Method of quadratic approximation: A new approach to identification of analysers and channels in human vision

Logvinenko, Alexander D.

doi:10.1016/s0022-2496(03)00048-8

Cited by 4 publications

(3 citation statements)

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“…Such a finding signals that multiple mechanisms interact to determine the salience of the difference between textures. It may sometimes still be possible to infer the sensitivity functions of the underlying mechanisms (Logvinenko 2003); typically, however, the threshold hypersurface is ellipsoidal and hence consistent with a quadratic combination rule (as illustrated in Fig. 2A).…”

Section: A Framework For Textures and Tasksmentioning

confidence: 99%

Textures as Probes of Visual Processing

Victor

Conte

Chubb

2017

Annu. Rev. Vis. Sci.

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Visual textures are a class of stimuli with properties that make them well-suited for addressing general questions about visual function at the levels of behavior and neural mechanism. They have structure across multiple spatial scales, they put the focus on the inferential nature of visual processing, and they help bridge the gap between stimuli that are analytically convenient and the complex, naturalistic stimuli that have the greatest biological relevance. Key questions that are well-suited for analysis via visual textures include the nature and structure of perceptual spaces, modulation of early visual processing by task, and the transformation of sensory stimuli into patterns of population activity that are relevant to perception.

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Section: A Framework For Textures and Tasksmentioning

confidence: 99%

Textures as Probes of Visual Processing

Victor

Conte

Chubb

2017

Annu. Rev. Vis. Sci.

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“…However, as well known, this model is incorrect as far as color discrimination is concerned [46,47]. Undoubtedly, the cone spectral sensitivity functions can be derived from color discrimination data, even if color discrimination is a more complex process than that implied by the peak detector [48]. However, in this case the linear relationship between color-matching functions and cone spectral sensitivity functions will not retain.…”

Section: Discussionmentioning

confidence: 99%

The color cone

Logvinenko

2015

J. Opt. Soc. Am. A

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While the notion of a color cone can be found in writings of Maxwell, Helmholtz, Grassmann, and other scientists of the nineteenth century, it has not been clearly defined as yet. In this paper, the color cone is understood as the set of points in the cone excitation space produced by all possible lights. The spectral curve representing all the monochromatic lights is shown not to entirely belong to the color cone boundary, since its ends turn into the color cone interior. The monochromatic lights represented by the fragment of the spectral curve lying on the color cone boundary make up what is called the effective visible spectrum. The color cone is shown to be a convex hull of the conical surface through the fragment of the spectral curve representing the effective visible spectrum. The effective visible spectrum ends are shown to be determined by the photopigment spectral absorbance being independent of the prereceptor filters (e.g., the spectral transmittance of the lense and macular pigment).

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“…It has turned out that, with very few exceptions (an obvious one is when q=2 in Eq. (1)), the opposite is also true, that is, the postreceptor channels (specifically, the matrix A) can be derived from the shape of the EDS (Logvinenko, 2002). So, the shape of the EDS, particularly its convexity, has an important implication for a methodological strategy at estimating the channels underlying colour discrimination.…”

Section: Introductionmentioning

confidence: 99%