Learning and neural plasticity in visual object recognition

Current Opinion in Neurobiology

Volumn 16, Issue 2, 2006, Pages 152-158

  Kourtzi, Zoe ^a   DiCarlo, James J ^b  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; EXPERIENCE; FEEDBACK SYSTEM; LEARNING; NERVE CELL PLASTICITY; NEUROSCIENCE; NONHUMAN; PERCEPTION; PRIMATE; PRIORITY JOURNAL; REVIEW; VISUAL MEMORY; VISUAL SYSTEM;

ANIMALS; BRAIN; CEREBRAL CORTEX; HUMANS; LEARNING; MAGNETIC RESONANCE IMAGING; MEMORY; NERVE NET; NEURAL PATHWAYS; NEURONAL PLASTICITY; PATTERN RECOGNITION, VISUAL; VISUAL PATHWAYS;

EID: 33645788857     PISSN: 09594388     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conb.2006.03.012     Document Type: Review

References (82)

1. Ungerleider L.G., and Mishkin M. Two cortical visual systems. In: Ingle D.J., Goodale M.A., and Mansfield R.J.W. (Eds). Analysis of Visual Behavior (1982), M.I.T. Press 549-585
2. Rolls E.T. Functions of the primate temporal lobe cortical visual areas in invariant visual object and face recognition. Neuron 27 (2000) 205-218
3. Hung C.P., Kreiman G., Poggio T., and DiCarlo J.J. Fast readout of object identity from macaque inferior temporal cortex. Science 310 (2005) 863-866
4. Quiroga R.Q., Reddy L., Kreiman G., Koch C., and Fried I. Invariant visual representation by single neurons in the human brain. Nature 435 (2005) 1102-1107
5. Grill-Spector K., and Malach R. The human visual cortex. Annu Rev Neurosci 27 (2004) 649-677
6. Wallis G., and Bulthoff H. Learning to recognize objects. Trends Cogn Sci 3 (1999) 22-31
7. Poggio T. A theory of how the brain might work. Cold Spring Harb Symp Quant Biol 55 (1990) 899-910
8. Wallis G., and Rolls E.T. Invariant face and object recognition in the visual system. Prog Neurobiol 51 (1997) 167-194
9. Foldiak P. Learning invariance from transformation sequences. Neural Comput 3 (1991) 194-200
10. Wiskott L., and Sejnowski T.J. Slow feature analysis: unsupervised learning of invariances. Neural Comput 14 (2002) 715-770
11. Ullman S., and Soloviev S. Computation of pattern invariance in brain-like structures. Neural Netw 12 (1999) 1021-1036
12. Karklin Y., and Lewicki M.S. A hierarchical Bayesian model for learning nonlinear statistical regularities in nonstationary natural signals. Neural Comput 17 (2005) 397-423
13. Rodman H.R. Development of inferior temporal cortex in the monkey. Cereb Cortex 4 (1994) 484-498
14. Blakemore C., and Van Sluyters R.C. Innate and environmental factors in the development of the kitten's visual cortex. J Physiol 248 (1975) 663-716
15. Sinha P, Bouvrie JVaS: Object concept learning: observations in congenitally blind children and a computational model. Neurocomputing 2006. in press.
16. Goldstone R.L. Perceptual learning. Annu Rev Psychol 49 (1998) 585-612
17. Gilbert C.D., Sigman M., and Crist R.E. The neural basis of perceptual learning. Neuron 31 (2001) 681-697
18. Schyns P.G., Goldstone R.L., and Thibaut J.P. The development of features in object concepts. Behav Brain Sci 21 (1998) 1-17 discussion 17-54
19. Fahle M. Perceptual learning: a case for early selection. J Vis 4 (2004) 879-890
20. Fine I., and Jacobs R.A. Comparing perceptual learning tasks: a review. J Vis 2 (2002) 190-203
21. Ghose G.M., Yang T., and Maunsell J.H. Physiological correlates of perceptual learning in monkey V1 and V2. J Neurophysiol 87 (2002) 1867-1888
22. Schoups A., Vogels R., Qian N., and Orban G. Practising orientation identification improves orientation coding in V1 neurons. Nature 412 (2001) 549-553
23. Kobatake E., Wang G., and Tanaka K. Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. J Neurophysiol 80 (1998) 324-330
24. Miyashita Y. Cognitive memory: cellular and network machineries and their top-down control. Science 306 (2004) 435-440
25. Rolls E.T. Learning mechanisms in the temporal lobe visual cortex. Behav Brain Res 66 (1995) 177-185
26. Rainer G., Lee H., and Logothetis N.K. The effect of learning on the function of monkey extrastriate visual cortex. PLoS Biol 2 (2004) E44. This study, in combination with previous findings (Rainer et al., [28]), suggests that different neural plasticity mechanisms in early visual areas (V4) and the prefrontal cortex mediate enhanced behavioural performance in discriminating degraded images after training. In particular, stimulus degradation by increasing noise resulted in decreased responses of V4 neurons to novel images, whereas learning enhanced their responses to degraded stimuli. By contrast, the number of prefrontal neurons that responded to undegraded images decreased as these images became familiar with training, whereas their object selectivity increased (narrowed tuning) and generalized across degradation levels.
27. Sigala N., and Logothetis N.K. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature 415 (2002) 318-320
28. Rainer G., and Miller E.K. Effects of visual experience on the representation of objects in the prefrontal cortex. Neuron 27 (2000) 179-189
29. Baker C.I., Behrmann M., and Olson C.R. Impact of learning on representation of parts and wholes in monkey inferotemporal cortex. Nat Neurosci 5 (2002) 1210-1216
30. Freedman D.J., Riesenhuber M., Poggio T., and Miller E.K. A comparison of primate prefrontal and inferior temporal cortices during visual categorization. J Neurosci 23 (2003) 5235-5246
31. Yang T., and Maunsell J.H. The effect of perceptual learning on neuronal responses in monkey visual area V4. J Neurosci 24 (2004) 1617-1626. This paper is the latest in a series of impressively thorough monkey neurophysiological studies of perceptual learning effects along the ventral stream (V1, V2 and V4) [21]. In these studies the authors systematically examine a large range of potential single neuron effects of orientation discrimination training. The overall conclusion is that, whereas all effects in V1 and V2 are small (especially when compared with results in other primary sensory areas), V4 neurons near the trained visual field location and stimulus orientation show moderately sharper orientation selectivity and better population discrimination performance.
32. Logothetis N.K., Pauls J., and Poggio T. Shape representation in the inferior temporal cortex of monkeys. Curr Biol 5 (1995) 552-563
33. Jagadeesh B., Chelazzi L., Mishkin M., and Desimone R. Learning increases stimulus salience in anterior inferior temporal cortex of the macaque. J Neurophysiol 86 (2001) 290-303
34. Lee T.S., Yang C.F., Romero R.D., and Mumford D. Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency. Nat Neurosci 5 (2002) 589-597
35. van Turennout M., Ellmore T., and Martin A. Long-lasting cortical plasticity in the object naming system. Nat Neurosci 3 (2000) 1329-1334
36. Grill-Spector K., Kushnir T., Hendler T., and Malach R. The dynamics of object-selective activation correlate with recognition performance in humans. Nat Neurosci 3 (2000) 837-843
37. Schiltz C., Bodart J.M., Dubois S., Dejardin S., Michel C., Roucoux A., Crommelinck M., and Orban G.A. Neuronal mechanisms of perceptual learning: changes in human brain activity with training in orientation discrimination. Neuroimage 9 (1999) 46-62
38. Dolan R.J., Fink G.R., Rolls E., Booth M., Holmes A., Frackowiak R.S., and Friston K.J. How the brain learns to see objects and faces in an impoverished context. Nature 389 (1997) 596-599
39. Gauthier I., Tarr M.J., Anderson A.W., Skudlarski P., and Gore J.C. Activation of the middle fusiform 'face area' increases with expertise in recognizing novel objects. Nat Neurosci 2 (1999) 568-573
40. Furmanski C.S., Schluppeck D., and Engel S.A. Learning strengthens the response of primary visual cortex to simple patterns. Curr Biol 14 (2004) 573-578. This study provides an elegant example of the link between experience-dependent behavioral improvement and neural plasticity in the primary visual cortex. In particular, after training with oblique orientation patterns, behavioral performance in the discrimination of these patterns improved and fMRI responses in V1 increased at magnitudes similar to those for cardinal orientation patterns before training.
41. Kourtzi Z., Betts L.R., Sarkheil P., and Welchman A.E. Distributed neural plasticity for shape learning in the human visual cortex. PLoS Biol 3 (2005) e204. The authors show that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations and optimising neural coding for the task context.
42. Edelman S. Representation and Recognition in Vision (1999), MIT Press
43. Riesenhuber M., and Poggio T. Hierarchical models of object recognition in cortex. Nat Neurosci 2 (1999) 1019-1025
44. Ullman S. High Level Vision (1996), MIT Press
45. Barlow H. The neuron doctrine in perception. In: Gazzaniga (Ed). The Cognitive Neurosciences (1995), MIT Press 415-435
46. Fukushima K. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36 (1980) 193-202
47. Fiser J., and Aslin R.N. Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychol Sci 12 (2001) 499-504
48. Chun M.M. Contextual cueing of visual attention. Trends Cogn Sci 4 (2000) 170-178
49. Simoncelli E.P. Vision and the statistics of the visual environment. Curr Opin Neurobiol 13 (2003) 144-149
50. Messinger A., Squire L.R., Zola S.M., and Albright T.D. Neural correlates of knowledge: stable representation of stimulus associations across variations in behavioral performance. Neuron 48 (2005) 359-371. This study provides evidence for a novel class of inferotemporal neurons that represent arbitrary associations between pairs of stimuli independent of whether the animal chooses the correct remembered visual stimulus or not. These findings suggest that knowledge in the context of memorized associations is represented in the inferior temporal cortex regardless of its role and weight in behavioral decisions.
51. Messinger A., Squire L.R., Zola S.M., and Albright T.D. Neuronal representations of stimulus associations develop in the temporal lobe during learning. Proc Natl Acad Sci USA 98 (2001) 12239-12244
52. Rosenthal O., Fusi S., and Hochstein S. Forming classes by stimulus frequency: behavior and theory. Proc Natl Acad Sci USA 98 (2001) 4265-4270
53. Freedman DJ, Riesenhuber M, Poggio T, Miller EK: Experience-dependent sharpening of visual shape selectivity in inferior temporal cortex. Cereb Cortex 2005. In press. This neurophysiological study provides evidence for enhanced performance in object learning in the context of a categorization task linked to improved neural selectivity in IT. Interestingly, these learning effects were specific to the trained object orientation and were observed not only for objects with which the monkeys were explicitly trained but also for stimuli to which the monkeys were passively exposed. These findings suggest that similar neural plasticity mechanisms that result in the sharpening of neural responses might underlie learning through supervised training or passive experience.
54. Wallis G., and Bulthoff H.H. Effects of temporal association on recognition memory. Proc Natl Acad Sci USA 98 (2001) 4800-4804
55. Stringer S.M., Perry G., Rolls E.T., and Proske J.H. Learning invariant object recognition in the visual system with continuous transformations. Biol Cybern 94 (2006) 128-142
56. DiCarlo J.J., and Maunsell J.H.R. Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. J Neurophysiol 89 (2003) 3264-3278
57. Kourtzi Z., and Shiffrar M. One-shot view invariance in a moving world. Psychol Sci 8 (1997) 461-466
58. Cox D.D., Meier P., Oertelt N., and DiCarlo J.J. 'Breaking' position-invariant object recognition. Nat Neurosci 8 (2005) 1145-1147. In normal, real-world viewing, the same object is seen at different positions across short time intervals as the eyes rapidly explore the visual world. To test the idea that this experience might underlie the position-tolerance of recognition, the authors introduced undetected changes in object identity during eye movements. This manipulation disrupted the position tolerance of object recognition in healthy adult observers. This shows that position tolerance is plastic in adults, and suggests that even this bedrock property of primate object recognition might be acquired or maintained by natural visual experience.
59. Wang G., Obama S., Yamashita W., Sugihara T., and Tanaka K. Prior experience of rotation is not required for recognizing objects seen from different angles. Nat Neurosci 8 (2005) 1768-1775
60. Rolls E.T., Aggelopoulos N.C., and Zheng F. The receptive fields of inferior temporal cortex neurons in natural scenes. J Neurosci 23 (2003) 339-348
61. Sheinberg D.L., and Logothetis N.K. Noticing familiar objects in real world scenes: the role of temporal cortical neurons in natural vision. J Neurosci 21 (2001) 1340-1350
62. Gold J., Bennett P.J., and Sekuler A.B. Signal but not noise changes with perceptual learning. Nature 402 (1999) 176-178
63. Brady M.J., and Kersten D. Bootstrapped learning of novel objects. J Vis 3 (2003) 413-422
64. Dosher B.A., and Lu Z.L. Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proc Natl Acad Sci USA 95 (1998) 13988-13993
65. Li R.W., Levi D.M., and Klein S.A. Perceptual learning improves efficiency by re-tuning the decision 'template' for position discrimination. Nat Neurosci 7 (2004) 178-183
66. Eckstein M.P., Abbey C.K., Pham B.T., and Shimozaki S.S. Perceptual learning through optimization of attentional weighting: human versus optimal bayesian learner. J Vis 4 (2004) 1006-1019. This paper introduces a new experimental paradigm into the study of perceptual learning by comparing human and optimal Bayesian learners. The results suggest that humans learn to localize targets with uncertainty about orientation and polarity within a brief sequence of trials, but they rely more on previous decisions than on feedback, resulting in slower and more incomplete learning than that of an ideal observer.
67. Barlow H. Conditions for versatile learning, Helmholtz's unconscious inference, and the task of perception. Vision Res 30 (1990) 1561-1571
68. Cox D., Meyers E., and Sinha P. Contextually evoked object-specific responses in human visual cortex. Science 304 (2004) 115-117
69. Bar M., and Aminoff E. Cortical analysis of visual context. Neuron 38 (2003) 347-358
70. Zoccolan D., Cox D.D., and DiCarlo J.J. Multiple object response normalization in monkey inferotemporal cortex. J Neurosci 25 (2005) 8150-8164
71. Aggelopoulos N.C., and Rolls E.T. Scene perception: inferior temporal cortex neurons encode the positions of different objects in the scene. Eur J Neurosci 22 (2005) 2903-2916
72. Crist R.E., Li W., and Gilbert C.D. Learning to see: experience and attention in primary visual cortex. Nat Neurosci 4 (2001) 519-525
73. Schoups A.A., Vogels R., and Orban G.A. Human perceptual learning in identifying the oblique orientation: retinotopy, orientation specificity and monocularity. J Physiol 483 (1995) 797-810
74. Schwartz S., Maquet P., and Frith C. Neural correlates of perceptual learning: a functional MRI study of visual texture discrimination. Proc Natl Acad Sci USA 99 (2002) 17137-17142
75. Crist R.E., Li W., and Gilbert C.D. Learning to see: experience and attention in primary visual cortex. Nat Neurosci 4 (2001) 519-525
76. Sigman M., Pan H., Yang Y., Stern E., Silbersweig D., and Gilbert C.D. Top-down reorganization of activity in the visual pathway after learning a shape identification task. Neuron 46 (2005) 823-835. The authors report large-scale reorganization across cortical networks for task-dependent learning. Learning to detect a target among distractors in the context of a visual search task resulted in increased activations in retinotopic areas By contrast, decreased activations were observed both in higher occipitotemporal areas involved in shape analysis and in frontal and parietal areas involved in attentional processing.
77. Ahissar M., and Hochstein S. The reverse hierarchy theory of visual perceptual learning. Trends Cogn Sci 8 (2004) 457-464. This review highlights psychophysical and physiological evidence that learning begins at higher stages of visual analysis and proceeds in a top-down manner to earlier stages when finer analysis of the stimulus is necessary.
78. Buchel C., Coull J.T., and Friston K.J. The predictive value of changes in effective connectivity for human learning. Science 283 (1999) 1538-1541
79. McIntosh A.R., Rajah M.N., and Lobaugh N.J. Interactions of prefrontal cortex in relation to awareness in sensory learning. Science 284 (1999) 1531-1533
80. Li W., Piech V., and Gilbert C.D. Perceptual learning and top-down influences in primary visual cortex. Nat Neurosci 7 (2004) 651-657
81. Sagi D., and Tanne D. Perceptual learning: learning to see. Curr Opin Neurobiol 4 (1994) 195-199
82. Sigman M., and Gilbert C.D. Learning to find a shape. Nat Neurosci 3 (2000) 264-269