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Illusions, perception and Bayes

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A new model shows that a range of visual illusions in humans can be explained as rational inferences about the odds that a motion stimulus on the retina results from a particular real-world source.

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Figure 1: Bayesian ideal observers for tasks involving the perception of objects or events that differ along two physical dimensions, such as aspect ratio and slant, size and distance, or speed and direction of motion.

References

  1. Weiss, Y., Simoncelli, E. & Adelson, E.H. Nat. Neurosci. 5, 598–604 (2002).

    Article  CAS  Google Scholar 

  2. Freeman, W.T. Nature 368, 542–545 (1994).

    Article  CAS  Google Scholar 

  3. Knill, D.C., Kersten, D. & Yuille, A. in Perception as Bayesian Inference (eds. Knill, D. C. & Richards, R. W.) 1–21 (Cambridge Univ. Press, 1996).

    Google Scholar 

  4. Yuille, A.L. & Bülthoff, H.H. in Perception as Bayesian Inference (eds. Knill, D. C. & Richards, R. W.) 123–161 (Cambridge Univ. Press, 1996).

    Book  Google Scholar 

  5. Kersten, D. in The New Cognitive Neurosciences. 2nd Edn. (ed. Gazzaniga, M. S.) 353–363 (MIT Press, Cambridge, Massachusetts, 1999).

    Google Scholar 

  6. Geisler, W. Psychol. Rev. 96, 267–314 (1989).

    Article  CAS  Google Scholar 

  7. Liu, Z. & Kersten, D. Vision Res. 38, 2507–2519 (1998).

    Article  CAS  Google Scholar 

  8. Bloj, M.G., Kersten, D. & Hurlbert, A.C. Nature 402, 877–879 (1999).

    Article  CAS  Google Scholar 

  9. Saunders, J.A. & Knill, D.C. Vision Res. 41, 3163–3183 (2001).

    Article  CAS  Google Scholar 

  10. Mamassian, P., Landy, M.S. & Maloney, L.T. in Statistical Theories of the Brain (eds. Rao, R., Olshausen, B. & Lewicki, M.) 13–36 (MIT Press, Cambridge, Massachusetts, 2002).

    Google Scholar 

  11. Ernst, M.O. & Banks, M.S. Nature 415, 429–433 (2002).

    Article  CAS  Google Scholar 

  12. Geisler, W.S., Perry, J.S., Super, B.J. & Gallogly, D.P. Vision Res. 41, 711–724 (2001).

    Article  CAS  Google Scholar 

  13. Brainard, D.H. & Freeman, W.T. J. Opt. Soc. Am. A 14, 1393–1411 (1997).

    Article  CAS  Google Scholar 

  14. Schrater, P.R. & Kersten, D. How optimal depth cue integration depends on the task. Int. J. Comput. Vision 40, 73–91 (2000).

    Article  Google Scholar 

  15. Geisler, W.S. & Diehl, R. Phil. Trans. R. Soc. Lond. B Biol. Sci. 357, 419–448 (2002).

    Article  Google Scholar 

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Authors and Affiliations

  1. Center for Perceptual Systems, University of Texas Austin, Seay Psychology Building, Austin, 78712-7789, Texas, USA

    Wilson S. Geisler

  2. Psychology Dept., University of Minnesota, 75 E. River Rd., Minneapolis, 55455, Minnesota, USA

    Daniel Kersten

Authors
  1. Wilson S. Geisler
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  2. Daniel Kersten
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Geisler, W., Kersten, D. Illusions, perception and Bayes. Nat Neurosci 5, 508–510 (2002). https://doi.org/10.1038/nn0602-508

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