Relationships between the threshold and slope of psychometric and neurometric functions during perceptual learning: Implications for neuronal pooling

Journal of Neurophysiology

Volumn 103, Issue 1, 2010, Pages 140-154

  Gold, Joshua I ^a   Law, Chi Tat ^a   Connolly, Patrick ^a   Bennur, Sharath ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL EXPERIMENT; ARTICLE; CONTROLLED STUDY; FEMALE; LEARNING; MACACA; MALE; MATHEMATICAL MODEL; MOTION ANALYSIS SYSTEM; MOVEMENT PERCEPTION; MUSCLE STRENGTH; NONHUMAN; PERCEPTION; PERCEPTIVE THRESHOLD; PRIORITY JOURNAL; PSYCHOMETRY; SENSORY NERVE CELL; SENSORY STIMULATION; SPATIAL DISCRIMINATION; TASK PERFORMANCE; TIME PERCEPTION; VISION;

ALGORITHMS; ANIMALS; DECISION MAKING; DISCRIMINATION (PSYCHOLOGY); FEMALE; LEARNING; MACACA MULATTA; MALE; MICROELECTRODES; MODELS, NEUROLOGICAL; MOTION PERCEPTION; NEUROPSYCHOLOGICAL TESTS; NONLINEAR DYNAMICS; PSYCHOMETRICS; TEMPORAL LOBE; TIME FACTORS;

EID: 74049121984     PISSN: 00223077     EISSN: 15221598     Source Type: Journal    
DOI: 10.1152/jn.00744.2009     Document Type: Article

References (114)

1. Adelson EH, Bergen JR. Spatiotemporal energy models for the perception of motion. J Opt Soc Am A Opt Image Sci Vis 2: 284-299, 1985.
2. Ahissar M, Hochstein S. Attentional control of early perceptual learning. Proc Natl Acad Sci USA 90: 5718-5722, 1993.
3. Anderson JS, Lampl I, Gillespie DC, Ferster D. The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science 290: 1968-1972, 2000.
4. Bair W, Zohary E, Newsome WT. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J Neurosci 21: 1676-1697, 2001.
5. Bogacz R, Brown E, Moehlis J, Holmes P, Cohen JD. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced choice tasks. Psychol Rev 113: 700-765, 2006.
6. Brainard DH. The Psychophysics Toolbox. Spat Vis 10: 433-436, 1997.
7. Britten KH. The middle temporal area: motion processing and the link to perception. In: The Visual Neurosciences, edited by Chalupa LM, Werner JS. Cambridge, MA: MIT Press, 2003, p. 1203-1216.
8. Britten KH, Heuer HW. Spatial summation in the receptive fields of MT neurons. J Neurosci 19: 5074-5084, 1999.
9. Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis Neurosci 13: 87-100, 1996.
10. Britten KH, Shadlen MN, Newsome WT, Movshon JA. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12: 4745-4765, 1992.
11. Britten KH, Shadlen MN, Newsome WT, Movshon JA. Responses of neurons in macaque MT to stochastic motion signals. Vis Neurosci 10: 1157-1169, 1993.
12. Carandini M, Heeger DJ. Summation and division by neurons in primate visual cortex. Science 264: 1333-1336, 1994.
13. Carandini M, Heeger DJ, Movshon JA. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci 17: 8621-8644, 1997.
14. Celebrini S, Newsome WT. Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey. J Neurosci 14: 4109-4124, 1994.
15. Celebrini S, Newsome WT. Microstimulation of extrastriate area MST influences performance on a direction discrimination task. J Neurophysiol 73: 437-448, 1995.
16. Connolly PM, Bennur S, Gold JI. Correlates of perceptual learning in an oculomotor decision variable. J Neurosci 29: 2136-2150, 2009.
17. Crist RE, Li W, Gilbert CD. Learning to see: experience and attention in primary visual cortex. Nat Neurosci 4: 519-525, 2001.
18. de la Rocha J, Doiron B, Shea-Brown E, Josic K, Reyes A. Correlation between neural spike trains increases with firing rate. Nature 448: 802-806, 2007.
19. Destexhe A, Marder E. Plasticity in single neuron and circuit computations. Nature 431: 789-795, 2004.
20. Ditterich J. Evidence for time-variant decision making. Eur J Neurosci 24: 3628-3641, 2006.
21. Dosher BA, Lu ZL. Mechanisms of perceptual learning. Vision Res 39: 3197-3221, 1999.
22. Draper NR, Smith H. Applied Regression Analysis. New York: Wiley, 1998.
23. Eckhoff P, Holmes P, Law C, Connolly PM, Gold JI. On diffusion processes with variable drift rates as models for decision making during learning. New J Phys 10: 015006, 2008.
24. Fahle M. Perceptual learning: specificity versus generalization. Curr Opin Neurobiol 15: 154-160, 2005.
25. Fine I, Jacobs RA. Comparing perceptual learning across tasks: a review. J Vis 2: 190-203, 2002.
26. Furmanski CS, Schluppeck D, Engel SA. Learning strengthens the response of primary visual cortex to simple patterns. Curr Biol 14: 573-578, 2004.
27. Geisler WS, Albrecht DG. Visual cortex neurons in monkeys and cats: detection, discrimination, and identification. Vis Neurosci 14: 897-919, 1997.
28. Gilbert CD, Sigman M, Crist RE. The neural basis of perceptual learning. Neuron 31: 681-697, 2001.
29. Gilchrist JM, Jerwood D, Ismaiel HS. Comparing and unifying slope estimates across psychometric function models. Percept Psychophys 67: 1289-1303, 2005.
30. Gold JI, Law CT, Connolly P, Bennur S. The relative influences of priors and sensory evidence on an oculomotor decision variable during perceptual learning. J Neurophysiol 100: 2653-2668, 2008.
31. Gold JI, Shadlen MN. Representation of a perceptual decision in developing oculomotor commands. Nature 404: 390-394, 2000.
32. Gold JI, Shadlen MN. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J Neurosci 23: 632-651, 2003.
33. Gold JI, Shadlen MN. The neural basis of decision making. Annu Rev Neurosci 30: 535-574, 2007.
34. Goldstone RL. Perceptual learning. Annu Rev Psychol 49: 585-612, 1998.
35. Graham N. Detection and identification of near-threshold visual patterns. J Opt Soc Am A Opt Image Sci Vis 2: 1468-1482, 1985.
36. Green DM, Luce RD. Parallel psychometric functions from a set of independent detectors. Psychol Bull 82: 483-486, 1975.
37. Green DM, Swets JA. Signal Detection Theory and Psychophysics. New York: Wiley, 1966.
38. Grossberg S, Pilly PK. Temporal dynamics of decision-making during motion perception in the visual cortex. Vision Res 48: 1345-1373, 2008.
39. Gu Y, DeAngelis GC, Angelaki DE. A functional link between area MSTd and heading perception based on vestibular signals. Nat Neurosci 10: 1038-1047, 2007.
40. Hanks TD, Ditterich J, Shadlen MN. Microstimulation of macaque area LIP affects decision-making in a motion discrimination task. Nat Neurosci 9: 682-689, 2006.
41. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29-36, 1982.
42. Hays AV, Richmond BJ, Optican LM. Unix-based multiple-process system for real-time data acquisition and control. WESCON Proc Conf 1-10, 1982.
43. Heeger DJ. Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. J Neurophysiol 70: 1885-1898, 1993.
44. Herzog MH, Fahle M. Modeling perceptual learning: difficulties and how they can be overcome. Biol Cybern 78: 107-117, 1998.
45. Hol K, Treue S. Different populations of neurons contribute to the detection and discrimination of visual motion. Vision Res 41: 685-689, 2001.
46. Horwitz GD, Newsome WT. Target selection for saccadic eye movements: direction-selective visual responses in the superior colliculus. J Neurophysiol 86: 2527-2542, 2001.
47. Ito M, Westheimer G, Gilbert CD. Attention and perceptual learning modulate contextual influences on visual perception. Neuron 20: 1191-1197, 1998.
48. Jacobs RA. Adaptive precision pooling of model neuron activities predicts the efficiency of human visual learning. J Vis 9: 1-15, 2009.
49. Jazayeri M, Movshon JA. Integration of sensory evidence in motion discrimination. J Vis 7: 71-77, 2007.
50. Judge SJ, Richmond BJ, Chu FC. Implantation of magnetic search coils for measurement of eye position: an improved method. Vision Res 20: 535-538, 1980.
51. Kim JN, Shadlen MN. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat Neurosci 2: 176-185, 1999.
52. Klein SA. Measuring, estimating, and understanding the psychometric function: a commentary. Percept Psychophys 63: 1421-1455, 2001.
53. Knudsen EI. Instructed learning in the auditory localization pathway of the barn owl. Nature 417: 322-328, 2002.
54. Kontsevich LL, Tyler CW. Distraction of attention and the slope of the psychometric function. J Opt Soc Am A Opt Image Sci Vis 16: 217-222, 1999.
55. Kouh M, Poggio T. A canonical neural circuit for cortical nonlinear operations. Neural Comput 20: 1427-1451, 2008.
56. Law CT, Gold JI. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat Neurosci 11: 505-513, 2008.
57. Law CT, Gold JI. Reinforcement learning can account for associative and perceptual learning on a visual-decision task. Nat Neurosci 12: 655-663, 2009.
58. Lewis JW, Van Essen DC. Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey. J Comp Neurol 428: 112-137, 2000.
59. Macmillan NA, Creelman CD. Detection Theory: A User's Guide. Mahwah, NJ: Erlbaum, 2004.
60. Maertens M, Pollmann S. fMRI reveals a common neural substrate of illusory and real contours in V1 after perceptual learning. J Cogn Neurosci 17: 1553-1564, 2005.
61. Maunsell JH, Treue S. Feature-based attention in visual cortex. Trends Neurosci 29: 317-322, 2006.
62. Meeker WQ, Escobar LA. Teaching about approximate confidence regions based on maximum likelihood estimation. Am Statistician 49: 48-53, 1995.
63. Mendenhall W, Scheaffer RL, Wackerly DD. Mathematical Statistics with Applications. Boston, MA: Duxbury Press, 1986.
64. Miller KD, Troyer TW. Neural noise can explain expansive, power-law nonlinearities in neural response functions. J Neurophysiol 87: 653-659, 2002.
65. Nachmias J. On the psychometric function for contrast detection. Vision Res 21: 215-223, 1981.
66. Newsome WT, Paré EB. A selective impairment of motion perception following lesions of the middle temporal visual area (MT). J Neurosci 8: 2201-2211, 1988.
67. Parker AJ, Newsome WT. Sense and the single neuron: probing the physiology of perception. Annu Rev Neurosci 21: 227-277, 1998.
68. Pasternak T, Merigan WH. Motion perception following lesions of the superior temporal sulcus in the monkey. Cereb Cortex 4: 247-259, 1994.
69. Pelli DG. Uncertainty explains many aspects of visual contrast detection and discrimination. J Opt Soc Am A Opt Image Sci Vis 2: 1508-1532, 1985.
70. Pelli DG. On the relation between summation and facilitation. Vision Res 27: 119-123, 1987.
71. Pelli DG. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat Vis 10: 437-442, 1997.
72. Petrov AA, Dosher BA, Lu ZL. The dynamics of perceptual learning: an incremental reweighting model. Psychol Rev 112: 715-743, 2005.
73. Platt ML. Neural correlates of decisions. Curr Opin Neurobiol 12: 141-148, 2002.
74. Pouget A, Dayan P, Zemel RS. Inference and computation with population codes. Annu Rev Neurosci 26: 381-410, 2003.
75. Purushothaman G, Bradley DC. Neural population code for fine perceptual decisions in area MT. Nat Neurosci 8: 99-106, 2005.
76. Quick RFJ. A vector-magnitude model of contrast detection. Kybernetik 16: 65-67, 1974.
77. Raiguel S, Vogels R, Mysore SG, Orban GA. Learning to see the difference specifically alters the most informative V4 neurons. J Neurosci 26: 6589-6602, 2006.
78. Reichardt W. Processing of optical information by the visual system of the fly. Vision Res 26: 113-126, 1986.
79. Ricciardi LM, Sacerdote L. The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time. Biol Cybern 35: 1-9, 1979.
80. Robinson DA. A method of measuring eye movement using a scleral search coil in a magnetic field. IEEE Trans Biomed Eng 10: 137-145, 1963.
81. Roitman JD, Shadlen MN. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J Neurosci 22: 9475-9489, 2002.
82. Rust NC, Schwartz O, Movshon JA, Simoncelli EP. Spatiotemporal elements of macaque V1 receptive fields. Neuron 46: 945-956, 2005.
83. Salzman CD, Britten KH, Newsome WT. Cortical microstimulation influences perceptual judgements of motion direction. Nature 346: 174-177, 1990.
84. Schoups AA, Vogels R, Qian N, Orban G. Practising orientation identification improves orientation coding in V1 neurons. Nature 412: 549-553, 2001.
85. Schwartz S, Maquet P, Frith C. Neural correlates of perceptual learning: a functional MRI study of visual texture discrimination. Proc Natl Acad Sci USA 99: 17137-17142, 2002.
86. Sclar G, Maunsell JH, Lennie P. Coding of image contrast in central visual pathways of the macaque monkey. Vision Res 30: 1-10, 1990.
87. Seidemann E, Newsome WT. Effect of spatial attention on the responses of area MT neurons. J Neurophysiol 81: 1783-1794, 1999.
88. Seitz AR, Kim D, Watanabe T. Rewards evoke learning of unconsciously processed visual stimuli in adult humans. Neuron 61: 700-707, 2009.
89. Seitz AR, Watanabe T. Psychophysics: is subliminal learning really passive? (Abstract). Nature 422: 36, 2003.
90. Seitz AR, Watanabe T. A unified model for perceptual learning. Trends Cogn Sci 9: 329-334, 2005.
91. Seung HS, Sompolinsky H. Simple models for reading neuronal population codes. Proc Natl Acad Sci USA 90: 10749-10753, 1993.
92. Shadlen MN, Britten KH, Newsome WT, Movshon JA. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J Neurosci 16: 1486-1510, 1996.
93. Shadlen MN, Hanks TD, Churchland AK, Kiani R, Yang T. The speed and accuracy of a simple perceptual decision: a mathematical primer. In: Bayesian Brain: Probabilistic Approaches to Neural Coding, edited by Doya K, Ishii S, Rao R, Pouget A. Cambridge, MA: MIT Press, 2007, p. 209-238.
94. Shadlen MN, Newsome WT. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J Neurophysiol 86: 1916-1936, 2001.
95. Shamir M, Sompolinsky H. Nonlinear population codes. Neural Comput 16: 1105-1136, 2004.
96. Snyder LH, Batista AP, Andersen RA. Coding of intention in the posterior parietal cortex. Nature 386: 167-170, 1997.
97. Sompolinsky H, Yoon H, Kang K, Shamir M. Population coding in neuronal systems with correlated noise. Phys Rev E Stat Nonlin Soft Matter Phys 64: 051904, 2001.
98. Stanton PK. LTD, LTP, and the sliding threshold for long-term synaptic plasticity. Hippocampus 6: 35-42, 1996.
99. Strasburger H. Converting between measures of slope of the psychometric function. Percept Psychophys 63: 1348-1355, 2001.
100. Sugrue LP, Corrado GS, Newsome WT. Matching behavior and the representation of value in the parietal cortex. Science 304: 1782-1787, 2004.
101. Tyler CW, Chen CC. Signal detection theory in the 2AFC paradigm: attention, channel uncertainty and probability summation. Vision Res 40: 3121-3144, 2000.
102. Walker MP, Stickgold R, Jolesz FA, Yoo SS. The functional anatomy of sleep-dependent visual skill learning. Cereb Cortex 15: 1666 -1675, 2005.
103. Watanabe T, Nanez JE, Koyama S, Mukai I, Liederman J, Sasaki Y. Greater plasticity in lower-level than higher-level visual motion processing in a passive perceptual learning task. Nat Neurosci 5: 1003-1009, 2002.
104. Watanabe T, Nanez JE, Sasaki Y. Perceptual learning without perception. Nature 413: 844-848, 2001.
105. Watson AB. Probability summation over time. Vision Res 19: 515-522, 1979.
106. Wichmann FA, Hill NJ. The psychometric function: I. Fitting, sampling, and goodness of fit. Percept Psychophys 63: 1293-1313, 2001a.
107. Wichmann FA, Hill NJ. The psychometric function: II. Bootstrap-based confidence intervals and sampling. Percept Psychophys 63: 1314-1329, 2001b.
108. Wong KF, Wang XJ. A recurrent network mechanism of time integration in perceptual decisions. J Neurosci 26: 1314-1328, 2006.
109. Yang T, Maunsell JH. The effect of perceptual learning on neuronal responses in monkey visual area V4. J Neurosci 24: 1617-1626, 2004.
110. Yotsumoto Y, Watanabe T, Sasaki Y. Different dynamics of performance and brain activation in the time course of perceptual learning. Neuron 57: 827-833, 2008.
111. Yu AJ, Giese MA, Poggio TA. Biophysiologically plausible implementations of the maximum operation. Neural Comput 14: 2857-2881, 2002.
112. Yu C, Klein SA, Levi DM. Perceptual learning in contrast discrimination and the (minimal) role of context. J Vis 4: 169-182, 2004.
113. Zohary E, Celebrini S, Britten KH, Newsome WT. Neuronal plasticity that underlies improvement in perceptual performance. Science 263: 1289-1292, 1994a.
114. Zohary E, Shadlen MN, Newsome WT. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370: 140-143, 1994b.