Multistability and perceptual inference

Neural Computation

Volumn 24, Issue 1, 2012, Pages 1-24

  Gershman, Samuel J ^a   Vul, Edward ^b   Tenenbaum, Joshua B ^c  

^a PRINCETON UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOLOGICAL MODEL; BRAIN; HUMAN; MONTE CARLO METHOD; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY; PROBABILITY; SIGNAL PROCESSING; UNCERTAINTY; VISION;

ALGORITHMS; BAYES THEOREM; BRAIN; HUMANS; MARKOV CHAINS; MODELS, NEUROLOGICAL; MONTE CARLO METHOD; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONS; SIGNAL PROCESSING, COMPUTER-ASSISTED; UNCERTAINTY; VISUAL PERCEPTION;

EID: 83455200259     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00226     Document Type: Article

References (72)

1. Ackley, D., Hinton, G., & Sejnowski, T. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9(1), 147-169.
2. Alais, D.,&Blake, R. (1999).Grouping visual features during binocular rivalry. Vision Research, 39(26), 4341-4353.
3. Alais, D., Cass, J., O'Shea, R.,&Blake, R. (2010).Visual sensitivity underlying changes in visual consciousness. Current Biology, 20, 1362-1367.
4. Amit, D. (1989). Modeling brain function: The world of attractor neural networks. Cambridge: Cambridge University Press.
5. Bialek, W., & DeWeese, M. (1995). Random switching and optimal processing in the perception of ambiguous signals. Physical Review Letters, 74(15), 3077-3080.
6. Blake, R. (2001).Aprimer on binocular rivalry, including current controversies. Brain and Mind, 2(1), 5-38.
7. Blake, R., & Logothetis, N. (2002). Visual competition. Nature Reviews Neuroscience, 3(1), 13-21.
8. Blake, R., O'Shea, R. P., & Mueller, T. J. (1992). Spatial zones of binocular rivalry in central and peripheral vision. Visual Neuroscience, 8, 469-478.
9. Blake, R., Westendorf, D.,&Overton, R. (1980).What is suppressed during binocular rivalry? Perception, 9(2), 223-231.
10. Brainard, D., & Freeman, W. (1997). Bayesian color constancy. Journal of the Optical Society of America A, 14(7), 1393-1411.
11. Brascamp, J., van Ee, R., Noest, A., Jacobs, R., & van den Berg, A. (2006). The time course of binocular rivalry reveals a fundamental role of noise. Journal of Vision, 6(11), 8.
12. Burke, D., Alais, D., &Wenderoth, P. (1999). Determinants of fusion of dichoptically presented orthogonal gratings. Perception, 28, 73-88.
13. Carandini, M., Demb, J., Mante, V., Tolhurst, D., Dan, Y., Olshausen, B., et al. (2005). Do we know what the early visual system does? Journal of Neuroscience, 25(46), 10577-10587.
14. Das, A., & Gilbert, C. (1999). Topography of contextual modulations mediated by short-range interactions in primary visual cortex. Nature, 399(6737), 655-661.
15. Daw, N., & Courville, A. (2007). The pigeon as particle filter. In J. C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in neural information processing systems, 20 (pp. 369-376). Cambridge, MA: MIT.
16. Dayan, P. (1998).Ahierarchicalmodel of binocular rivalry. Neural Computation, 10(5), 1119-1135.
17. Epley, N., & Gilovich, T. (2006). The anchoring-and-adjustment heuristic. Psychological Science, 17(4), 311-318.
18. Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010). Statistically optimal perception and learning: Frombehavior to neural representations. Trends in Cognitive Sciences, 14, 119-130.
19. Fox, R.,&Check, R. (1968).Detection of motion during binocular rivalry suppression. Journal of Experimental Psychology, 78(3), 388-395.
20. Freeman, A. (2005).Multistagemodel for binocular rivalry. Journal of Neurophysiology, 94(6), 4412-4420.
21. Fukuda, H. (1981).Magnitude of suppression of binocular rivalry within the invisible pattern. Perceptual and Motor Skills, 53(2), 371-375.
22. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions of Pattern Analysis and Machine Intelligence, 6, 721-741.
23. Glauber, R. (1963). Time-dependent statistics of the Ising model. Journal ofMathematical Physics, 4, 294-307.
24. Gold, J., & Shadlen, M. (2002). Banburismus and the brain: Decoding the relationship between sensory stimuli, decisions, and reward. Neuron, 36(2), 299-308.
25. Griffiths, T., & Tenenbaum, J. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767-773.
26. Grinband, J., Hirsch, J.,&Ferrera, V. (2006).Aneural representation of categorization uncertainty in the human brain. Neuron, 49(5), 757-763.
27. Hohwy, J., Roepstorff, A., & Friston, K. (2008). Predictive coding explains binocular rivalry: An epistemological review. Cognition, 108(3), 687-701.
28. Hollins, M. (1980). The effect of contrast on the completeness of binocular rivalry suppression. Attention, Perception, and Psychophysics, 27(6), 550-556.
29. Hopfield, J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79(8), 2254-2258.
30. Hoyer, P., & Hyvärinen, A. (2003). Interpreting neural response variability asMonte Carlo sampling of the posterior. In S. Becker, S. Thrün, & K. Obermayer (Eds.), Advances in neural information processing systems, 15 (pp. 277-284). Cambridge, MA: MIT Press.
31. Kang, M. (2009). Size matters: A study of binocular rivalry dynamics. Journal of Vision, 9(1), 1-11.
32. Kiani, R., & Shadlen, M. (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science, 324(5928), 759-764.
33. Knapen, T., Kanai, R., Brascamp, J., van Boxtel, J., & van Ee, R. (2007). Distance in feature space determines exclusivity in visual rivalry. Vision Research, 47(26), 3269-3275.
34. Knill, D., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712-719.
35. Knill, D., & Richards, W. (1996). Perception as Bayesian inference. Cambridge: Cambridge University Press.
36. Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT Press.
37. Kording, K., & Wolpert, D. (2004). Bayesian integration in sensorimotor learning. Nature, 427(6971), 244-247.
38. Laing, C., & Chow, C. (2002). A spiking neuron model for binocular rivalry. Journal of Computational Neuroscience, 12(1), 39-53.
39. Lankheet, M. (2006). Unraveling adaptation and mutual inhibition in perceptual rivalry. Journal of Vision, 6(4), 304-310.
40. Lee, S., Blake, R., & Heeger, D. (2005). Traveling waves of activity in primary visual cortex during binocular rivalry. Nature Neuroscience, 8(1), 22-23.
41. Lee, T., & Mumford, D. (2003). Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A, 20(7), 1434-1448.
42. Leopold, D., & Logothetis, N. (1996). Activity changes in early visual cortex reflect monkeys' percepts during binocular rivalry. Nature, 379(6565), 549-553.
43. Levelt, W. (1965). On binocular rivalry. Soesterberg, Netherlands: Institute for Perception Rvo-Tno.
44. Levy, R., Reali, F., & Griffiths, T. (2009). Modeling the effects of memory on human online sentence processing with particle filters. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems, 21 (pp. 937-944). Cambridge, MA: MIT Press.
45. Liu, L., Tyler, C., & Schor, C. (1992). Failure of rivalry at low contrast: Evidence of a suprathreshold binocular summation process. Vision Research, 32(8), 1471-1479.
46. Logothetis, N., Leopold, D., & Sheinberg, D. (1996). What is rivalling during binocular rivalry? Nature, 380(6575), 621-624.
47. Mamassian, P., & Goutcher, R. (2005). Temporal dynamics in bistable perception. Journal of Vision, 5(4), 361-375.
48. Moreno-Bote, R., Rinzel, J., & Rubin, N. (2007). Noise-induced alternations in an attractor networkmodel of perceptual bistability. Journal of Neurophysiology, 98(3), 1125-1139.
49. Mueller, T., & Blake, R. (1989). A fresh look at the temporal dynamics of binocular rivalry. Biological Cybernetics, 61(3), 223-232.
50. Noest, A., van Ee, R., Nijs, M., & Van Wezel, R. (2007). Percept-choice sequences driven by interrupted ambiguous stimuli: A low-level neural model. Journal of Vision, 7(8), 1-14.
51. O'Shea, R. (1987). Chronometric analysis supports fusion rather than suppression theory of binocular vision. Vision Research, 27(5), 781-791.
52. O'Shea, R., Sims, A.,&Govan, D. (1997). The effect of spatial frequency and field size on the spread of exclusive visibility in binocular rivalry. Vision Research, 37(2), 175-183.
53. Polonsky, A., Blake, R., Braun, J., & Heeger, D. (2000). Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry. Nature Neuroscience, 3(11), 1153-1159.
54. Robert, C., & Casella, G. (2004). Monte Carlo statistical methods. New York: Springer.
55. Sanborn, A., Griffiths, T., & Navarro, D. (2006). Rational approximations to rational models: Alternative algorithms for category living. Psychological Review, 117, 1144-1167.
56. Schrater, P., & Kersten, D. (2000). How optimal depth cue integration depends on the task. International Journal of Computer Vision, 40(1), 71-89.
57. Schrater, P., & Sundareswara, R. (2007). Theory and dynamics of perceptual bistability. In B. Schölkopf, J. Platt, & T. Hoffman (Eds.), Advances in neural information processing systems, 19 (pp. 1217-1224). Cambridge, MA: MIT Press.
58. Schwartz, O., Pillow, J., Rust, N., & Simoncelli, E. (2006). Spike-triggered neural characterization. Journal of Vision, 6(4), 484-507.
59. Shi, L., & Griffiths, T. (2009). Neural implementation of hierarchical Bayesian inference by importance sampling. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in neural information processing systems, 22 (pp. 1669-1677). Cambridge, MA: MIT Press.
60. Shimojo, S., & Nakayama, K. (1990). Real world occlusion constraints and binocular rivalry. Vision Research, 30(1), 69-80.
61. Sundareswara, R.,&Schrater, P. (2008). Perceptualmultistability predicted by search model for Bayesian decisions. Journal of Vision, 8(5), 1-19.
62. Swersky, K., Chen, B., Marlin, B., & de Freitas, N. (2010). A tutorial on stochastic approximation algorithms for training restricted Boltzmann machines and deep belief nets. In Information Theory and Applications Workshop, 2010 (pp. 1-10). Piscataway, NJ: IEEE.
63. Tong, F., Meng, M., & Blake, R. (2006). Neural bases of binocular rivalry. Trends in Cognitive Sciences, 10(11), 502-511.
64. Ullman, T., Goodman, N., & Tenenbaum, J. (2010). Theory acquisition as stochastic search. In Proceedings of the 32nd Annual Cognitive Science Society. Cognitive Science Society.
65. van Ee, R. (2009). Stochastic variations in sensory awareness are driven by noisy neuronal adaptation: Evidence from serial correlations in perceptual bistability. JOSA A, 26(12), 2612-2622.
66. Vul, E., Goodman, N., Griffiths, T., & Tenenbaum, J. (2009). One and done? Optimal decisions from very few samples. In Proceedings of the 31st Annual Meeting of the Cognitive Science Society. Cognitive Science Society.
67. Wales, R., & Fox, R. (1970). Increment detection thresholds during binocular rivalry suppression. Perception and Psychophysics, 8(2), 90-94.
68. Weiss, Y., Simoncelli, E., & Adelson, E. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5(6), 598-604.
69. Wilson, H. (2003). Computational evidence for a rivalry hierarchy in vision. Proceedings of the National Academy of Sciences of the United States of America, 100(24), 14499-14503.
70. Wilson, H. (2007).Minimal physiological conditions for binocular rivalry and rivalry memory. Vision Research, 47(21), 2741-2750.
71. Wilson, H., Blake, R., & Lee, S. (2001). Dynamics of travelling waves in visual perception. Nature, 412(6850), 907-910.
72. Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Sciences, 10(7), 301-308.