Organizing probabilistic models of perception

Trends in Cognitive Sciences

Volumn 16, Issue 10, 2012, Pages 511-518

  Ma, Wei Ji ^a  

^a BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN MODEL; EMPIRICAL EVIDENCE; PROBABILISTIC MODELS; SIGNAL DETECTION THEORY;

BAYESIAN NETWORKS; PROBABILITY DISTRIBUTIONS;

OPTIMIZATION;

BAYESIAN LEARNING; DECISION MAKING; FEEDBACK SYSTEM; MATHEMATICAL ANALYSIS; PERCEPTION; PROBABILITY; PSYCHOPHYSIOLOGY; REVIEW;

ALGORITHMS; BAYES THEOREM; DECISION MAKING; HUMANS; MODELS, STATISTICAL; PERCEPTION; SIGNAL DETECTION, PSYCHOLOGICAL;

EID: 84866555374     PISSN: 13646613     EISSN: 1879307X     Source Type: Journal    
DOI: 10.1016/j.tics.2012.08.010     Document Type: Review

References (112)

1. Alhacen's Theory of Visual Perception: A Critical Edition, with English Translation and Commentary, of the First Three Books of Alhacen's De aspectibus, the Medieval Latin Version of Ibn al-Haytham's Kitab al-Manazir 2001, American Philosophical Society. A.M. Smith (Ed.).
2. Helmholtz H.L.F.v. Treatise on Physiological Optics 1856, Thoemmes Press.
3. Mach E. Contributions to the Analysis of the Sensations 1886/1897, Open Court Publishing.
4. Tanner W.P., et al. A decision-making theory of visual detection. Psychol. Rev. 1954, 61:401-409.
5. Peterson W.W., et al. The theory of signal detectability. Transactions IRE Profession Group on Information Theory, PGIT-4 1954, 171-212.
6. Green D.M., Swets J.A. Signal Detection Theory and Psychophysics 1966, John Wiley & Sons.
7. Perception as Bayesian Inference 1996, Cambridge University Press. D.C. Knill, W. Richards (Eds.).
8. Kording K. Decision theory: what 'should' the nervous system do?. Science 2007, 318:606-610.
9. Geisler W.S. Contributions of ideal observer theory to vision research. Vision Res. 2011, 51:771-781.
10. Bowers J.S., Davis C.J. Bayesian just-so stories in psychology and neuroscience. Psychol. Bull. 2012, 138:389-414.
11. Trommershauser J., et al. Decision making, movement planning and statistical decision theory. Trends Cogn. Sci. 2008, 12:291-297.
12. Kersten D., et al. Object perception as Bayesian inference. Annu. Rev. Psychol. 2004, 55:271-304.
13. Vilares I., Kording K.P. Bayesian models: the structure of the world, uncertainty, behavior, and the brain. Ann. N. Y. Acad. Sci. 2011, 1224:22-39.
14. McGurk H., MacDonald J. Hearing lips and seeing voices. Nature 1976, 264:746-748.
15. Sensory Cue Integration 2011, Oxford University Press. J. Trommershauser (Ed.).
16. Ma W.J., et al. Bayesian inference with probabilistic population codes. Nat. Neurosci. 2006, 9:1432-1438.
17. Ma W.J., et al. Behavior and neural basis of near-optimal visual search. Nat. Neurosci. 2011, 14:783-790.
18. Graham N., et al. Signal dection models for multidimensional stimuli: probability distributions and combination rules. J. Math. Psychol. 1987, 31:366-409.
19. Nolte L.W., Jaarsma D. More on the detection of one of M orthogonal signals. J. Acoust. Soc. Am. 1966, 41:497-505.
20. Verghese P. Visual search and attention: a signal detection theory approach. Neuron 2001, 31:523-535.
21. Gu Y., et al. Neural correlates of multisensory cue integration in macaque MSTd. Nat. Neurosci. 2008, 11:1201-1210.
22. Ernst M.O., Banks M.S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 2002, 415:429-433.
23. Alais D., Burr D. The ventriloquist effect results from near-optimal bimodal integration. Curr. Biol. 2004, 14:257-262.
24. van Beers R.J., et al. How humans combine simultaneous proprioceptive and visual position information. Exp. Brain Res. 1996, 111:253-261.
25. Knill D.C. Mixture models and the probabilistic structure of depth cues. Vision Res. 2003, 43:831-854.
26. Landy M.S., Kojima H. Ideal cue combination for localizing texture-defined edges. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 2001, 18:2307-2320.
27. Battaglia P.W., et al. Bayesian integration of visual and auditory signals for spatial localization. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 2003, 20:1391-1397.
28. Fetsch C.R., et al. Neural correlates of reliability-based cue weighting during multisensory integration. Nat. Neurosci. 2012, 15:146-154.
29. Rosas P., et al. Texture and haptic cues in slant discrimination: reliability-based cue weighting without statistically optimal cue combination. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 2005, 22:801-809.
30. Ratcliff R. A theory of memory retrieval. Psychological Review 1978, 85:59-108.
31. Gold J.I., Shadlen M.N. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 2001, 5:10-16.
32. Bogacz R., et al. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 2006, 113:700-765.
33. Wald A., Wolfowitz J. Optimum character of the sequential probability ratio test. Ann. Math. Stat. 1948, 19:326-339.
34. Kording K.P., et al. Causal inference in multisensory perception. PLoS ONE 2007, 2:e943.
35. Van den Berg R., et al. Optimal inference of sameness. Proc. Natl. Acad. Sci. U.S.A. 2011, 109:3178-3183.
36. Vul E., et al. Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. Neural Inf. Proc. Syst. 2009, 22:1955.
37. Sato Y., et al. Bayesian inference explains perception of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli. Neural Comput. 2007, 19:3335-3355.
38. Hospedales T., Vijayakumar S. Multisensory oddity detection as Bayesian inference. PLoS ONE 2009, 4:e4205.
39. Keshvari S., et al. Probabilistic computation in human perception under variability in encoding precision. PLoS ONE 2012, 7:e40216.
40. Geisler W.S. Visual perception and the statistical properties of natural scenes. Annu. Rev. Psychol. 2008, 59:167-192.
41. Geisler W.S., et al. Edge co-occurrence in natural images predicts contour grouping performance. Vision Res. 2001, 41:711-724.
42. Geisler W.S., Perry J.S. Contour statistics in natural images: Grouping across occlusions. Vis. Neurosci. 2009, 26:109-121.
43. Elder J.H., Goldberg R.M. Ecological statistics of Gestalt laws for the perceptual organization of contours. J. Vis. 2002, 2:324-353.
44. Girshick A.R., et al. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat. Neurosci. 2011, 14:926-932.
45. Barlow H. Retinal noise and absolute threshold. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 1956, 46:634-639.
46. Cohn T.E., Lasley D.J. Detectability of a luminance increment: effect of spatial uncertainty. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 1974, 64:1715-1719.
47. De Vries H.D. The quantum character of light and its bearing upon threshold of vision, the differential sensitivity and visual acuity of the eye. Physica 1943, 10:553-564.
48. Rose A. The sensitivity performance of the human eye on an absolute scale. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 1948, 38:196-208.
49. Hecht S., et al. Energy, quanta, and vision. J. Gen. Physiol. 1942, 25:819-840.
50. Burgess A.E., et al. Efficiency of human visual signal discrimination. Science 1981, 214:93-94.
51. Burgess A.E., Colborne B. Visual signal detection. IV. Observer inconsistency. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 1988, 5:617-627.
52. Legge G.E., et al. Contrast discrimination in noise. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 1987, 4:391-404.
53. Geisler W.S. Sequential ideal-observer analysis of visual discriminations. Psychol. Rev. 1989, 96:267-314.
54. Barlow H.B. The efficiency of detecting changes of density in random dot patterns. Vision Res. 1978, 18:637-650.
55. Tjan B.S., et al. Human efficiency for recognizing 3-D objects in luminance noise. Vision Res. 1995, 35:3053-3069.
56. Liu Z., et al. Object classification for human and ideal observer. Vision Res. 1995, 35:549-568.
57. Beckmann P.J., Legge G.E. Preneural limitations on letter identification in central and peripheral vision. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 2002, 19:2349-2362.
58. Beck J.M., et al. Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 2012, 74:30-39.
59. Stocker A.A., Simoncelli E.P. Noise characteristics and prior expectations in human visual speed perception. Nat. Neurosci. 2006, 9:578-585.
60. Weiss Y., et al. Motion illusions as optimal percepts. Nat. Neurosci. 2002, 5:598-604.
61. Ahrens M., Sahani M. Observers exploit stochastic models of sensory change to help judge the passage of time. Curr. Biol. 2011, 21:1-7.
62. Mamassian P., Landy M.S. Observer biases in the 3D interpretation of line drawings. Vision Res. 1998, 38:2817-2832.
63. Brainard D.H., et al. Bayesian model of human color constancy. J. Vis. 2006, 6:1267-1281.
64. Ernst U.A., et al. Optimality of human contour detection. PLoS Comp. Biol. 2012, 8:e1002520.
65. Feldman J. Bayesian contour integration. Percept. Psychophys. 2001, 63:1171-1182.
66. Barthelmé S., Mamassian P. Flexible mechanisms underlie the evaluation of visual confidence. Proc. Natl. Acad. Sci. U. S. A. 2010, 107:20834-20839.
67. Knill D.C., Saunders J.A. Do humans optimally integrate stereo and texture information for judgments of surface slant?. Vision Res. 2003, 43:2539-2558.
68. Whiteley L., Sahani M. Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes. J. Vis. 2008, 8:1-15.
69. Gao J.T., et al. Dynamic integration of reward and stimulus information in perceptual decision making. PLoS ONE 2010, 6:e16749.
70. Kepecs A., et al. Neural correlates, computation and behavioural impact of decision confidence. Nature 2008, 455:227-233.
71. Kiani R., Shadlen M.N. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 2009, 324:759-764.
72. Feng S., et al. Can monkeys choose optimally when faced with noisy stimuli and unequal rewards?. PLoS Comp. Biol. 2009, 5:e1000284.
73. Huk A.C., Shadlen M.N. Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. J. Neurosci. 2005, 25:10420-10436.
74. Baldassi S., Verghese P. Comparing integration rules in visual search. J. Vis. 2002, 2:559-570.
75. Eckstein M.P. The lower visual search efficiency for conjunctions is due to noise and not serial attentional processing. Psychol. Sci. 1998, 9:111-118.
76. Wilken P., Ma W.J. A detection theory account of change detection. J. Vis. 2004, 4:1120-1135.
77. Baldassi S., Burr D. Visual clutter causes high-magnitude errors. PLoS Biol. 2006, 4:e56.
78. Eckstein M.P., et al. A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays. Percept. Psychophys. 2000, 62:425-451.
79. Pelli D.G. Uncertainty explains many aspects of visual contrast detection and discrimination. J. Opt. Soc. Am. A: Opt. Image Proc. Syst. 1985, 2:1508-1532.
80. Shams L., et al. Sound-induced flash illusion as an optimal percept. Neuroreport 2005, 16:1923-1927.
81. Roach N.W., et al. Resolving multisensory conflict: a strategy for balancing the costs and benefits of audio-visual integration. Proc. Biol. Sci. 2006, 273:2159-2168.
82. Maij F., et al. Temporal uncertainty separates flashes from their background during saccades. J. Neurosci. 2011, 31:3708-3711.
83. Brenner E., et al. If I saw it, it probably wasn't far from where I was looking. J. Vis. 2008, 8:1-10.
84. Rowland B., et al. A Bayesian model unifies multisensory spatial localization with the physiological properties of the superior colliculus. Exp. Brain Res. 2007, 180:153-161.
85. Griffiths T., Tenenbaum J. Optimal predictions in everyday cognition. Psychol. Sci. 2006, 17:767-773.
86. Griffiths T.L., et al. Probabilistic models of cognition: exploring representations and inductive biases. Trends Cogn. Sci. 2010, 14:357-364.
87. Shi L., et al. Exemplar models as a mechanism for performing Bayesian inference. Psychon. Bull. Rev. 2010, 17:443-464.
88. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer.
89. Attwell D., Laughlin S.B. An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 2001, 21:1133-1145.
90. Lennie P. The cost of cortical computation. Curr. Biol. 2003, 13:493-497.
91. Bays P.M., Husain M. Dynamic shifts of limited working memory resources in human vision. Science 2008, 321:851-854.
92. Van den Berg R., et al. Variability in encoding precision accounts for visual short-term memory limitations. Proc. Natl. Acad. Sci. U.S.A. 2012, 10.1073/pnas.1117465109.
93. Ma W.J., Huang W. No capacity limit in attentional tracking: Evidence for probabilistic inference under a resource constraint. J. Vis. 2009, 9:1-30.
94. Shaw M.L. Identifying attentional and decision-making components in information processing. Attention and Performance 1980, 277-296. Erlbaum. R.S. Nickerson (Ed.).
95. Palmer J. Attentional limits on the perception and memory of visual information. J. Exp. Psychol. Hum. Percept. Perform. 1990, 16:332-350.
96. Barlow H.B. Single units and sensation: a neuron doctrine for perceptual psychology?. Perception 1972, 1:371-394.
97. Foldiak P. The 'ideal homunculus': statistical inference from neural population responses. Computation and Neural Systems 1993, 55-60. Kluwer Academic Publishers. F. Eeckman, J. Bower (Eds.).
98. Sanger T. Probability density estimation for the interpretation of neural population codes. J. Neurophysiol. 1996, 76:2790-2793.
99. Ma W.J. Signal detection theory, uncertainty, and Poisson-like population codes. Vision Res. 2010, 50:2308-2319.
100. Beck J.M., et al. Bayesian decision-making with probabilistic population codes. Neuron 2008, 60:1142-1145.
101. Huys Q., et al. Fast population coding. Neural Comput. 2007, 19:404-441.
102. Beck J.M., et al. Marginalization in neural circuits with divisive normalization. J. Neurosci. 2011, 31:15310-15319.
103. Fiser J., et al. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 2010, 14:119-130.
104. Anastasio T.J., et al. Using Bayes' rule to model multisensory enhancement in the superior colliculus. Neural Comput. 2000, 12:1165-1187.
105. Deneve S. Bayesian spiking neurons I: inference. Neural Comput. 2008, 20:91-117.
106. Anderson C. Neurobiological computational systems. Computational Intelligence Imitating Life 1994, 213-222. IEEE Press. J.M. Zurada (Ed.).
107. Hoyer P.O., Hyvarinen A. Interpreting neural response variability as Monte Carlo sampling of the posterior. Advances in Neural Information Processing Systems 2003, MIT Press, pp. 277-284.
108. Barlow H.B. Pattern recognition and the responses of sensory neurons. Ann. N. Y. Acad. Sci. 1969, 156:872-881.
109. Zemel R., et al. Probabilistic interpretation of population code. Neural Comput. 1998, 10:403-430.
110. Rao R.P. Bayesian computation in recurrent neural circuits. Neural Comput. 2004, 16:1-38.
111. Berkes P., et al. Spontaneous cortical activity reveals hallmarks of an optimal internel model of the environment. Science 2011, 331:83-87.
112. Jazayeri M., Movshon J.A. Optimal representation of sensory information by neural populations. Nat. Neurosci. 2006, 9:690-696.