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A discrete magno–parvo additive model in early vision for explaining brightness perception in varying contrastive contexts

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Abstract

A varying contrastive context filter (VCCF)-based model of brightness perception has been proposed. It is motivated first by a recently proposed difference of difference-of-Gaussian (DDOG) filter. Alongside, it is also inspired from the fact that the nature evolves various discrete systems and mechanisms to carry out many of its complex tasks. A weight factor, used for the linear combination of two filters representing the magnocellular and parvocellular channels in the central visual pathway, has been defined and termed as the factor of contrastive context (FOCC) in the present model. This is a binary variable that lends a property of discretization to the DDOG filter. By analyzing important brightness contrast as well as brightness assimilation illusions, we arrive at the minimal set of values (only two) for FOCC, using which one is able to successfully predict the direction of brightness shift in both situations of brightness contrast, claimed and categorized here as low contrastive context, and those of brightness assimilation, claimed and categorized here as high contrastive context perception, depending upon whether the initial M-channel-filtered stimulus is above or below a threshold of the contrastive context. As distinct from Michelson/Weber/RMS contrast, high or low, the contrastive context claimed is dependent on the edge information in the stimulus determined by the Laplacian operator, also used in the DDOG model. We compared the proposed model against the already well-established oriented difference-of-Gaussian (ODOG) model of brightness perception. Extensive simulations suggest that though for most illusions both ODOG and VCCF produce correct output, for certain intricate cases in which the ODOG filter fails to correctly predict the illusory effect, our proposed VCCF model continues to remain effective.

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Fig. 1

reproduced from Fig. 11(g), Bakshi and Ghosh 2018). The red curve shows the step function discretization of the M channel weights where all values below a threshold of 0.35 are mapped to 0.1 and all values above 0.35 are mapped to 0.55. b The dotted curve shows intensity profile of a sine grating input stimulus and the solid curve shows the corresponding DDOG output. This stimulus corresponds to the leftmost point in Fig. 1(a) where it is pointed out with an arrow. c The intensity profile (dotted line) of an intermediate stimulus lying between sine and square gratings with the corresponding DDOG output (solid curve). This stimulus is also pointed out with an arrow in Fig. 1(a). d The intensity profile (dotted line) of a square grating stimulus with the corresponding DDOG output profile (solid line). This stimulus corresponds to the rightmost point in Fig. 1(a) and is pointed out with an arrow as well

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Data and Code Availability

The data and code are available at https://github.com/ashbakcode/Contrastive-Context-Filter-Code.

References

  • Bakshi A, Ghosh K (2018) A parsimonious model of brightness induction. Biol Cyb 112:237–251

    Article  Google Scholar 

  • Bakshi A, Roy S, Mallick A, Ghosh K (2016) Limitations of the oriented difference of gaussian filter in special cases of brightness perception illusions. Perception 45:328–336

    Article  Google Scholar 

  • Blakeslee B, McCourt ME (1999) A multiscale spatial filtering account of the White effect, simultaneous brightness contrast and grating induction. Vis Res 39:4361–4377

    Article  CAS  Google Scholar 

  • Blakeslee B, McCourt ME (2004) A unified theory of brightness contrast and assimilation incorporating oriented multiscale spatial filtering and contrast normalization. Vis Res 44:2483–2503

    Article  Google Scholar 

  • Blakeslee B, Cope D, McCourt ME (2016) The Oriented Difference of Gaussians (ODOG) model of brightness perception: Overview and executable Mathematica notebooks. Behav Res Meth 48:306–312

    Article  Google Scholar 

  • Breitmeyer BG (2014) Contributions of magno-and parvocellular channels to conscious and non-conscious vision. Philos Transac Royal Soc b: Biol Sci 369(1641):20130213

    Article  Google Scholar 

  • Buracas GT, Boynton GM (2007) The effect of spatial attention on contrast response functions in human visual cortex. J Neurosci 27:93–97

    Article  CAS  Google Scholar 

  • Chen Y, Martinez-Conde S, Macknik SL, Bereshpolova Y, Swadlow HA, Alonso J-M (2008) Task difficulty modulates the activity of specific neuronal populations in primary visual cortex. Nat Neurosci 11:974–982

    Article  CAS  Google Scholar 

  • Dobkins KR, Teller DY (1996) Infant contrast detectors are selective for direction of motion. Vis Res 36:281–294

    Article  CAS  Google Scholar 

  • Heinemann EG (1955) Simultaneous brightness induction as a function of inducing-and test-field luminances. J Exp Psych 50:89–96

    Article  CAS  Google Scholar 

  • Howe PDL (2001) A comment on the Anderson (1997), the Todorovic (1997), and the Ross and Pessoa (2000) explanations of White’s effect. Perception 30:1023–1026

    Article  CAS  Google Scholar 

  • Levinson E, Sekuler R (1975) The independence of channels in human vision selective for direction of movement. J Physiol 250:347–366

    Article  CAS  Google Scholar 

  • Marr D, Hildreth E (1980) Theory of edge detection. Proc Roy Soc Lond B 207:187–217

    Article  CAS  Google Scholar 

  • Mazumdar D, Mitra S, Ghosh K, Bhaumik K (2016) A DOG filter model of the occurrence of Mach bands on spatial contrast discontinuities. Biol Cybern 110(2):229–236

    Article  Google Scholar 

  • McCourt ME (1982) A spatial frequency dependent grating-induction effect. Vis Res 22:119–134

    Article  CAS  Google Scholar 

  • Merigan WH, Maunsell JH (1993) How parallel are the primate visual pathways? Annu Rev Neurosci 16(1):369–402

    Article  CAS  Google Scholar 

  • Pelli DG, Bex P (2013) Measuring contrast sensitivity. Vis Res 90:10–14. https://doi.org/10.1016/j.visres.2013.04.015

    Article  PubMed  Google Scholar 

  • Pooresmaeili A, Poort J, Thiele A, Roelfsema PR (2010) Separable codes for attention and luminance contrast in the primary visual cortex. J Neurosci 30(38):12701–12711

    Article  CAS  Google Scholar 

  • Reynolds JH, Pasternak T, Desimone R (2000) Attention increases sensitivity of V4 neurons. Neuron 26:703–714

    Article  CAS  Google Scholar 

  • Robinson AE, Hammon PS, de Sa VR (2007) Explaining brightness illusions using spatial filtering and local response normalization. Vis Res 47:1631–1644

    Article  Google Scholar 

  • Rodieck RW, Stone J (1965) Analysis of receptive fields of cat retinal ganglion cells. J Neurophysiol 28:833–849

    Article  CAS  Google Scholar 

  • Sawatari A, Callaway EM (1996) Convergence of magno-and parvocellular pathways in layer 4B of macaque primary visual cortex. Nature 380(6573):442–446

    Article  CAS  Google Scholar 

  • Schneider KA (2011) Attention alters decision criteria but not appearance: A reanalysis of Anton-Erxleben, Abrams, and Carrasco (2010). J vis 11(13):7–7

    Article  Google Scholar 

  • Sclar G, Maunsell JHR, Lennie P (1990) Coding of image contrast in central visual pathways of the macaque monkey. Vis Res 30:1–10

    Article  CAS  Google Scholar 

  • Shapiro A, Lu Z-L (2011) Relative brightness in natural images can be accounted for by removing blurry content. Psychol Sci 22:1452–1459

    Article  Google Scholar 

  • Short JRH, Mock VL, Briggs F (2017) Attentional Modulation of neuronal activity depends on neuronal feature selectivity. Curr Biol 27:1878–1887

    Article  Google Scholar 

  • Spehar B, Gilchrist A, Arend L (1997) Qualitative boundaries critical in White’s effect and square-wave brightness induction: a reply to Kingdom et al. (1997). Vis Res 37:1044–1047

    Article  Google Scholar 

  • Thiele A, Distler C, Hoffmann KP (1999) Decision-related activity in the macaque dorsal visual pathway. Eur J Neurosci 11:2044–2058

    Article  CAS  Google Scholar 

  • Thiele A, Dobkins KR, Albright TD (2000) Neural correlates of contrast detection at threshold. Neuron 26:715–724

    Article  CAS  Google Scholar 

  • Thiele A, Pooresmaeili A, Delicato LS, Herrero JL, Roelfsema PR (2009) Additive effects of attention and stimulus contrast in primary visual cortex. Cereb Cortex 19:2970–2981

    Article  Google Scholar 

  • Thiele A, Brandt C, Dasilva M, Gotthardt S, Chicharro D, Panzeri S, Distler C (2016) Attention induced gain stabilization in broad and narrow-spiking cells in the frontal eye-field of macaque monkeys. J Neurosci 36:7601–7612

    Article  CAS  Google Scholar 

  • Watson AB, Thompson PG, Murphy BJ, Nachmias J (1980) Summation and discrimination of gratings moving in opposite directions. Vis Res 20:341–347

    Article  CAS  Google Scholar 

  • White M (1981) The effect of the nature of the surround on the perceived lightness of grey bars within square-wave test gratings. Perception 10:215–230

    Article  CAS  Google Scholar 

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Funding

The authors acknowledge the financial support received from TAC-DCSW-CCSD, ISI (MIU-15-18 18-21), and the Cognitive Science Research Initiative (CSRI/307/2016), Govt. of India. The authors are very grateful to Alan Robinson for providing the MATLAB code of the ODOG filter that has been used in Robinson et al. (2007), and for permitting the use of the same in the present work for the sake of comparison with the proposed VCCF model.

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Authors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Bakshi & Kuntal Ghosh

  2. Jadavpur University, Kolkata, India

    Sourya Roy & Arijit Mallick

Authors
  1. Ashish Bakshi
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  2. Sourya Roy
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  3. Arijit Mallick
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  4. Kuntal Ghosh
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Correspondence to Kuntal Ghosh.

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Communicated by Benjamin Lindner.

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Bakshi, A., Roy, S., Mallick, A. et al. A discrete magno–parvo additive model in early vision for explaining brightness perception in varying contrastive contexts. Biol Cybern 116, 5–21 (2022). https://doi.org/10.1007/s00422-021-00896-4

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