Natural selection and veridical perceptions

Mark, Justin T.; Marion, Brian B.; Hoffman, Donald D.

doi:10.1016/j.jtbi.2010.07.020

Cited by 78 publications

(84 citation statements)

References 22 publications

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“…There is no reason to suppose that vision depends on feature detection, neural representation or probabilistic (Bayesian) inferences; the operational success of randomly varying neural circuitry that is retained or not retained seems sufficient to contend with the fundamental problem of behaving in the world whose physical nature is effectively unknowable (1-4; also ref. 23). …”

Section: Discussionmentioning

confidence: 99%

Properties of artificial networks evolved to contend with natural spectra

Morgenstern

Rostami

Purves

2014

Proc. Natl. Acad. Sci. U.S.A.

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Understanding why spectra that are physically the same appear different in different contexts (color contrast), whereas spectra that are physically different appear similar (color constancy) presents a major challenge in vision research. Here, we show that the responses of biologically inspired neural networks evolved on the basis of accumulated experience with spectral stimuli automatically generate contrast and constancy. The results imply that these phenomena are signatures of a strategy that biological vision uses to circumvent the inverse optics problem as it pertains to light spectra, and that double-opponent neurons in early-level vision evolve to serve this purpose. This strategy provides a way of understanding the peculiar relationship between the objective world and subjective color experience, as well as rationalizing the relevant visual circuitry without invoking feature detection or image representation.empirical ranking | color vision | perception | simple networks | receptive field T he spectral properties of retinal images conflate illumination, surface reflectance, atmospheric transmittance, and a host of other factors. Nonetheless, biological visual systems routinely generate lightness and color percepts that lead to successful behavior in the real world. Thus, a major question in vision research is how the visual system contends with the fact that the physical parameters of the world are not available in retinal stimuli (the "inverse optics problem").A clue to the possible answer is the phenomenology of lightness and color perception. As shown in Fig. 1, the same contextual information can make regions of an image returning identical spectra to the eye appear different (lightness/color contrast) and regions returning different spectra look similar (lightness/color constancy). Based on psychophysics and analyses of natural images, we have argued that such perceptual effects are signatures of the strategy vision uses to circumvent the inverse problem (1-4). By depending on the frequency of occurrence of biologically determined stimulus patterns, perceptions of lightness and color track reproductive success rather than the physical qualities of objects and conditions in the world, abrogating the need for information about the physical world as such. Here, we show that simulated evolution driven by the frequency of occurrence of spectral stimulus patterns gives rise to the key receptive field characteristic in biological color vision and to responses whose perceptual counterparts are color contrast and constancy. MethodsNetwork. The network and evolutionary paradigm are shown in Fig. 2. The input to the network was the output of 37 single-opponent neurons that responded to equiluminant spectral patterns along the blue-yellow color axis of natural images. Although the choice of blue-yellow input rather than red-green was arbitrary, the color gamut of dichromats is based primarily on this axis (5). The input thus simulates the information provided to cortical neurons by single-opponent blue-yellow b...

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Section: Discussionmentioning

confidence: 99%

Properties of artificial networks evolved to contend with natural spectra

Morgenstern

Rostami

Purves

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…20 Palmer (1999: 23). 21 Ibid are supposed to do is a matter of debate (I will come back to this issue in section 3.4). However, what is important for my present purpose is that this process of relying on assumptions is not infallible after all: sometimes the visual system relies on wrong assumptions, resulting in visual error.…”

Section: Perceptual Error As Erroneous Judgmentmentioning

confidence: 99%

“…43 Orlandi (2014, p. 70). 44 Mark et al (2010). Also, see: Hoffman and Prakash (2014), Singh and Hoffman (2013).…”

Section: Why Do We Need Perceptual Content?mentioning

confidence: 99%

Why do we need perceptual content?

Shahmoradi

2016

Philosophical Psychology

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“…Such model is in line with the signal-processing approach towards image quality where it is generally taken for granted that there always will be a reference (for example, in image coding the original image) and that the impairment of image quality due to coding artifacts is related to the distance between the original and coded image 4,5 . Recently, however, the model of vision as inverse optics striving for veridicality has been challenged by the so-called interface theory of human perception 6,7,8,9 . This theory states that perception is not about accurately reconstructing the physical world but about constructing the properties and categories of an organism's perceptual world.…”

Section: Introductionmentioning

confidence: 99%

“…This theory states that perception is not about accurately reconstructing the physical world but about constructing the properties and categories of an organism's perceptual world. On evolutionary grounds 6,9 , one can argue that these perceptual structures are not intended to accurately match the physical world but, instead, are fast, intentiondriven explorations of the meaningless physical world in preparation of "optically guided potential behavior" 7,8 , thus striving for utility and efficiency, not veridicality. The efficacy of such mind-to-world approach to perception increases by making use of prototypes or templates like memory colors 10 or canonical visual size 11 .…”

Section: Introductionmentioning

confidence: 99%

Box spaces in pictorial space: linear perspective versus templates

Ridder

Pont

2012

SPIE Proceedings

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In the past decades perceptual (or perceived) image quality has been one of the most important criteria for evaluating digitally processed image and video content. With the growing popularity of new media like stereoscopic displays there is a tendency to replace image quality with viewing experience as the ultimate criterion. Adopting such a high-level psychological criterion calls for a rethinking of the premises underlying human judgment. One premise is that perception is about accurately reconstructing the physical world in front of you ("inverse optics"). That is, human vision is striving for veridicality. The present study investigated one of its consequences, namely, that linear perspective will always yield the correct description of the perceived 3D geometry in 2D images. To this end, human observers adjusted the frontal view of a wireframe box on a television screen so as to look equally deep and wide (i.e. to look like a cube) or twice as deep as wide. In a number of stimulus configurations, the results showed huge deviations from veridicality suggesting that the inverse optics model fails. Instead, the results seem to be more in line with a model of "vision as optical interface".

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Natural selection and veridical perceptions

Cited by 78 publications

References 22 publications

Properties of artificial networks evolved to contend with natural spectra

Properties of artificial networks evolved to contend with natural spectra

Why do we need perceptual content?

Box spaces in pictorial space: linear perspective versus templates

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