Abstract
In this paper, we argue in the light of visual perceptual experience in favour of the fact that true contradictions are very much perceivable and happen to be an inherent part of the real world. We first describe the phenomenon of perception of two contrary types of brightness illusions, termed as the brightness–contrast and the brightness assimilation type illusions. Next, we present a model of brightness induction which can envisage the above-mentioned contradictions in visual brightness perception. The proposed model, called DDOG (Difference of Difference of Gaussians) is based on two aspects. First, two Difference of Gaussians (DOG) functions acting in opposition in two complementary channels, Magno & Parvo, in the central visual pathway and second, a two-pass model of attentive vision. Although the Oriented Difference of Gaussian (ODOG) model of Blakeslee et al. (Vis Res 45:607–615, 2005) can already account for most of these types of illusions, our model is significantly simpler, more consistent than ODOG and biologically more plausible as a neurocomputational model for explaining brightness contradictions in the brain.
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Acknowledgments
We express our sincere gratitude to Alan Robinson for providing us with the code of his implementation of the ODOG filter [17, 18] whose outputs have been shown in this article, and Mr. Sourya Roy, Mr. Arijit Mallick and Mr. Debjyoti Bhattacharjee for helping us with experiments and discussions. One of the authors, Ashish Bakshi, would like to thank the Council of Scientific and Industrial Research (CSIR) for providing financial support to him that enabled him to perform the present work.
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Bakshi, A., Ghosh, K. (2015). Perceiving and Modelling Brightness Contradictions Through the Study of Brightness Illusions. In: Beziau, JY., Chakraborty, M., Dutta, S. (eds) New Directions in Paraconsistent Logic. Springer Proceedings in Mathematics & Statistics, vol 152. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2719-9_20
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DOI: https://doi.org/10.1007/978-81-322-2719-9_20
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