A Neural Population Model for Visual Pattern Detection.
Pattern detection is the bedrock of modern vision science. Nearly half a century ago, psychophysicists advocated a quantitative theoretical framework that connected visual pattern detection with its neurophysiological underpinnings. In this theory, neurons in primary visual cortex constitute linear...
| Publicado en: | Psychological Review Vol. 120; no. 3; pp. 472 - 497 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Jul2013
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=89562048&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 89562048 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: Jul2013 vid: 120 iid: 3 pid: 34 pub: American Psychological Association artinfo: ui: 89562048 10.1037/a0033136 ppf: 472 ppct: 25 formats: tig: atl: A Neural Population Model for Visual Pattern Detection. aug: au: Goris, Robbe L. T. Putzeys, Tom Wagemans, Johan Wichman, Felix A. affil: New York University KU Leuven Eberhard Karls Universität Tübingen and Bernstein Center for Computational Neuroscience Tübingen, Max Planck Institute for Intelligent Systems, Tübingen, Germany su: Decision making Pattern perception Neurophysiology Neurons Visual cortex Maximum likelihood statistics sug: subj: Decision making Pattern perception Neurophysiology Neurons Visual cortex Maximum likelihood statistics keyword: extrinsic uncertainty neural population model pattern adaptation summation experiments visual detection extrinsic uncertainty neural population model pattern adaptation summation experiments visual detection ab: Pattern detection is the bedrock of modern vision science. Nearly half a century ago, psychophysicists advocated a quantitative theoretical framework that connected visual pattern detection with its neurophysiological underpinnings. In this theory, neurons in primary visual cortex constitute linear and independent visual channels whose output is linked to choice behavior in detection tasks via simple read-out mechanisms. This model has proven remarkably successful in accounting for threshold vision. It is fundamentally at odds, however, with current knowledge about the neuro-physiological underpinnings of pattern vision. In addition, the principles put forward in the model fail to generalize to suprathreshold vision or perceptual tasks other than detection. We propose an alternative theory of detection in which perceptual decisions develop from maximum-likelihood decoding of a neurophysiologically inspired model of population activity in primary visual cortex. We demonstrate that this theory explains a broad range of classic detection results. With a single set of parameters, our model can account for several summation, adaptation, and uncertainty effects, thereby offering a new theoretical interpretation for the vast psychophysical literature on pattern detection. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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