Probabilistic combination of slant information: Weighted averaging and robustness as optimal percepts

Journal of Vision

Volumn 9, Issue 9, 2009, Pages

  Girshick, Ahna R ^a,c   Banks, Martin S ^b  

^a NEW YORK UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c NONE   (United States)

Author keywords

Cue combination; Heavy tailed estimators; Robust estimation; Sensory fusion; Slant estimation; Visual perception

Indexed keywords

ARTICLE; BAYES THEOREM; BINOCULAR VISION; BIOLOGICAL MODEL; DEPTH PERCEPTION; DIFFERENTIAL THRESHOLD; HUMAN; METHODOLOGY; NORMAL DISTRIBUTION; PHOTOSTIMULATION; PHYSIOLOGY; PROBABILITY; THREE DIMENSIONAL IMAGING;

BAYES THEOREM; DEPTH PERCEPTION; DIFFERENTIAL THRESHOLD; HUMANS; IMAGING, THREE-DIMENSIONAL; MODELS, NEUROLOGICAL; NORMAL DISTRIBUTION; PHOTIC STIMULATION; PROBABILITY; VISION, BINOCULAR;

EID: 69249149996     PISSN: None     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/9.9.8     Document Type: Article

References (52)

1. Alais, D., & Burr, D. (2004). The ventriloquist effect results from near-optimal bimodal integration. Current Biology, 14, 257-262.
2. Arnold, R. D., & Binford, T. O. (1980). Geometric constraints in stereo vision. SPIE: Image Processing for Missile Guidance, 238, 281-292.
3. Backus, B. T., Banks, M. S., van Ee, R., & Crowell, J. A. (1999). Horizontal and vertical disparity, eye position, and stereoscopic slant perception. Vision Research, 39, 1143-1170.
4. Banks, M. S., & Backus, B. T. (1998). Extra-retinal and perspective cues cause the small range of the induced effect. Vision Research, 38, 187194.
5. Box, G. E. P., & Tiao, G. C. (1992). Bayesian inference in statistical analysis. Wiley Classics Library edition. New York: John Wiley & Sons.
6. Bresciani, J. P., Dammeier, F., & Ernst, M. O. (2006). Vision and touch are automatically integrated for the perception of sequences of events. Journal of Vision, 6(5):2, 554-564, http://journalofvision.org/6/5/2/, doi:10.1167/6.5.2.
7. Bülthoff, H. H., & Mallot, H. A. (1988). Integration of depth modules: Stereo and shading. Journal of the Optical Society of America A, Optics and Image Science, 5, 1749-1758.
8. Burnham, K. P., & Anderson, D. (2002). Model selection and multi-model inference: A practical-theoretical approach (2nd ed.). New York: Springer.
9. de Berg, M., van Kreveld, M., Overmars, M., & Schwarzkopf, O. (2000). Computational geometry: Algorithms and applications (2nd ed.). New York: Springer-Verlag.
10. Ernst, M. O. (2005). A Bayesian view on multimodal cue integration. In G. Knoblich, I. M. Thornton, M. Grosjean, & M. Shiffrar (Eds.), Perception of the human body from the inside out (pp. 105-131). New York: Oxford University Press.
11. Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415, 429-433.
12. Gårding, J., Porrill, J., Mayhew, J. E. W., & Frisby, J. P. (1995). Stereopsis, vertical disparity and relief transformations. Vision Research, 35, 703-722.
13. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis (2nd ed.). London: Chapman & Hall.
14. Gepshtein, S., & Banks, M. S. (2003). Viewing geometry determines how vision and haptics combine in size perception. Current Biology, 13, 6, 483-488.
15. Ghahramani, Z., Wolpert, D. M., & Jordan, M. I. (1997). Computational models of sensorimotor integration. In P.G. Morasso & V. Sanguineti (Eds.), Self-organization, computational maps and motor control. Amsterdam: Elsevier Press.
16. Girshick, A. R. (2007). Probabilistic integration of sensory information for 3D visual surface slant perception. Ph.D. thesis, Berkeley: University of California.
17. Green, D. M., & Swets, J. A. (1974). Signal detection theory and psychophysics. New York: Robert E. Krieger.
18. Hafter, E. R., & Carrier, S. C. (1972). Binaural interaction in low-frequency stimuli: The inability to trade time and intensity completely. Journal of the Acoustical Society of America, 51, 1852-1862.
19. Hay, J., & Pick, H. L., Jr. (1966). Visual and proprioceptive adaptation to optical displacement of the visual stimulus. Journal of Experimental Psychology, 71, 150-158.
20. Hillis, J. M., & Banks, M. S. (2001). Are corresponding points fixed? Vision Research, 41, 2457-2473.
21. Hillis, J. M., Ernst, M. O., Banks, M. S., & Landy, M. S. (2002). Combining sensory information: Mandatory fusion within, but not between, senses. Science, 298, 1627-1630.
22. Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S. (2004). Slant from texture and disparity cues: Optimal cue combination. Journal of Vision, 4(12):1, 967-992, http://journalofvision.org/ 4/12/1/, doi:10.1167/4.12.1.
23. Howard, I. P., & Templeton, W. B. (1966). Human spatial orientation. New York: Wiley.
24. Huber, P. J. (1981). Robust statistics. New York: Wiley.
25. Jacobs, R. A. (1999). Optimal integration of texture and motion cues to depth. Vision Research, 39, 3621-3629.
26. Knill, D. C. (2003). Mixture models and the probabilistic structure of depth cues. Vision Research, 43, 831-854.
27. Knill, D. C. (2007). Robust cue integration: A Bayesian model and evidence from cue-conflict studies with stereoscopic and figure cues to slant. Journal of Vision, 7(7):5, 1-24, http://journalofvision.org/7/7/5/, doi:10.1167/7.7.5.
28. Knill, D. C., Kersten, D., & Yuille, A. (1996). A Bayesian formulation of visual perception. In D. C. Knill & W. Richards (Eds.), Perception as Bayesian inference (pp. 1-21). New York: Cambridge University Press.
29. Knill, D. C., & Saunders, J. A. (2003). Do humans optimally integrate stereo and texture information for judgments of surface slant? Vision Research, 43, 2539-2558.
30. Körding, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., & Shams, L. (2007). Causal inference in multisensory perception. PLoS ONE, 2, e943.
31. Landy, M. S., Maloney, L. T., Johnston, E. B., & Young, M. J. (1995). Measurement and modeling of depth cue combination: In defense of weak fusion. Vision Research, 35, 389-412.
32. Mamassian, P., & Landy, M. S. (1998). Observer biases in the 3D interpretation of line drawings. Vision Research, 38, 2817-2832.
33. McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: John Wiley & Sons.
34. Natarajan, R., Murray, I., Shams, L., & Zemel, R. (2009). Characterizing response behavior in multisensory perception with conflicting cues. In Advances in Neural Information Processing Systems 21. Cambridge, MA: MIT Press.
35. Necker, L. A. (1832). Observations on some remarkable phenomena seen in Switzerland, and an optical phenomenon which occurs on viewing of a crystal or geometrical solid. Philosophical Magazine, 1, 329-337.
36. Ogle, K. N. (1938). Induced size effect: I. A new phenomenon in binocular space-perception associated with the relative sizes of the images of the two eyes. Archives of Ophthalmology, 20, 604-623.
37. Oruç, I., Maloney, L. T., & Landy, M. S. (2003). Weighted linear cue combination with possibly correlated error. Vision Research, 43, 2451-2468.
38. Pick, H. L., Warren, D. H., & Hay, J. C. (1969). Sensory conflict in judgments of spatial direction. Perception & Psychophysics, 6, 203-205.
39. Porrill, J., Frisby, J. P., Adams, W. J., & Buckley, D. (1999). Robust and optimal use of information in stereo vision. Nature, 397, 63-66.
40. Roach, N. W., Heron, J., & McGraw, P. V. (2006). Resolving multisensory conflict: A strategy for balancing the costs and benefits of audio-visual integration. Proceedings of the Royal Society of London B: Biological Sciences, 273, 2159-2168.
41. Rock, L., & Victor, J. (1964). Vision and touch: An experimentally created conflict between the two senses. Science, 143, 594-596.
42. Sato, Y., Toyoizumi, T., & Aihara, K. (2007). Bayesian inference explains perception of unity and ventriloquism aftereffect: Identification of common sources of audiovisual stimuli. Neural Computation, 19, 3335-3355.
43. Shams, L., Ma, W. J., & Beierholm, U. (2005). Sound-induced flash illusion as an optimal percept. Neuroreport, 16, 1923-1927.
44. Stocker, A. A., & Simoncelli, E. P. (2006). Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience, 9, 578-585.
45. van Ee, R., & Erkelens, C. J. (1998). Temporal aspects of stereoscopic slant estimation: An evaluation and extension of Howard and Kaneko's theory. Vision Research, 38, 3871-3882.
46. van Ee, R., van Dam, L. C. J., & Erkelens, C. J. (2002). Bi-stability in perceived slant when binocular disparity and monocular perspective specify different slants. Journal of Vision, 2(9):2, 597-607, http://journalofvision.org/2/9/2/, doi:10.1167/2.9.2.
47. Wade, N. J., & Hughes, P. (1999). Fooling the eyes: Trompe l'œil and reverse perspective. Perception, 28, 1115-1119.
48. Wallace, M. T., Roberson, G. E., Hairston, W. D., Stein, B. E., Vaughan, J. W., & Schirillo, J. A. (2004). Unifying multisensory signals across time and space. Experimental Brain Research, 158, 252-258.
49. Watt, S. J., Akeley, K., Ernst, M. O., & Banks, M. S. (2005). Focus cues affect perceived depth. Journal of Vision, 5(10):7, 834-862, http://journalofvision.org/5/10/7/, doi:10.1167/5.10.7.
50. Wichmann, F. A., & Hill, N. J. (2001a). The psychometric function: I. Fitting, sampling, and goodness of fit. Perception & Psychophysics, 63, 1293-1313.
51. Wichmann, F. A., & Hill, N. J. (2001b). The psychometric function: II. Bootstrap-based confidence intervals and sampling. Perception & Psychophysics, 63, 1314-1329.
52. Yuille, A. L., & Bülthoff, H. H. (1996). Bayesian decision theory and psychophysics. In D. C. Knill & W. Richards (Eds.), Perception as Bayesian inference (pp. 123-161). New York: Cambridge University Press.