizer were selective and specific to a similar extent in activating category-selective areas. Only SWIFT progressively activated the visual pathway from low- to high-level areas, consistent with predictions from standard hierarchical models. We confirmed these results with criterion-free methods, generalizing the validity of our approach and show that it is possible to dissociate neural activation in early and category-selective areas. Our results provide direct evidence for the hierarchical nature of the representation of visual objects along the visual stream and open up future applications of frequency-tagging methods in fMRI.
    Full-text · Article · Dec 2015 · PLoS ONE
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    • "We implemented an isotrigon discrimination task using the crowdsourcing platform Amazon Mechanical Turk (mTurk)[5]. An important secondary aim was to evaluate the suitability of mTurk for visual psychometric studies as very few exist[6].Eccles Institute for Neuroscience, John Curtin School of Medical Research, ANU, Canberra, ACT 0200, Australia Full list of author information is available at the end of the article "

    Full-text · Article · Dec 2015 · BMC Neuroscience
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    • "Now we can begin to ask how to relate cortical neural circuits to those perceptual phenomena. We have found that the average modulation index of V2 neurons for particular texture images is correlated with the ability of human observers to detect the presence of the model statistics in synthetic versions of those images (Freeman et al. 2013a). In the context of a more natural perceptual task, we have obtained preliminary evidence that V2 neurons also provide a better substrate for classifying textures into categories than V1 neurons—as a population , V2 neurons are selective for the properties of particular textures, while being tolerant of image variations that preserve texture identity, such as those that arise during the synthesis procedure (Ziemba et al. 2012). "
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    ABSTRACT: The perception of complex visual patterns emerges from neuronal activity in a cascade of areas in the primate cerebral cortex. We have probed the early stages of this cascade with "naturalistic" texture stimuli designed to capture key statistical features of natural images. Humans can recognize and classify these synthetic images and are insensitive to distortions that do not alter the local values of these statistics. The responses of neurons in the primary visual cortex, V1, are relatively insensitive to the statistical information in these textures. However, in the area immediately downstream, V2, cells respond more vigorously to these stimuli than to matched control stimuli. Humans show blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI responses in V1 and V2) that are consistent with the neuronal measurements in macaque. These fMRI measurements, as well as neurophysiological work by others, show that true natural scenes become a more prominent driving feature of cortex downstream from V2. These results suggest a framework for thinking about how information about elementary visual features is transformed into the specific representations of scenes and objects found in areas higher in the visual pathway. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.
    Full-text · Article · May 2015 · Cold Spring Harbor Symposia on Quantitative Biology
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