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A Bayesian Observer Replicates Convexity Context Effects in Figure–Ground Perception

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For more content, see Multisensory Research and Spatial Vision.

Peterson and Salvagio (2008) demonstrated convexity context effects in figure–ground perception. Subjects shown displays consisting of unfamiliar alternating convex and concave regions identified the convex regions as foreground objects progressively more frequently as the number of regions increased; this occurred only when the concave regions were homogeneously colored. The origins of these effects have been unclear. Here, we present a two-free-parameter Bayesian observer that replicates convexity context effects. The Bayesian observer incorporates two plausible expectations regarding three-dimensional scenes: (1) objects tend to be convex rather than concave, and (2) backgrounds tend (more than foreground objects) to be homogeneously colored. The Bayesian observer estimates the probability that a depicted scene is three-dimensional, and that the convex regions are figures. It responds stochastically by sampling from its posterior distributions. Like human observers, the Bayesian observer shows convexity context effects only for images with homogeneously colored concave regions. With optimal parameter settings, it performs similarly to the average human subject on the four display types tested. We propose that object convexity and background color homogeneity are environmental regularities exploited by human visual perception; vision achieves figure–ground perception by interpreting ambiguous images in light of these and other expected regularities in natural scenes.

Affiliations: 1: 1Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada; 2: 2Department of Psychology and Cognitive Science Program, University of Arizona, Tucson, Arizona, USA


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1. Adams W. J. , Mamassian P. ( 2004). "Bayesian combination of ambiguous shape cues", J. Vision Vol 4, 921929.
2. Angelaki D. E. , Klier E. M. , Snyder L. H. ( 2009). "A vestibular sensation: probabilistic approaches to spatial perception", Neuron Vol 64, 448461.
3. Battaglia P. W. , Kersten D. , Schrater P. R. ( 2011). "How haptic size sensations improve distance perception", PLoS Comput. Biol. Vol 7, e1002080.
4. Burge J. , Fowlkes C. C. , Banks M. S. ( 2010). "Natural-scene statistics predict how the figure–ground cue of convexity affects human depth perception", J. Neurosci. Vol 30, 72697280.
5. Burge J. , Peterson M. A. , Palmer S. E. ( 2005). "Ordinal configural cues combine with metric disparity in depth perception", J. Vision Vol 5, 534542.
6. Deneve S. , Pouget A. ( 2004). "Bayesian multisensory integration and cross-modal spatial links", J. Physiol. Paris Vol 98, 249258.
7. Elder J. H. , Goldberg R. M. ( 2002). "Ecological statistics of Gestalt laws for the perceptual organization of contours", J. Vision Vol 2, 324353.
8. Ernst M. O. ( 2006). "A Bayesian view on multimodal cue integration", in: Human Body Perception From The Inside Out, Knoblich G. , Thornton I. M. , Grosjean M. , Shiffrar M. (Eds), Ch. 6, pp.  105131. Oxford University Press, New York, NY, USA.
9. Fine I. , MacLeod D. I. , Boynton G. M. ( 2003). "Surface segmentation based on the luminance and color statistics of natural scenes", J. Optic. Soc. Amer. A Opt. Image. Sci. Vis. Vol 20, 12831291.
10. Fowlkes C. C. , Martin D. R. , Malik J. ( 2007). "Local figure–ground cues are valid for natural images", J. Vision Vol 7, 19.
11. Geisler W. S. , Diehl R. L. ( 2003). "A Bayesian approach to the evolution of perceptual and cognitive systems", Cognit. Sci. Vol 27, 379402.
12. Gershman S. J. , Vul E. , Tenenbaum J. B. ( 2012). "Multistability and perceptual inference", Neural Comput. Vol 24, 124.
13. Goldreich D. ( 2007). "A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions", PLoS One Vol 2, e333.
14. Hulleman J. , Humphreys G. W. ( 2004). "A new cue to figure–ground coding: top–bottom polarity", Vision Research Vol 44, 27792791.
15. Ing A. D. , Wilson J. A. , Geisler W. S. ( 2010). "Region grouping in natural foliage scenes: image statistics and human performance", J. Vision Vol 10, 1119.
16. Kanizsa G. , Gerbino W. ( 1976). "Convexity and symmetry in figure–ground organization", in: Vision and Artifact, Henle M. (Ed.), pp.  2532. Springer, New York, NY, USA.
17. Kennedy J. M. ( 1974). A Psychology of Picture Perception. Jossey-Bass, San Francisco, USA.
18. Knill D. C. , Kersten D. , Mamassian P. ( 1996). "Implications of a Bayesian formulation of visual information for processing for psychophysics", in: Perception as Bayesian Inference, Knill D. C. , Richards W. (Eds), pp.  239286, Cambridge University Press, New York, NY, USA.
19. Kording K. P. , Ku S. P. , Wolpert D. M. ( 2004). "Bayesian integration in force estimation", J. Neurophysiol. Vol 92, 31613165.
20. Langer M. S. , Bülthoff H. H. ( 2001). "A prior for global convexity in local shape-from-shading", Perception Vol 30, 403410.
21. Ma W. J. , Pouget A. ( 2008). "Linking neurons to behavior in multisensory perception: a computational review", Brain Research Vol 1242, 412.
22. Mamassian P. ( 2006). "Bayesian inference of form and shape", Prog. Brain. Res. Vol 154, 265270.
23. Mamassian P. , Landy M. S. ( 1998). "Observer biases in the 3D interpretation of line drawings", Vision Research Vol 38, 28172832.
24. Mamassian P. , Landy M. , Maloney L. T. ( 2002). "Bayesian modelling of visual perception", in: Probabilistic Models of the Brain: Perception and Neural Function, Rao R. P. N. , Olshausen B. A. , Lewicki M. S. (Eds), pp.  1336. MIT Press, Cambridge, MA, USA.
25. Moreno-Bote R. , Knill D. C. , Pouget A. ( 2011). "Bayesian sampling in visual perception", Proc. Natl Acad. Sci. USA Vol 108, 1249112496.
26. Norris D. , Mcqueen J. M. ( 2008). "Shortlist B: a Bayesian model of continuous speech recognition", Psychol. Rev. Vol 115, 357395.
27. O’Shea J. P. , Agrawala M. , Banks M. S. ( 2010). "The influence of shape cues on the perception of lighting direction", J. Vision Vol 10, 121.
28. Palmer S. E. , Brooks J. L. ( 2008). "Edge-region grouping in figure–ground organization and depth perception", J. Exper. Psychol. Hum. Percept. Perform. Vol 34, 13531371.
29. Palmer S. E. , Ghose T. ( 2008). "Extremal edges: a powerful cue to depth perception and figure–ground organization", Psychol. Sci. Vol 19, 7784.
30. Peterson M. A. ( 2003). "On figures, grounds, and varieties of amodal surface completion", in: Perceptual Organization in Vision: Behavioral and Neural Perspectives, Kimchi R. , Behrmann M. , Olson C. (Eds), pp.  87116. LEA, Mahwah, NJ, USA.
31. Peterson M. A. , Gibson B. S. ( 1994). "Must figure–ground organization precede object recognition? An assumption in peril", Psychol. Sci. Vol 5, 253259.
32. Peterson M. A. , Salvagio E. ( 2008). "Inhibitory competition in figure–ground perception: context and convexity", J. Vision Vol 8, 113.
33. Peterson M. A. , Harvey E. H. , Weidenbacher H. L. ( 1991). "Shape recognition input to figure–ground organization: which route counts?" J. Exper. Psychol. Hum. Percept. Perform. Vol 17, 10751089.
34. Pizlo Z. ( 2001). "Perception viewed as an inverse problem", Vision Research Vol 41, 31453161.
35. Vecera S. P. , Vogel E. K. , Woodman G. F. ( 2002). "Lower-region: a new cue for figure–ground assignment", J. Exper. Psychol. Gen. Vol 131, 194205.
36. Vul E. , Hanus D. , Kanwisher N. ( 2009). "Attention as inference: selection is probabilistic; responses are all-or-none samples", J. Exper. Psychol. Gen. Vol 138, 546560.
37. Wozny D. R. , Beierholm U. R. , Shams L. ( 2010). "Probability matching as a computational strategy used in perception", PLoS Comput. Biol. Vol 6, e1000871.

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