Please use this identifier to cite or link to this item: https://doi.org/10.1073/pnas.1311309111
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dc.titleHow biological vision succeeds in the physical world
dc.contributor.authorPurves, D.
dc.contributor.authorMonson, B.B.
dc.contributor.authorSundararajan, J.
dc.contributor.authorWojtach, W.T.
dc.date.accessioned2016-09-06T03:01:36Z
dc.date.available2016-09-06T03:01:36Z
dc.date.issued2014-04-01
dc.identifier.citationPurves, D., Monson, B.B., Sundararajan, J., Wojtach, W.T. (2014-04-01). How biological vision succeeds in the physical world. Proceedings of the National Academy of Sciences of the United States of America 111 (13) : 4750-4755. ScholarBank@NUS Repository. https://doi.org/10.1073/pnas.1311309111
dc.identifier.issn10916490
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/126584
dc.description.abstractBiological visual systems cannot measure the properties that define the physical world. Nonetheless, visually guided behaviors of humans and other animals are routinely successful.The purpose of this article is to consider how this feat is accomplished. Most concepts of vision propose, explicitly or implicitly, that visual behavior depends on recovering the sources of stimulus features either directly or by a process of statistical inference. Here we argue that, given the inability of the visual system to access the properties of the world, these conceptual frameworks cannot account for the behavioral success of biological vision. The alternative we present is that the visual system links the frequency of occurrence of biologically determined stimuli to useful perceptual and behavioral responses without recovering real-world properties. The evidence for this interpretation of vision is that the frequency of occurrence of stimulus patterns predicts many basic aspects of what we actually see. This strategy provides a different way of conceiving the relationship between objective reality and subjective experience, and offers a way to understand the operating principles of visual circuitry without invoking feature detection, representation, or probabilistic inference.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1073/pnas.1311309111
dc.sourceScopus
dc.typeReview
dc.contributor.departmentDUKE-NUS GRADUATE MEDICAL SCHOOL S'PORE
dc.description.doi10.1073/pnas.1311309111
dc.description.sourcetitleProceedings of the National Academy of Sciences of the United States of America
dc.description.volume111
dc.description.issue13
dc.description.page4750-4755
dc.description.codenPNASA
dc.identifier.isiut000333579700030
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