Event Abstract Back to Event Metamers of the ventral stream Jeremy Freeman1* and Eero P. Simoncelli2 1 New York University , Center for Neural Science, United States 2 New York University , United States How is image structure encoded in the extrastriate ventral visual pathway? Direct characterization of the stimulus selectivity of individual extrastriate cells has proven difficult. However, one robust population-level property of all visual areas is that receptive field sizes grow with eccentricity. It has also been reported (Gattass et al., 1988) that the rate of growth increases along the ventral stream. We hypothesize that this successive increase in pooling region size causes information loss. A well known example occurs in the retina, where spatial pooling in the periphery means that high spatial frequency information is lost. In general, stimuli that differ only in terms of information discarded by the visual system will be indistinguishable to a human observer. Such stimuli are called metamers. Here, we probe the population-level computations of the ventral stream using novel metameric stimuli. Starting from any prototype image, we generate stimuli that match in terms of the responses of a simple model for extrastriate ventral computation. The model is based on measurements previously used to characterize visual texture (Portilla & Simoncelli, 2000). The model decomposes an image using a bank of V1-like filters tuned for local orientation and spatial frequency, computing both simple and complex-cell responses. Extrastriate responses are then computed by taking pairwise products amongst these V1 responses, and averaging within overlapping spatial regions that grow with eccentricity. Stimuli are generated by using gradient descent to adjust a random (white noise) image to match the model responses of the original prototype. Previous work showed that the same statistics, averaged over an entire image, allow for the analysis and synthesis of homogenous visual textures. If this model accurately reflects representations in early extrastriate areas, then images synthesized to produce identical model responses should be metameric to a human observer. For each of several natural images and pooling region sizes, we generate multiple samples that are statistically-matched but otherwise as random as possible. We use a standard psychophysical task to measure observers’ ability to discriminate between image samples, as a function of the rate at which the statistical pooling regions grow with eccentricity. When image samples are statistically matched within small pooling regions, observers perform at chance (50%), failing to notice substantial differences in the periphery. When images are matched within larger pooling regions, discriminability approaches 100%. We fit the psychometric function to estimate the pooling region over which the observer estimates statistics. The result is consistent with receptive field sizes in macaque mid-ventral areas (particularly V2). Our model also fully instantiates a recently proposed explanation (Balas et al., 2009) of the phenomenon of "visual crowding", in which humans fail to recognize a peripheral target object surrounded by background clutter. In our model, crowding occurs because multiple objects fall within the same pooling region and the model responses cannot uniquely identify the target object. We synthesize images that are metameric to classic crowding stimuli (e.g. groups of letters), and find that stimulus configurations that produce crowding yield synthesized images with jumbled, unidentifiable objects. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Oral Presentation Topic: Oral presentations Citation: Freeman J and Simoncelli EP (2010). Metamers of the ventral stream. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00053 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 18 Feb 2010; Published Online: 18 Feb 2010. * Correspondence: Jeremy Freeman, New York University, Center for Neural Science, New York, United States, jeremy@carbonplan.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jeremy Freeman Eero P Simoncelli Google Jeremy Freeman Eero P Simoncelli Google Scholar Jeremy Freeman Eero P Simoncelli PubMed Jeremy Freeman Eero P Simoncelli Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.
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