1/f noise in human color vision: the role of S-cone signals

Abstract

We examine the functional role of S-cone signals on reaction time (RT) variability in human color vision. Stimuli were selected along red-green and blue-yellow cardinal directions and at random directions in the isoluminant plane of the color space. Trial-to-trial RT variability was not statistically independent but correlated across experimental conditions and exhibited 1/f noise spectra with an exponent close to unity in most of the cases. Regarding contrast coding, 1/f noise for random chromatic stimuli at isoluminance was similar to that for achromatic stimuli, thus suggesting that S-cone signals reduce variability of higher order color mechanisms. If we regard spatial coding, the effect of S-cone density in the retina on RT variability was investigated. The magnitude of 1/f noise at 16 min of arc (S-cone free zone) was higher than at 90 min of arc in the blue-yellow channel, and it was similar for the red-green channel. The results suggest that S-cone signals are beneficial and they modulate 1/f noise spectra at postreceptoral stages. The implications related to random multiplicative processes as a possible source of 1/f noise and the optimal information processing in color vision are discussed.

Effect of rod–cone interactions on mesopic visual performance mediated by chromatic and luminance pathways

“…The center-surround receptive field ratio is also lower for cone signaling than for rod, or combined rod and cone signaling in macaque MC ganglion cells under mesopic illumination [7], and so the variability due to noise in the dark surround during cone signaling may be higher. If the presence of a dark surround introduced uncertainty about the position of the stimulus, then the coefficient of variations for our peripheral data should be larger than those determined with photopic, foveated data measured on dark surrounds [65] because positional information is more inaccurate in the peripheral retina than the fovea [66], but the variance is not dissimilar between the peripheral and foveal [65] data. We previously proposed that the principal source of mesopic RT variability is subsequent to the primary visual cortex [27,67]; primate MC cell responses to moderate and high contrast stimuli are highly repeatable [68–70], as are recordings in areas of V1 when eye movements are relatively stable, but under natural viewing conditions that allow eye movements, the variability in the V1 response is in the order of six to tenfold higher [71].…”

Low-frequency correlations (1/f^α type) in paint application of metallic colors

“…These temporal color series are treated as stochastic time signals that can be transformed to the Fourier domain and the power spectral density can be estimated using a low-variance method [8]. A common type of correlations in many stochastic signals follows 1/f α Fourier power spectrum, where f is the frequency and α is the scaling exponent [9][10][11]. When there is no correlation in the color coordinate variations at different time scales, α= 0, the Fourier power spectrum will be flat and all the frequencies have equal probability, i.e., the power spectrum will resemble "white noise".…”

“…When there is no correlation in the color coordinate variations at different time scales, α= 0, the Fourier power spectrum will be flat and all the frequencies have equal probability, i.e., the power spectrum will resemble "white noise". If α= 2, there exits strong long-term correlations and the Fourier power spectrum resembles "Brown noise", i.e., similar to a random walker that follows a traditional one-dimensional Brownian motion over time [10,11]. However, a wide range of phenomena and many mathematical models can obey to a scaling exponent α close to unity known as "flicker noise", "pink noise", "1/f noise" or "1/f scaling" [9][10][11].…”

A theory of power laws in human reaction times: insights from an information-processing approach

“…The probability density function (pdf) is often heavy-tailed and can lead to an asymptotic power-law distribution in the right tail (Holden et al, 2009; Moscoso del Prado Martín, 2009; Sigman et al, 2010). (2) RT variability (e.g., variance) is not bounded and usually shows a power relation with the mean, with an exponent β close to unity (Luce, 1986; Wagenmakers and Brown, 2007; Holden et al, 2009; Medina and Díaz, 2011, 2012). This relationship is a manifestation of Taylor's law (also called “fluctuation scaling”) (Taylor, 1961; Eisler et al, 2008), although departures from power law have been reported (Eisler et al, 2008; Schmiedek et al, 2009).…”