The statistical structure of natural light patterns determines perceived light intensity

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The perceptual color qualities of hue, saturation, and brightness do not correspond in any simple way to the physical characteristics of retinal stimuli, a fact that poses a major obstacle for any explanation of color vision. Here we test the hypothesis that these basic color attributes are determined by the statistical covariations in the spectral stimuli that humans have always experienced in typical visual environments. Using a database of 1,600 natural images, we analyzed the joint probability distributions of the physical variables most relevant to each of these perceptual qualities. The cumulative density functions derived from these distributions predict the major colorimetric functions that have been reported in psychophysical experiments over the last century.color ͉ colorimetry ͉ perception ͉ psychophysics ͉ vision C olor percepts are described in terms of hue (the sensation of the relative redness, greenness, blueness, or yellowness of a spectral stimulus), saturation (the degree to which a color percept deviates from neutral gray), and brightness (the apparent intensity of the stimulus). Although color percepts are obviously initiated by the spectral characteristics of light stimuli, a major difficulty rationalizing these color qualities in either neurobiological or psychological terms is that they are not linked in any straightforward way to the physical attributes of the corresponding retinal stimuli (i.e., to the energy distribution in the stimuli, to their relative uniformity, or to their overall power, respectively). Thus varying the physical parameter underlying any one of these perceptual categories changes the appearance of all three qualities in highly nonlinear and interdependent ways (1-8) .These classical psychophysical functions, which are illustrated in the Figs. 1a-6a, can be divided into those measured by color discrimination testing and those revealed in color-matching paradigms. In color discrimination tests, the ability to distinguish equally noticeable (or just noticeable) differences in hue or saturation varies systematically as a function of the wavelength of a monochromatic stimulus (1, 2). In color-matching tests, (i) saturation varies as a function of luminance [the Hunt effect (3) Motivated by evidence that natural image statistics predict the response properties of some visual neurons (12) and that imagesource statistics predict several aspects of visual perception (13-16), we here examine the hypothesis that this perplexing phenomenology arises from a scheme of visual processing based on statistical covariations of the physical characteristics of light stimuli in typical visual environments. To test this possibility, we acquired a database of 1,600 color images of natural scenes to approximate the range of light stimuli that humans have normally witnessed (see Fig. 7, which is published as supporting information on the PNAS web site). The joint probability distributions of the physical correlates most closely associated with hue, saturation, and brightness were then analyz...

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spectral statistics in natural scenes predict hue, saturation, and brightness

Long

Yang

Purves

2006

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“…2 and 3 but also perceptions elicited by a variety of complex luminance patterns (20,27), geometrical patterns (18), spectral patterns (28), and moving stimuli (29,30). In addition, artificial neural networks that evolve on the basis of ranking the frequency of luminance patterns can rationalize major aspects of early-level receptive field properties in experimental animals (31,32).…”

Section: An Example: the Perception Of Lightnessmentioning

confidence: 99%

How biological vision succeeds in the physical world

Purves

Monson

Sundararajan

et al. 2014

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Biological 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.In the 1960s and for the following few decades, it seemed all but certain that the rapidly growing body of information about the electrophysiological and anatomical properties of neurons in the primary visual pathway of experimental animals would reveal how the brain uses retinal stimuli to generate perceptions and appropriate visually guided behaviors (1). However, despite the passage of 50 years, this expectation has not been met. In retrospect, the missing piece is understanding how stimuli that cannot specify the properties of physical sources can nevertheless give rise to generally successful perceptions and behaviors.The problematic relationship between visual stimuli and the physical world was recognized by Ptolemy in the 2nd century, Alhazen in the 11th century, Berkeley in the 18th century, Helmholtz in the 19th century, and many others since (2-12). To explain how accurate perceptions and behaviors could arise from stimuli that cannot specify their sources, Helmholtz, arguably the most influential figure over this history, proposed that observers augmented the information in retinal stimuli by making "unconscious inferences" about the world based on past experience. The idea of vision as inference has been revived in the last two decades using Bayesian decision theory, which posits that the uncertain provenance of retinal images illustrated in Fig.

“…1). Recent investigations of brightness, color, and form have suggested that to contend with this problem in other perceptual domains the visual system has evolved to operate empirically, generating percepts that represent the world in terms of accumulated experience with images and their possible sources rather than by using stimulus features as such (1)(2)(3)(4). Given this evidence, we suspected that the flash-lag effect might be a signature of this visual strategy as it pertains to the perception of motion.…”

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confidence: 99%

An empirical explanation of the flash-lag effect

Wojtach

Sung

Truong

et al. 2008

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When a flash of light is presented in physical alignment with a moving object, the flash is perceived to lag behind the position of the object. This phenomenon, known as the flash-lag effect, has been of particular interest to vision scientists because of the challenge it presents to understanding how the visual system generates perceptions of objects in motion. Although various explanations have been offered, the significance of this effect remains a matter of debate. Here, we show that: (i) contrary to previous reports based on limited data, the flash-lag effect is an increasing nonlinear function of image speed; and (ii) this function is accurately predicted by the frequency of occurrence of image speeds generated by the perspective transformation of moving objects. These results support the conclusion that perceptions of the relative position of a moving object are determined by accumulated experience with image speeds, in this way allowing for visual behavior in response to real-world sources whose speeds and positions cannot be perceived directly.image speed ͉ motion perception ͉ percentile rank ͉ perspective transformation ͉ motion processing T o produce biologically useful perceptions of motion, humans and other animals must contend with the fact that projected images cannot uniquely specify the real world speeds and positions of objects. This quandary-referred to as the inverse optics problem-is a consequence of the transformations that occur when objects in three-dimensional (3D) space project onto a two-dimensional (2D) surface, thus conflating the physical determinants of speed and position in the retinal image ( Fig. 1). Recent investigations of brightness, color, and form have suggested that to contend with this problem in other perceptual domains the visual system has evolved to operate empirically, generating percepts that represent the world in terms of accumulated experience with images and their possible sources rather than by using stimulus features as such (1-4). Given this evidence, we suspected that the flash-lag effect might be a signature of this visual strategy as it pertains to the perception of motion. We therefore examined the hypothesis that the perception of lag is determined by the frequency of occurrence of image speeds generated by moving objects. As explained in Discussion, perceiving speed in this way would allow observers to produce generally successful visually guided responses toward objects whose actual speeds and positions cannot be derived in any direct way from their projected images. Thus, explaining the flash-lag effect is important for understanding vision and visual behavior.To test this hypothesis, the frequency of occurrence of image speeds in relation to their corresponding 3D sources must be known. Since the precise distances, trajectories, and speeds of objects in the world are not simultaneously measurable with any current technology, we created a computer-simulated environment in which the relationship between moving objects and their projections on an image ...