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Models for preattentive texture discrimination: Fourier analysis and local feature processing in a unified framework

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image of Spatial Vision
For more content, see Multisensory Research and Seeing and Perceiving.

—Spatial frequency analysis and local feature analysis may be considered to be examples of a class of models for texture discrimination. In this theoretical framework, texture discrimination relies on differences in the distribution of responses generated in linear receptive fields placed randomly on the texture. If the set of receptive fields is taken to be a collection of gratings, spatial-frequency analysis is recovered. If the set of receptive-fields is taken to be a collection of local feature templates, a corresponding local-feature model is recovered. In order to test such models, it is necessary to construct distinct texture pairs that elicit similar distributions of responses for all of the postulated receptive field profiles: the model prediction is that such textures are not discriminable. A method is provided for construction of such textures which test generic models within this framework. This framework includes not only strict Fourier analysis, but also models which postulate a collection of arbitrarily-shaped local feature detectors, and models which postulate both Fourier analysis and local feature detection.

Affiliations: 1: Department of Neurology, Cornell University Medical College, 1300 York Avenue, New York City, NY 10021, USA, Laboratory of Biophysics, The Rockefeller University, 1230 York Avenue, New York City, NY 10021, USA


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