Literature DB >> 15152077

The statistical structure of natural light patterns determines perceived light intensity.

Zhiyong Yang1, Dale Purves.   

Abstract

The same target luminance in different contexts can elicit markedly different perceptions of brightness, a fact that has long puzzled vision scientists. Here we test the proposal that the visual system encodes not luminance as such but rather the statistical relationship of a particular luminance to all possible luminance values experienced in natural contexts during evolution. This statistical conception of vision was validated by using a database of natural scenes in which we could determine the probability distribution functions of co-occurring target and contextual luminance values. The distribution functions obtained in this way predict target brightness in response to a variety of challenging stimuli, thus explaining these otherwise puzzling percepts. That brightness is determined by the statistics of natural light patterns implies that the relevant neural circuitry is specifically organized to generate these probabilistic responses.

Mesh:

Year:  2004        PMID: 15152077      PMCID: PMC437094          DOI: 10.1073/pnas.0402192101

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  32 in total

Review 1.  An anchoring theory of lightness perception.

Authors:  A Gilchrist; C Kossyfidis; F Bonato; T Agostini; J Cataliotti; X Li; B Spehar; V Annan; E Economou
Journal:  Psychol Rev       Date:  1999-10       Impact factor: 8.934

2.  Neural correlates of perceived brightness in the retina, lateral geniculate nucleus, and striate cortex.

Authors:  A F Rossi; M A Paradiso
Journal:  J Neurosci       Date:  1999-07-15       Impact factor: 6.167

3.  A multiscale spatial filtering account of the White effect, simultaneous brightness contrast and grating induction.

Authors:  B Blakeslee; M E McCourt
Journal:  Vision Res       Date:  1999-10       Impact factor: 1.886

4.  T-junctions in inhomogeneous surrounds.

Authors:  T O Melfi; J A Schirillo
Journal:  Vision Res       Date:  2000       Impact factor: 1.886

Review 5.  Natural image statistics and neural representation.

Authors:  E P Simoncelli; B A Olshausen
Journal:  Annu Rev Neurosci       Date:  2001       Impact factor: 12.449

6.  Induction in variants of White's effect: common or separate mechanisms?

Authors:  Branka Spehar; Colin W G Clifford; Tiziano Agostini
Journal:  Perception       Date:  2002       Impact factor: 1.490

7.  Optimal nonlinear codes for the perception of natural colours.

Authors:  T von der Twer; D I MacLeod
Journal:  Network       Date:  2001-08       Impact factor: 1.273

8.  Efficiency and ambiguity in an adaptive neural code.

Authors:  A L Fairhall; G D Lewen; W Bialek; R R de Ruyter Van Steveninck
Journal:  Nature       Date:  2001-08-23       Impact factor: 49.962

9.  A comment on the Anderson (1997), the Todorović (1997), and the Ross and Pessoa (2000) explanations of White's effect.

Authors:  P D Howe
Journal:  Perception       Date:  2001       Impact factor: 1.490

10.  Lightness from contrast: a selective integration model.

Authors:  W D Ross; L Pessoa
Journal:  Percept Psychophys       Date:  2000-08
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  21 in total

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

Authors:  Fuhui Long; Zhiyong Yang; Dale Purves
Journal:  Proc Natl Acad Sci U S A       Date:  2006-04-04       Impact factor: 11.205

2.  An open access option for PNAS.

Authors:  Nicholas R Cozzarelli
Journal:  Proc Natl Acad Sci U S A       Date:  2004-06-08       Impact factor: 11.205

Review 3.  Inhomogeneous surrounds, conflicting frameworks, and the double-anchoring theory of lightness.

Authors:  Paola Bressan
Journal:  Psychon Bull Rev       Date:  2006-02

4.  Perceptual learning depends on perceptual constancy.

Authors:  Patrick Garrigan; Philip J Kellman
Journal:  Proc Natl Acad Sci U S A       Date:  2008-02-04       Impact factor: 11.205

5.  An empirical explanation of the flash-lag effect.

Authors:  William T Wojtach; Kyongje Sung; Sandra Truong; Dale Purves
Journal:  Proc Natl Acad Sci U S A       Date:  2008-10-13       Impact factor: 11.205

Review 6.  Understanding vision in wholly empirical terms.

Authors:  Dale Purves; William T Wojtach; R Beau Lotto
Journal:  Proc Natl Acad Sci U S A       Date:  2011-03-07       Impact factor: 11.205

7.  Properties of artificial networks evolved to contend with natural spectra.

Authors:  Yaniv Morgenstern; Mohammad Rostami; Dale Purves
Journal:  Proc Natl Acad Sci U S A       Date:  2014-07-14       Impact factor: 11.205

8.  How biological vision succeeds in the physical world.

Authors:  Dale Purves; Brian B Monson; Janani Sundararajan; William T Wojtach
Journal:  Proc Natl Acad Sci U S A       Date:  2014-03-17       Impact factor: 11.205

9.  Distributions of observed death tolls govern sensitivity to human fatalities.

Authors:  Christopher Y Olivola; Namika Sagara
Journal:  Proc Natl Acad Sci U S A       Date:  2009-12-15       Impact factor: 11.205

10.  An empirical explanation of the speed-distance effect.

Authors:  William T Wojtach; Kyongje Sung; Dale Purves
Journal:  PLoS One       Date:  2009-08-26       Impact factor: 3.240

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