弱视和肝豆状核变性患者视觉认知功能损害机制的噪音与模型分析研究
|
摘要
引入外部视觉噪音，测量视觉系统在受噪音干扰时对信号的检测能力是近年来在心理物理学研究中逐渐流行起来的实验方法。外部噪音的引入使得我们可以全面的研究视觉系统信号检测能力受到外部噪音影响的情况，从而得出信号检测阈值与外部噪音对比度之间的函数关系(TvC曲线)。在此基础之上，再通过模型分析求出视觉系统的一些内部组成成分，如内部加法噪音，内部乘法噪音，视觉模板效能，非线性传递因子等，从而进一步加深我们对视觉系统信号处理功能的了解。在本文中，我们运用引入外部噪音的方法，配以PTM模型分析，对弱视及肝豆状核变性患者视觉信号检测能力损害的机制进行了系统性分析，同时对弱视患者二阶运动信号的检测，以及肝豆状核变性患者知觉学习和分类学习之间的关系也作了相应研究。
弱视是视觉发育敏感期异常视觉经验所导致的以空间视力损害为特征的一组视力不良综合征。其视功能损害涉及多个脑区并与皮层神经元空间特性的异常密切相关。我们共在8个噪音度(噪音对比度0-0.33)以及两个正确率(d’=1.09和d’=1.63)下分别测量了弱视患者与正常被试中央视野辨别Gabor信号朝向的对比度阈值。实验结果显示，在空间频率2.3c／d时，弱视的主要损害表现为在低噪音段的信号检测能力远弱于正常被试(弱视眼阈值比正常被试高169％)，PTM模型分析显示这主要是由于弱视造成了视觉系统内部加法噪音的增高。弱视患者的成绩在高噪音段也表现出一定程度的受损(弱视眼阈值比正常被试高56％)，这在PTM模型中表现为视觉模板的滤波能力相应的降低。且随着需检测信号的空间频率从1.5c／d逐渐增加至4.6c／d，弱视眼在高噪音段的损伤增加幅度要高于其在低噪音段的增加幅度，显示弱视视觉模板随空间频率的提高损害程度大于内部加法噪音的增加。此外，我们还发现弱视眼的乘法噪音和非线性系统与正常被试相比并未受到明显损害。
在屈光参差性弱视患者对一阶运动和二阶运动信号的检测阈值测量实验中，我们在5个时间频率下分别测量了弱视眼与健康眼在未平衡载体(carrier)输入时与平衡载体输入后的二阶运动信号检测阈值。结果表明，与正常眼相比，弱视眼对未经载体平衡的二阶运动信号检测存在损伤，但其对比敏感度降低幅度(平均80％)小于其一阶信号降低幅度(平均150％)；同时弱视眼对经过载体平衡的二阶运动信号的检测能力与健康眼相当。我们的实验结果说明，弱视眼对二阶运动信号的检测能力并未受到明显的损伤，其在未平衡载体时表现出的能力受损主要是来自于其对载体信号的一阶空间信号检测能力的损伤。
肝豆状核变性又称Wilson病，是一种常染色体隐性遗传的铜代谢障碍疾病，由于铜在体内过度蓄积，损害肝、脑等器官而导致多种临床症状。肝豆状核变性患者大脑中的损害位点主要在基底节，通过对其进行知觉学习和分类学习实验，我们可以观察到两类学习处理过程与基底节的联系，并研究两类学习之间的相关性。我们的实验结果显示，肝豆状核变性患者的显性和隐性分类学习都受到损害，其成绩(显性分类达标正确率73.0％，隐性分类达标正确率65.3％)显著低于正常被试(显性分类达标正确率83.1％，隐性分类达标正确率72.7％)，进一步证实了这两类分类学习过程都需要基底节的参与。同时在知觉学习任务中，患者在低噪音下的知觉学习能力与正常被试相比无显著性差异，而在高噪音下的知觉学习能力则有明显损伤(肝豆状核变性患者阈值降低2.21dB，正常被试阈值降低4.66dB)，PTM模型分析显示肝豆状核变性患者视觉模板滤波能力降低，且此结果可能与基底节的损伤有关。此外我们还发现，高噪音下的知觉学习成绩与隐性分类学习有着显著性的相关，预示两类学习之间有着一些共同的神经机制。
引入外部噪音进行建模分析，通过这种有效的研究方法我们可以获得更丰富的实验信息，帮助我们在更广泛的视觉研究工作中深入的了解其内在的机制。
Currently, external noise is frequently used by psychologists to measure the properties of a wide range of perceptual process. The basic idea of this methos is to estimate the amount of internal noise and characteristics of the perceptual processes by studying how performance in some task is affected by experimenter-manipulated external noise. In this paper, we apply the external noise method and a Perceptucal Template Model (PTM) to identify the mechanism of the visual deficit in amblyopia and Wilson's Disease patients. In addition, we also studied the detection threshold of second-order motion signal in amblyopia and the relationship of perceptual learning and category learning in Wilson's Disease patients.
Amblyopia is a developmental visual disorder characterized by reduced vision in the absence of any detectable structural or pathological abnormalities that does not improve with refractive correction. In our experiment, amblyopic observers performed a Gabor orientation identification task in fovea with 8 level white external noises (contrast 0-0.33) added. Threshold versus external noise contrast (TvC) functions were measured at two performance criterion levels (d'=1.09 and d'=1.63). For a subset of observers, we also manipulated the center spatial frequency of the Gabor from spatial frequency 1.5-4.6 c/d. With PTM model analysis, we found that two independent factors contributed to amblyopic deficits: (1) increased additive internal noise, and (2) deficient perceptual templates. Whereas increased additive noise underlay performance deficits in all spatial frequencies, the degree of perceptual template deterioration increased with the center spatial frequency of the Gabor. In addition, we found that amblyopia did not affect the non-linear transducer and multiplicative noise.
In the first-order and second-order motion detection experiments, we measured the sensitivity of first-order and second-order motion of sinusoidal grating signal in eight anisometropic amblyopes and ten normal observers. The measurement of second-order motion was performed in two conditions: with non-equated visible carrier and with equated visible carrier. The amblyopic eyes of amblyopes showed deficit in both perceptions of first-order and second-order motion with non-equated carrier, relative to their fellow eyes and to normal eyes. But with the equated carrier, we revealed the second order motion perception was normal for the amblyopic eyes, which means the deficit of second-order motion in amblyopic eye could be a sequence of its first-order detection loss.
Wilson's disease (WD), hepatolenticular degeneration, is an autosomal recessively inherited disorder of copper metabolism. In this suty, we evaluated explicit and implicit category learning and perceptual learning in subjects with treated Wilson's disease and normal controls. The WD subjects exhibited deficits in both forms of category learning as well as perceptual learning in high external noise. However, their perceptual learning in low external noise was relatively spared. Furthermore, there were significant correlations between perceptual learning in high external noise and both forms of category learning, but perceptual learning in low external noise was not significantly correlated with either form of category learning. The results suggest that damages to brain structures in Wilson's disease that are important for category learning may lead to poor perceptual learning in high external noise, but may spare perceptual learning in low external noise; perceptual learning in high and low external noise environments may be served by different brain structures.
With the method of systematic variations of environmental noise in signal detection task and quantification in a model analysis, increasing information can be acquired in our research experiments and new mechanisms of visual processing can be revealed in future.
弱视是视觉发育敏感期异常视觉经验所导致的以空间视力损害为特征的一组视力不良综合征。其视功能损害涉及多个脑区并与皮层神经元空间特性的异常密切相关。我们共在8个噪音度(噪音对比度0-0.33)以及两个正确率(d’=1.09和d’=1.63)下分别测量了弱视患者与正常被试中央视野辨别Gabor信号朝向的对比度阈值。实验结果显示，在空间频率2.3c／d时，弱视的主要损害表现为在低噪音段的信号检测能力远弱于正常被试(弱视眼阈值比正常被试高169％)，PTM模型分析显示这主要是由于弱视造成了视觉系统内部加法噪音的增高。弱视患者的成绩在高噪音段也表现出一定程度的受损(弱视眼阈值比正常被试高56％)，这在PTM模型中表现为视觉模板的滤波能力相应的降低。且随着需检测信号的空间频率从1.5c／d逐渐增加至4.6c／d，弱视眼在高噪音段的损伤增加幅度要高于其在低噪音段的增加幅度，显示弱视视觉模板随空间频率的提高损害程度大于内部加法噪音的增加。此外，我们还发现弱视眼的乘法噪音和非线性系统与正常被试相比并未受到明显损害。
在屈光参差性弱视患者对一阶运动和二阶运动信号的检测阈值测量实验中，我们在5个时间频率下分别测量了弱视眼与健康眼在未平衡载体(carrier)输入时与平衡载体输入后的二阶运动信号检测阈值。结果表明，与正常眼相比，弱视眼对未经载体平衡的二阶运动信号检测存在损伤，但其对比敏感度降低幅度(平均80％)小于其一阶信号降低幅度(平均150％)；同时弱视眼对经过载体平衡的二阶运动信号的检测能力与健康眼相当。我们的实验结果说明，弱视眼对二阶运动信号的检测能力并未受到明显的损伤，其在未平衡载体时表现出的能力受损主要是来自于其对载体信号的一阶空间信号检测能力的损伤。
肝豆状核变性又称Wilson病，是一种常染色体隐性遗传的铜代谢障碍疾病，由于铜在体内过度蓄积，损害肝、脑等器官而导致多种临床症状。肝豆状核变性患者大脑中的损害位点主要在基底节，通过对其进行知觉学习和分类学习实验，我们可以观察到两类学习处理过程与基底节的联系，并研究两类学习之间的相关性。我们的实验结果显示，肝豆状核变性患者的显性和隐性分类学习都受到损害，其成绩(显性分类达标正确率73.0％，隐性分类达标正确率65.3％)显著低于正常被试(显性分类达标正确率83.1％，隐性分类达标正确率72.7％)，进一步证实了这两类分类学习过程都需要基底节的参与。同时在知觉学习任务中，患者在低噪音下的知觉学习能力与正常被试相比无显著性差异，而在高噪音下的知觉学习能力则有明显损伤(肝豆状核变性患者阈值降低2.21dB，正常被试阈值降低4.66dB)，PTM模型分析显示肝豆状核变性患者视觉模板滤波能力降低，且此结果可能与基底节的损伤有关。此外我们还发现，高噪音下的知觉学习成绩与隐性分类学习有着显著性的相关，预示两类学习之间有着一些共同的神经机制。
引入外部噪音进行建模分析，通过这种有效的研究方法我们可以获得更丰富的实验信息，帮助我们在更广泛的视觉研究工作中深入的了解其内在的机制。
Currently, external noise is frequently used by psychologists to measure the properties of a wide range of perceptual process. The basic idea of this methos is to estimate the amount of internal noise and characteristics of the perceptual processes by studying how performance in some task is affected by experimenter-manipulated external noise. In this paper, we apply the external noise method and a Perceptucal Template Model (PTM) to identify the mechanism of the visual deficit in amblyopia and Wilson's Disease patients. In addition, we also studied the detection threshold of second-order motion signal in amblyopia and the relationship of perceptual learning and category learning in Wilson's Disease patients.
Amblyopia is a developmental visual disorder characterized by reduced vision in the absence of any detectable structural or pathological abnormalities that does not improve with refractive correction. In our experiment, amblyopic observers performed a Gabor orientation identification task in fovea with 8 level white external noises (contrast 0-0.33) added. Threshold versus external noise contrast (TvC) functions were measured at two performance criterion levels (d'=1.09 and d'=1.63). For a subset of observers, we also manipulated the center spatial frequency of the Gabor from spatial frequency 1.5-4.6 c/d. With PTM model analysis, we found that two independent factors contributed to amblyopic deficits: (1) increased additive internal noise, and (2) deficient perceptual templates. Whereas increased additive noise underlay performance deficits in all spatial frequencies, the degree of perceptual template deterioration increased with the center spatial frequency of the Gabor. In addition, we found that amblyopia did not affect the non-linear transducer and multiplicative noise.
In the first-order and second-order motion detection experiments, we measured the sensitivity of first-order and second-order motion of sinusoidal grating signal in eight anisometropic amblyopes and ten normal observers. The measurement of second-order motion was performed in two conditions: with non-equated visible carrier and with equated visible carrier. The amblyopic eyes of amblyopes showed deficit in both perceptions of first-order and second-order motion with non-equated carrier, relative to their fellow eyes and to normal eyes. But with the equated carrier, we revealed the second order motion perception was normal for the amblyopic eyes, which means the deficit of second-order motion in amblyopic eye could be a sequence of its first-order detection loss.
Wilson's disease (WD), hepatolenticular degeneration, is an autosomal recessively inherited disorder of copper metabolism. In this suty, we evaluated explicit and implicit category learning and perceptual learning in subjects with treated Wilson's disease and normal controls. The WD subjects exhibited deficits in both forms of category learning as well as perceptual learning in high external noise. However, their perceptual learning in low external noise was relatively spared. Furthermore, there were significant correlations between perceptual learning in high external noise and both forms of category learning, but perceptual learning in low external noise was not significantly correlated with either form of category learning. The results suggest that damages to brain structures in Wilson's disease that are important for category learning may lead to poor perceptual learning in high external noise, but may spare perceptual learning in low external noise; perceptual learning in high and low external noise environments may be served by different brain structures.
With the method of systematic variations of environmental noise in signal detection task and quantification in a model analysis, increasing information can be acquired in our research experiments and new mechanisms of visual processing can be revealed in future.
引文
1. Kersten, D., R. F. Hess, and G.T. Plant, Assenssing contrast sensitivity behind cloudy media. Clininal Vision Science, 1988.2: p. 143-158.
2. Nordmann, J. P., R. D. Freeman, and C. Casanova, Contrast sensitivity in amblyopia: masking effects of noise. Invest Ophthalmol Vis Sci, 1992.33(10): p. 2975-85.
3. Schofield, A. J. and M.A. Georgeson, Sensitivity to modulations of luminance and contrast in visual white noise: separate mechanisms with similar behaviour. Vision Res, 1999.39(16): p. 2697-716.
4. Levi, D. M. and S. A. Klein, Noise provides some new signals about the spatial vision of amblyopes. J Neurosci, 2003.23(7): p. 2522-6.
5. Pelli, D. G, D. M. Levi, and S.T. Chung, Using visual noise to characterize amblyopic letter identification. J Vis, 2004. 4(10): p. 904-20.
6. Dosher, B. A. and Z. L. Lu, Mechanisms of perceptual learning. Vision Res, 1999. 39(19): p. 3197-221.
7. Lu, Z. L. and B.A. Dosher, External noise distinguishes attention mechanisms. Vision Res, 1998.38(9): p. 1183-98.
8. Dosher, B. A. and Z. L. Lu, Mechanisms of perceptual attention in precuing of location. Vision Res, 2000. 40(10-12): p. 1269-92.
9. Dosher, B. A. and Z. L. Lu, Noise exclusion in spatial attention. Psychol Sci, 2000. 11(2): p. 139-46.
10. Lesmes, L. A., et al., Bayesian adaptive estimation of threshold versus contrast external noise functions: the quick TvC method. Vision Res, 2006.46(19): p. 3160-76.
11. Lu, Z. L. and B. A. Dosher, Characterizing human perceptual inefficiencies with equivalent internal noise. J Opt Soc Am A Opt Image Sci Vis, 1999. 16(3): p. 764-78.
12. Pelli, D.G. and B. Farell, Why use noise? J Opt Soc Am A Opt Image Sci Vis, 1999. 16(3): p. 647-53.
13. Pelli, D. G., Effects of visual noise. PhD. disseration, 1981(University of Cambridge, Cambridge, England).
14. Lu, Z.L., C.Q. Liu, and B.A. Dosher, Attention mechanisms for multi-location first-and second-order motion perception. Vision Res, 2000. 40(2): p. 173-86.
15. Ohlsson, J., Defining amblyopia: the need for a joint classification. Strabismus, 2005.13(1): p. 15-20.
16. Ciuffreda KJ, Levi DM, and A. Selenow, Amblyopia: Basic and Clinical Aspects. 1991, Boston: Butterworth-Heinemann.
17. Barrett, B.T., A. Bradley, and P.V. McGraw, Understanding the neural basis of amblyopia. Neuroscientist, 2004. 10(2): p. 106-17.
18. Campos, E. C., M. L. Prampolini, and R. Gulli, Contrast sensitivity differences between strabismic and anisometropic amblyopia: objective correlate by means of visual evoked responses. Doc Ophthalmol, 1984. 58(1): p. 45-50.
19. Choi, M. Y., et al., Comparison between anisometropic and strabismic amblyopia using functional magnetic resonance imaging. Br J Ophthalmol, 2001.85(9): p. 1052-6.
20. McKee, S.P., D.M. Levi, and J.A. Movshon, The pattern of visual deficits in amblyopia. J Vis, 2003.3(5): p. 380-405.
21. McKee, S.P., et al., The classification of amblyopia on the basis of visual and oculomotor performance. Trans Am Ophthalmol Soc, 1992.90: p. 123-44; discussion 145-8.
22. Delint, P.J., et al., Photoreceptor function in unilateral amblyopia. Vision Res, 1998.38(4): p. 613-7.
23. Zhou, Y., A. G. Leventhal, and K.G. Thompson, Visual deprivation does not affect the orientation and direction sensitivity of relay cells in the lateral geniculate nucleus of the cat. J Neurosci, 1995.15(1 Pt 2): p. 689-98.
24. Levi, D. M. and S. A. Klein, Limitations on position coding imposed by undersampling and univariance. Vision Res, 1996.36(14): p. 2111-20.
25. Levi, D. M., S. A. Klein, and V. Sharma, Position jitter and undersampling in pattern perception. Vision Res, 1999. 39(3): p. 445-65.
26. Hess, R. F, Y. Z. Wang, and S.C. Dakin, Are judgements of circularity local or global? Vision Res, 1999. 39(26): p. 4354-60.
27. Polat, U. and Y. Bonneh, Collinear interactions and contour integration. Spat Vis, 2000. 13(4): p. 393-401.
28. Saarinen, J. and D. M. Levi, Integration of local features into a global shape. Vision Res, 2001. 41(14): p. 1785-90.
29. Chandna, A., et al., Contour integration deficits in anisometropic amblyopia. Invest Ophthalmol Vis Sci, 2001.42(3): p. 875-8.
30. Meese, T.S., R.F. Hess, and C. B. Williams, Spatial coherence does not affect contrast discrimination for multiple Gabor stimuli. Perception, 2001.30(12): p. 1411-22.
31. Hess, R. F., et al., Contour interaction in amblyopia: scale selection. Vision Res, 2001.41(17): p. 2285-96.
32. Crewther, D. P. and S.G. Crewther, Neural site of strabismic amblyopia in cats: spatial frequency deficit in primary cortical neurons. Exp Brain Res, 1990.79(3): p. 615-22.
33. Kossel, A., S. Lowel, and J. Bolz, Relationships between dendritic fields and functional architecture in striate cortex of normal and visually deprived cats. J Neurosci, 1995.15(5 Pt 2): p. 3913-26.
34. Kiorpes, L., et al., Neuronal correlates of amblyopia in the visual cortex of macaque monkeys with experimental strabismus and anisometropia. J Neurosci, 1998. 18(16): p. 6411-24.
35. Sireteanu, R., The binocular visualsystem in amblyopia. Strabismus, 2000.8(I): p. 39-51.
36. Kiorpes, L. and S. P. McKee, Neural mechanisms underlying amblyopia. Curr Opin Neurobiol, 1999. 9: p. 480-486.
37. Smith, E.L., 3rd, et al., Residual binocular interactions in the striate cortex of monkeys reared with abnormal binocular vision. J Neurophysiol, 1997.78(3): p. 1353-62.
38. Roelfsema, P. R., et al., Reduced synchronization in the visual cortex of cats with strabismic amblyopia. Eur J Neurosci, 1994.6(11): p. 1645-55.
39. Schroder, J.H., et al., Ocular dominance in extrastriate cortex of strabismic amblyopic cats. Vision Res, 2002.42(1): p. 29-39.
40. Imamura, K., et al., Reduced activity in the extrastriate visual cortex of individuals with strabismic amblyopia. Neurosci Lett, 1997. 225(3): p. 173-6.
41. Hess, R. F., Amblyopia: site unseen. Clin Exp Optom, 2001.84(6): p. 321-336.
42. Goodyear, B.G., et al., BOLD fMRI response of early visual areas to perceived contrast in human amblyopia. J Neurophysiol, 2000. 84(4): p. 1907-13.
43. Barnes, G.R., et al., The cortical deficit in humans with strabismic amblyopia. J Physiol, 2001. 533(Pt 1): p. 281-97.
44. Lee, K.M., et al., Binocularity and spatial frequency dependence of calcarine activation in two types of amblyopia. Neurosci Res, 2001.40(2): p. 147-53.
45. Curtis, L., J. Baker, and I. Mareschal, Processing of second-order stimuli in the visual cortex. Progress in Brain Research, 2001. 134: p. 1-21.
46. Zhou, Y.X. and C.L. Baker, Jr., A processing stream in mammalian visual cortex neurons for non-Fourier responses. Science, 1993.261(5117): p. 98-101.
47. Zhou, Y.X. and C. L. Baker, Jr., Envelope-responsive neurons in areas 17 and 18 of cat. J Neurophysiol, 1994.72(5): p. 2134-50.
48. Zhou, Y.X. and C.L. Baker, Jr., Spatial properties of envelope-responsive cells in area 17 and 18 neurons of the cat. J Neurophysiol, 1996.75(3): p. 1038-50.
49. Mareschal, I. and C.L. Baker, Jr., Temporal and spatial response to second-order stimuli in cat area 18. J Neurophysiol, 1998.80(6): p. 2811-23.
50. Mareschal, I. and C.L. Baker, Jr., Cortical processing of second-order motion. Vis Neurosei, 1999. 16(3): p. 527-40.
51. Leventhal, A. G., et al., Neural correlates of boundary perception. Vis Neurosci, 1998. 15(6): p. 1107-18.
52. Albright, T.D., Form-cue invariant motion processing in primate visual cortex. Science, 1992. 255(5048): p. 1141-3.
53. Henning, G.B., B.G. Hertz, and D.E. Broadbent, Some experiments bearing on the hypothesis that the visual system analyses spatial patterns in independent bands of spatial frequency. Vision Res, 1975: p. 887-897.
54. Nishida, S., T. Ledgeway, and M. Edwards, Dual multiple-scale processing for motion in the human visual system. Vision Res, 1997. 37(19): p. 2685-98.
55. Goldstone, R. L., Perceptual learning. Annu Rev Psychol, 1998.49: p. 585-612.
56. Ciuffreda, K., L. DM, and S. A, Amblyopia: Basic and Clinical Aspects. 1991, Boston: Butterworth-Heinemann.
57. McKee, S. P., D.M. Levi, and J. A. Movshon, The pattern of visual deficits in amblyopia. Journal of Vision, 2003. 3(5): p. 380-405.
58. Eggers, H.M. and C. Blakemore, Physiological basis of anisometropic amblyopia. Science, 1978. 201(4352): p. 264-7.
59. Movshon, J. A., et al., Effects of early unilateral blur on the macaque's visual system. Ⅲ. Physiological observations. J Neurosci, 1987.7(5): p. 1340-51.
60. Sireteanu, R., et al., Cortical cite of the amblyopic deficit in strabismic and anisometropic subjects, investigated with fMRI, nvestigative Ophthalmology and Visual Science, 1998.39(4): p. s909.
61. Daw, N., Critical periods and amblyopia. Arch Ophthalmol, 1998. 116: p. 502-505.
62. Rose, A., The sensitivity performance of the human eye on an absolute scale. Journal of the Optical Society of America, 1948.38: p. 196-208.
63. Barlow, H. B., Retinal noise and absolute threshold Journal of the Optical Society of America, 1956.46: p. 634-639.
64. Tanner, W.P., Jr. and T.G. Birdsall, Definitions of d' and n as psychophysical measures. Journal of the Acoustical Society of America, 1958.30: p. 922-928.
65. Van Meeteren, A. and H. B. Barlow, The statistical effciency for detecting sinusoidal modulation of average dot density in random figures. Vision Research, 1981. 21(6): p. 765-777.
66. Nagaraja, N.S., Effect of luminance noise on contrast thresholds. Journal of the Optical Society of America, 1964.54(7): p. 950-955.
67. Montero, V. M., Amblyopia decreases activation of the corticogeniculate pathway and visual thalamic reticularis in attentive rats: a 'focal attention' hypothesis. Neuroscience, 1999. 91(3): p. 805-17.
68. Ahumada, A.J. and A. B. Watson, Equivalent-noise model for contrast detection and discrimination. Journal of the Optical Society of America A, 1985.2(7): p. 1133-1139.
69. Legge, G. E., D. Kersten, and A. E. Burgess, Contrast discrimination in noise. Journal of the Optical Society of America. A, 1987.4(2): p. 391-404.
70. Geisler, W.S., Sequential ideal-observer analysis of visual discriminations. Psychological Review, 1989.96(2): p. 267-314.
71. Pelli, D. G., The quantum efficiency of vision, in Vision: Coding and Effciency, C. Blakemore, Editor. 1990, Cambridge University Press: Cambridge, UK. p. 3-24.
72. Gegenfurtner, K. R. and D. C. Kiper, Contrast detection in luminance and chromatic noise. Journal of the Optical Society of America A, 1992. 9(11): p. 1880-1888.
73. D'Zmura, M. and K. Knoblauch, Spectral bandwidths for the detection of color. Vision Research, 1998.38(20): p. 3117-3128.
74. Tjan, B. S., et al., Human efficiency for recognizing 3-D objects in luminance noise. Vision Research, 1995.35(21): p. 3053-3069.
75. Baker, C.L., Jr., Central neural mechanisms for detecting second-order motion. Curt Opin Neurobiol, 1999. 9(4): p. 461-6.
76. Hay, G. A. and M. S. Chesters, Signal-transfer functions in threshold and suprathreshold vision. Journal of the Optical Society of America, 1972.62: p. 990-998.
77. Ahumada, A.J., Puing the visual system noise back in the picture. Journal of the Optical Society of America, 1987.4: p. 2372-2378.
78. Burgess, A. E., et al., Effciency of human visual signal discrimination. Science, 1981. 214(4516): p. 93-94.
79. Pelli, D.G., Uncertainty explains many aspects of visual contrast detection and discrimination. Journal of the Optical Society of America, A, 1985.2: p. 1508-1532.
80. Burgess, A. E. and B. Colborne, Visual signal detection. Ⅳ. Observer inconsistency. J Opt Soc Am A, 1988.5(4): p. 617-27.
81. Eckstein, M. P., A.J. Ahumada, Jr., and A. B. Watson, Visual signal detection in structured backgrounds: Ⅱ. Effects of contrast gain control, background variations, and white noise. Journal of the Optical Society of America, 1997.14(9): p. 2406-2419.
82. Burgess, A. E., R. Shaw, and J. Lubin, Noise in image Processing. Journal of the Optical Society of America A [Special issue], 1999(16).
83. Dosher, B. A., et al., The spatial window of the perceptual template and endogenous attention. Vision Res, 2004.44(12): p. 1257-71.
84. Arrighi, R., D. Alais, and D. Burr, Perceived timing of first- and second-order changes in vision and hearing. Exp Brain Res, 2005. 166(3-4): p. 445-54.
85. Gold, J., P.J. Bennett, and A.B. Sekuler, Signal but not noise changes with perceptual learning. Nature, 1999. 402(6758): p. 176-8.
86. Dao, D.Y., Z.L. Lu, and B.A. Dosher, Adaptation to sine-wave gratings selectively reduces the contrast gain of the adapted stimuli. J Vis, 2006.6(7): p. 739-59.
87. Kobatake, E., G. Wang, and K. Tanaka, Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. J Neurophysiol, 1998.80(1): p. 324-30.
88. Eckstein, M. P. and A. J. Ahumada, Jr., Classification images: a tool to analyze visual strategies. J Vis, 2002.2(1): p. ⅸ.
89. Levi, D. M., S. Hariharan, and S. A. Klein, Suppressive and facilitatory spatial interactions in amblyopic vision. Vision Res, 2002.42(11): p. 1379-94.
90. Chung, S.T., et al., Spatial-frequency properties of letter identification in amblyopia. Vision Res, 2002. 42(12): p. 1571-81.
91. Ahissar, M. and S. Hochstein, The reverse hierarchy theory of visual perceptual learning. Trends Cogn Sci, 2004. 8(10): p. 457-64.
92. Brainard, D., The Psychophysics Toolbox. Spat Vis, 1997.10(4): p. 433-6.
93. Li, X., et al., Generating high gray-level resolution monochrome displays with conventional computer graphics cards and color monitors. J Neurosci Methods, 2003.130(1): p. 9-18.
94. Hess, R. F., et al., Integration of local motion is normal in amblyopia. J Opt Soe Am A Opt Image Sci Vis, 2006.23(5): p. 986-92.
95. Maloney, L.T., Confidence intervals for the parameters of psychometric functions. Percept Psychophys, 1990.47(2): p. 127-34.
96. Shadlen, M. N. and W.T. Newsome, The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci, 1998.18(10): p. 3870-96.
97. Tolhurst, D. J., J.A. Movshon, and A.F. Dean, The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res, 1983.23(8): p. 775-85.
98. Hess, R. F., Developmental sensory impairment: amblyopia or tarachopia? Hum Neurobiol, 1982. 1(1): p. 17-29.
99. Hess, R. F. and I. E. Holliday, The spatial localization deficit in amblyopia. Vision Res, 1992. 32(7): p. 1319-39.
100. Gingras, G., D. E. Mitchell, and R. F. Hess, Haphazard neural connections underlie the visual deficits of cats with strabismic or deprivation amblyopia. Eur J Neurosci, 2005. 22(1): p. 119-24.
101. Jeter, P. E., et al., Identical Transfer of Perceptual Learning Following Easy and Difficult Task Training. 2005-Abstract.
102. Posner, M. I. and S.W. Keele, On the genesis of abstract ideas. Journal of Experimental Psychology, 1968.77(3): p. 353-363.
103. Smith, E. E. and D.L. Medin, Categories and Concepts. 1981, Cambridge, MA: Harvard University Press.
104. Nosofsky, R.M., Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition., 1984.10(1): p. 104-114.
105. Hintzman, D., "Schema abstraction" in a multiple-trace memory model. Psychological Review, 1986.93(4): p. 411-428.
106. McKee, S. P. and G. Westheimer, Improvement in vernier acuity with practice. Perception & Psychophysics, 1978, 24(3): p. 258-262.
107. Ramachandran, V. S. and O. Braddick, Orientation-specific learning in stereopsis. Perception, 1973.2(3): p. 371-376.
108. Poggio, T., M. Fahle, and S. Edelman, Fast perceptual learning in visual hyperacuity. Science, 1992. 256(5059): p. 1018-1021.
109. Karni, A. and D. Sagi, Where Practice Makes Perfect in Texture-Discrimination-Evidence for Primary Visual-Cortex Plasticity. Proceedings of the National Academy of Sciences of the United States of America, 1991.88(11): p. 4966-4970.
110. Fiorentini, A. and N. Berardi, Perceptual learning specific for orientation and spatial frequency. Nature, 1980. 287(5777): p. 43-44.
111. Sagi, D. and D. Tanne, Perceptual learning: learning to see. Curt Opin Neurobiol, 1994.4(2): p. 195-9.
112. Gibson, J.J., Principles of perceptual learning and development. 1967, New York: Appleton Century Crofts.
113. Ball, K. and R. Sekuler, A specific and enduring improvement in visual motion discrimination. Science, 1982. 218(4573): p. 697-698.
114. Crist, R.E., W. Li, and C. D. Gilbert, Learning to see.: Experience and attention in primary visual cortex. Nature Neuroscience, 2001.4(5): p. 519-525.
115. Yang, T. and J.H. Maunsell, The effect of perceptual learning on neuronal responses in monkey visual area V4. J Neurosci, 2004. 24(7): p. 1617-26.
116. Recanzone, G.H., C.E. Schreiner, and M.M. Merzenich, Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. Journal of Neuroscience, 1993.13(1): p. 87-103.
117. Ashby, F.G., et al., A neuropsychological theory of multiple systems in categorical learning. Psychological Review, 1998. 105: p. 442-481.
118. Ashby, F.G. and S. W. Ell, The neurobiological basis of categoriy learning. Trends in Cognitive Science, 2001. 5: p. 204-210.
119. Maddox, W.T. and F.G. Ashby, Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 2004.66: p. 309-332.
120. Erikson, M.A. and J.K. Kruschke, Rules and examplars in category learning. Journal of Experimental Psychology: General, 1998. 127: p. 107-140.
121. Reber, P. J. and L.R. Squire, Parallel brain systems for learning with and without awareness. Learning and Memory, 1994.1: p. 217-229.
122. Reber, P.J., C.E.L. Stark, and L.R. Squire, Cortical areas supporting category learning identified using functional magnetic resonance imaging. Proceedings of the National Academy of Sciences of the United States of Arnerica, 1998. 95(747-750.).
123. Ashby, F.G. and J.B. O'Brien, Category learning and multiple memory systems. Trends in Cognitive Sciences, 2005.9(2): p. 83-89.
124. Squire, L. R. and D.L. Shacter, The Neuropsychology of Memory. 3rd ed. 2002, New York, NY: Guilford Press.
125. Dosher, B. A. and Z.-L. Lu, Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences of the United States of America, 1998.95(23): p. 13988-13993.
126. Ghose, G. M., T. Yang, and J. H. Maunsell, Physiological correlates of perceptual learning in monkey V1 and V2. J Neurophysiol, 2002.87(4): p. 1867-88.
127. Schoups, A., et al., Practising orientation identification improves orientation coding in V1 neurons. Nature, 2001. 412(6846): p. 549-553.
128. Wilson SAK, Progressive lenticular degeneration. A familial nervous disease associated with cirrhosis of the liver. Brain, 1912.34: p. 295-307.
129. Bull, P. C., et al., The Wilson disease gene is a putative copper transporting P-type APTase similar to the Menkes gene. Nat Genet, 1993.5: p. 327-337.
130. Walshe, J. M., Wilson disease. Handbook of clinical neurology: extrapyramidal disorders, ed. B.G. Vinken PJ, Klawans HL. Vol. 5. 1986, Amsterdam: Elsevier Science. 223-238.
131. Starosta-Rubinstein, S., A.B. Young, and K. Kluin, Clinical assessment of 31 patients with Wilson disease. Arch Neurol, 1987. 44: p. 365-370.
132. Hawkins, R.A., J.C. Maziotta, and M. E. Phelps, Wilson disease studied with FDG and positron emission tomography. Neurology, 1987.37: p. 1707-1711.
133. Gajda, J., A. Czlonkowska, and T. Kryst, Correlation of clinical manifestation and MRI findings in Wilson disease. J Neurol, 1994. suppl. 1: p. 161.
134. Knehr, C. A. and A. G. Beam, Psychological impairment in Wilson disease. J Nerv Ment Disord, 1956. 156: p. 251-255.
135. Cummings, J. L. and d. Subcortical, Neuropsychology, neuropsychiatry and pathophysiology. Br J Psychiatry, 1986. 149: p. 682-697.
136. Joanna, S., et al., Cognitive Functioning in Neurologically Symptomatic and Asymptomatic Forms of Wilson Disease. Movement Disorders, 2002. 17(5): p. 1077-1083.
137. Ashby, F.G., et al., Category learning deficits in Parkinson's Disease. Neuropsychology, 2003. 17(1): p. 115-124.
138. Schacter, D.L., A.D. Wagner, and R.L. Buckner, Memory systems of 1999, in The Oxford Handbook of Memory, E. a. C. Tulving, F. I. M., Editor. 2000, Oxford University Press: New York, NY.
139. Goldstone, R. L., Perceptual learning. Annual Review of Psychology, 1998.49: p. 585-612.
140. Dosher, B. A. and Z.L. Lu, Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proe Natl Acad Sci U S A, 1998. 95(23): p. 13988-93.
141. Hess, R. F., W. Mcllhagga, and D. J. Field, Contour integration in strabismic amblyopia: the sufficiency of an explanation based on positional uncertainty. Vision Res, 1997. 37(22): p. 3145-61.
142. Hess, R. F. and R. Demanins, Contour integration in anisometropic amblyopia. Vision Res, 1998. 38(6): p. 889-94.
143. Simmers, A. J., T. Ledgeway, and R.F., Hess, The influences of visibility and anomalous integration processes on the perception of global spatial form versus motion in human amblyopia. Vision Res, 2005.45(4): p. 449-60.
144. Wong, E. H., D.M. Levi, and P.V. McGraw, Is second-order spatial loss in amblyopia explained by the loss of first-order spatial input? Vision Res, 2001.41(23): p. 2951-60.
145. Baker, J. E., et al., The unique properties of tonic smooth muscle emerge from intrinsic as well as intermolecular behaviors of Myosin molecules. J Biol Chem, 2003. 278(31): p. 28533-9.
146. Kubova, Z. and M. Kuba, Clinical application of motion-onset visual evoked potentials. Doc Ophthalmol, 1992.81(2): p. 209-18.
147. Campbell, F. W., et al., Preliminary results of a physiologically based treatment of amblyopia. Br J Ophthalmol, 1978. 62(11): p. 748-55.
2. Nordmann, J. P., R. D. Freeman, and C. Casanova, Contrast sensitivity in amblyopia: masking effects of noise. Invest Ophthalmol Vis Sci, 1992.33(10): p. 2975-85.
3. Schofield, A. J. and M.A. Georgeson, Sensitivity to modulations of luminance and contrast in visual white noise: separate mechanisms with similar behaviour. Vision Res, 1999.39(16): p. 2697-716.
4. Levi, D. M. and S. A. Klein, Noise provides some new signals about the spatial vision of amblyopes. J Neurosci, 2003.23(7): p. 2522-6.
5. Pelli, D. G, D. M. Levi, and S.T. Chung, Using visual noise to characterize amblyopic letter identification. J Vis, 2004. 4(10): p. 904-20.
6. Dosher, B. A. and Z. L. Lu, Mechanisms of perceptual learning. Vision Res, 1999. 39(19): p. 3197-221.
7. Lu, Z. L. and B.A. Dosher, External noise distinguishes attention mechanisms. Vision Res, 1998.38(9): p. 1183-98.
8. Dosher, B. A. and Z. L. Lu, Mechanisms of perceptual attention in precuing of location. Vision Res, 2000. 40(10-12): p. 1269-92.
9. Dosher, B. A. and Z. L. Lu, Noise exclusion in spatial attention. Psychol Sci, 2000. 11(2): p. 139-46.
10. Lesmes, L. A., et al., Bayesian adaptive estimation of threshold versus contrast external noise functions: the quick TvC method. Vision Res, 2006.46(19): p. 3160-76.
11. Lu, Z. L. and B. A. Dosher, Characterizing human perceptual inefficiencies with equivalent internal noise. J Opt Soc Am A Opt Image Sci Vis, 1999. 16(3): p. 764-78.
12. Pelli, D.G. and B. Farell, Why use noise? J Opt Soc Am A Opt Image Sci Vis, 1999. 16(3): p. 647-53.
13. Pelli, D. G., Effects of visual noise. PhD. disseration, 1981(University of Cambridge, Cambridge, England).
14. Lu, Z.L., C.Q. Liu, and B.A. Dosher, Attention mechanisms for multi-location first-and second-order motion perception. Vision Res, 2000. 40(2): p. 173-86.
15. Ohlsson, J., Defining amblyopia: the need for a joint classification. Strabismus, 2005.13(1): p. 15-20.
16. Ciuffreda KJ, Levi DM, and A. Selenow, Amblyopia: Basic and Clinical Aspects. 1991, Boston: Butterworth-Heinemann.
17. Barrett, B.T., A. Bradley, and P.V. McGraw, Understanding the neural basis of amblyopia. Neuroscientist, 2004. 10(2): p. 106-17.
18. Campos, E. C., M. L. Prampolini, and R. Gulli, Contrast sensitivity differences between strabismic and anisometropic amblyopia: objective correlate by means of visual evoked responses. Doc Ophthalmol, 1984. 58(1): p. 45-50.
19. Choi, M. Y., et al., Comparison between anisometropic and strabismic amblyopia using functional magnetic resonance imaging. Br J Ophthalmol, 2001.85(9): p. 1052-6.
20. McKee, S.P., D.M. Levi, and J.A. Movshon, The pattern of visual deficits in amblyopia. J Vis, 2003.3(5): p. 380-405.
21. McKee, S.P., et al., The classification of amblyopia on the basis of visual and oculomotor performance. Trans Am Ophthalmol Soc, 1992.90: p. 123-44; discussion 145-8.
22. Delint, P.J., et al., Photoreceptor function in unilateral amblyopia. Vision Res, 1998.38(4): p. 613-7.
23. Zhou, Y., A. G. Leventhal, and K.G. Thompson, Visual deprivation does not affect the orientation and direction sensitivity of relay cells in the lateral geniculate nucleus of the cat. J Neurosci, 1995.15(1 Pt 2): p. 689-98.
24. Levi, D. M. and S. A. Klein, Limitations on position coding imposed by undersampling and univariance. Vision Res, 1996.36(14): p. 2111-20.
25. Levi, D. M., S. A. Klein, and V. Sharma, Position jitter and undersampling in pattern perception. Vision Res, 1999. 39(3): p. 445-65.
26. Hess, R. F, Y. Z. Wang, and S.C. Dakin, Are judgements of circularity local or global? Vision Res, 1999. 39(26): p. 4354-60.
27. Polat, U. and Y. Bonneh, Collinear interactions and contour integration. Spat Vis, 2000. 13(4): p. 393-401.
28. Saarinen, J. and D. M. Levi, Integration of local features into a global shape. Vision Res, 2001. 41(14): p. 1785-90.
29. Chandna, A., et al., Contour integration deficits in anisometropic amblyopia. Invest Ophthalmol Vis Sci, 2001.42(3): p. 875-8.
30. Meese, T.S., R.F. Hess, and C. B. Williams, Spatial coherence does not affect contrast discrimination for multiple Gabor stimuli. Perception, 2001.30(12): p. 1411-22.
31. Hess, R. F., et al., Contour interaction in amblyopia: scale selection. Vision Res, 2001.41(17): p. 2285-96.
32. Crewther, D. P. and S.G. Crewther, Neural site of strabismic amblyopia in cats: spatial frequency deficit in primary cortical neurons. Exp Brain Res, 1990.79(3): p. 615-22.
33. Kossel, A., S. Lowel, and J. Bolz, Relationships between dendritic fields and functional architecture in striate cortex of normal and visually deprived cats. J Neurosci, 1995.15(5 Pt 2): p. 3913-26.
34. Kiorpes, L., et al., Neuronal correlates of amblyopia in the visual cortex of macaque monkeys with experimental strabismus and anisometropia. J Neurosci, 1998. 18(16): p. 6411-24.
35. Sireteanu, R., The binocular visualsystem in amblyopia. Strabismus, 2000.8(I): p. 39-51.
36. Kiorpes, L. and S. P. McKee, Neural mechanisms underlying amblyopia. Curr Opin Neurobiol, 1999. 9: p. 480-486.
37. Smith, E.L., 3rd, et al., Residual binocular interactions in the striate cortex of monkeys reared with abnormal binocular vision. J Neurophysiol, 1997.78(3): p. 1353-62.
38. Roelfsema, P. R., et al., Reduced synchronization in the visual cortex of cats with strabismic amblyopia. Eur J Neurosci, 1994.6(11): p. 1645-55.
39. Schroder, J.H., et al., Ocular dominance in extrastriate cortex of strabismic amblyopic cats. Vision Res, 2002.42(1): p. 29-39.
40. Imamura, K., et al., Reduced activity in the extrastriate visual cortex of individuals with strabismic amblyopia. Neurosci Lett, 1997. 225(3): p. 173-6.
41. Hess, R. F., Amblyopia: site unseen. Clin Exp Optom, 2001.84(6): p. 321-336.
42. Goodyear, B.G., et al., BOLD fMRI response of early visual areas to perceived contrast in human amblyopia. J Neurophysiol, 2000. 84(4): p. 1907-13.
43. Barnes, G.R., et al., The cortical deficit in humans with strabismic amblyopia. J Physiol, 2001. 533(Pt 1): p. 281-97.
44. Lee, K.M., et al., Binocularity and spatial frequency dependence of calcarine activation in two types of amblyopia. Neurosci Res, 2001.40(2): p. 147-53.
45. Curtis, L., J. Baker, and I. Mareschal, Processing of second-order stimuli in the visual cortex. Progress in Brain Research, 2001. 134: p. 1-21.
46. Zhou, Y.X. and C.L. Baker, Jr., A processing stream in mammalian visual cortex neurons for non-Fourier responses. Science, 1993.261(5117): p. 98-101.
47. Zhou, Y.X. and C. L. Baker, Jr., Envelope-responsive neurons in areas 17 and 18 of cat. J Neurophysiol, 1994.72(5): p. 2134-50.
48. Zhou, Y.X. and C.L. Baker, Jr., Spatial properties of envelope-responsive cells in area 17 and 18 neurons of the cat. J Neurophysiol, 1996.75(3): p. 1038-50.
49. Mareschal, I. and C.L. Baker, Jr., Temporal and spatial response to second-order stimuli in cat area 18. J Neurophysiol, 1998.80(6): p. 2811-23.
50. Mareschal, I. and C.L. Baker, Jr., Cortical processing of second-order motion. Vis Neurosei, 1999. 16(3): p. 527-40.
51. Leventhal, A. G., et al., Neural correlates of boundary perception. Vis Neurosci, 1998. 15(6): p. 1107-18.
52. Albright, T.D., Form-cue invariant motion processing in primate visual cortex. Science, 1992. 255(5048): p. 1141-3.
53. Henning, G.B., B.G. Hertz, and D.E. Broadbent, Some experiments bearing on the hypothesis that the visual system analyses spatial patterns in independent bands of spatial frequency. Vision Res, 1975: p. 887-897.
54. Nishida, S., T. Ledgeway, and M. Edwards, Dual multiple-scale processing for motion in the human visual system. Vision Res, 1997. 37(19): p. 2685-98.
55. Goldstone, R. L., Perceptual learning. Annu Rev Psychol, 1998.49: p. 585-612.
56. Ciuffreda, K., L. DM, and S. A, Amblyopia: Basic and Clinical Aspects. 1991, Boston: Butterworth-Heinemann.
57. McKee, S. P., D.M. Levi, and J. A. Movshon, The pattern of visual deficits in amblyopia. Journal of Vision, 2003. 3(5): p. 380-405.
58. Eggers, H.M. and C. Blakemore, Physiological basis of anisometropic amblyopia. Science, 1978. 201(4352): p. 264-7.
59. Movshon, J. A., et al., Effects of early unilateral blur on the macaque's visual system. Ⅲ. Physiological observations. J Neurosci, 1987.7(5): p. 1340-51.
60. Sireteanu, R., et al., Cortical cite of the amblyopic deficit in strabismic and anisometropic subjects, investigated with fMRI, nvestigative Ophthalmology and Visual Science, 1998.39(4): p. s909.
61. Daw, N., Critical periods and amblyopia. Arch Ophthalmol, 1998. 116: p. 502-505.
62. Rose, A., The sensitivity performance of the human eye on an absolute scale. Journal of the Optical Society of America, 1948.38: p. 196-208.
63. Barlow, H. B., Retinal noise and absolute threshold Journal of the Optical Society of America, 1956.46: p. 634-639.
64. Tanner, W.P., Jr. and T.G. Birdsall, Definitions of d' and n as psychophysical measures. Journal of the Acoustical Society of America, 1958.30: p. 922-928.
65. Van Meeteren, A. and H. B. Barlow, The statistical effciency for detecting sinusoidal modulation of average dot density in random figures. Vision Research, 1981. 21(6): p. 765-777.
66. Nagaraja, N.S., Effect of luminance noise on contrast thresholds. Journal of the Optical Society of America, 1964.54(7): p. 950-955.
67. Montero, V. M., Amblyopia decreases activation of the corticogeniculate pathway and visual thalamic reticularis in attentive rats: a 'focal attention' hypothesis. Neuroscience, 1999. 91(3): p. 805-17.
68. Ahumada, A.J. and A. B. Watson, Equivalent-noise model for contrast detection and discrimination. Journal of the Optical Society of America A, 1985.2(7): p. 1133-1139.
69. Legge, G. E., D. Kersten, and A. E. Burgess, Contrast discrimination in noise. Journal of the Optical Society of America. A, 1987.4(2): p. 391-404.
70. Geisler, W.S., Sequential ideal-observer analysis of visual discriminations. Psychological Review, 1989.96(2): p. 267-314.
71. Pelli, D. G., The quantum efficiency of vision, in Vision: Coding and Effciency, C. Blakemore, Editor. 1990, Cambridge University Press: Cambridge, UK. p. 3-24.
72. Gegenfurtner, K. R. and D. C. Kiper, Contrast detection in luminance and chromatic noise. Journal of the Optical Society of America A, 1992. 9(11): p. 1880-1888.
73. D'Zmura, M. and K. Knoblauch, Spectral bandwidths for the detection of color. Vision Research, 1998.38(20): p. 3117-3128.
74. Tjan, B. S., et al., Human efficiency for recognizing 3-D objects in luminance noise. Vision Research, 1995.35(21): p. 3053-3069.
75. Baker, C.L., Jr., Central neural mechanisms for detecting second-order motion. Curt Opin Neurobiol, 1999. 9(4): p. 461-6.
76. Hay, G. A. and M. S. Chesters, Signal-transfer functions in threshold and suprathreshold vision. Journal of the Optical Society of America, 1972.62: p. 990-998.
77. Ahumada, A.J., Puing the visual system noise back in the picture. Journal of the Optical Society of America, 1987.4: p. 2372-2378.
78. Burgess, A. E., et al., Effciency of human visual signal discrimination. Science, 1981. 214(4516): p. 93-94.
79. Pelli, D.G., Uncertainty explains many aspects of visual contrast detection and discrimination. Journal of the Optical Society of America, A, 1985.2: p. 1508-1532.
80. Burgess, A. E. and B. Colborne, Visual signal detection. Ⅳ. Observer inconsistency. J Opt Soc Am A, 1988.5(4): p. 617-27.
81. Eckstein, M. P., A.J. Ahumada, Jr., and A. B. Watson, Visual signal detection in structured backgrounds: Ⅱ. Effects of contrast gain control, background variations, and white noise. Journal of the Optical Society of America, 1997.14(9): p. 2406-2419.
82. Burgess, A. E., R. Shaw, and J. Lubin, Noise in image Processing. Journal of the Optical Society of America A [Special issue], 1999(16).
83. Dosher, B. A., et al., The spatial window of the perceptual template and endogenous attention. Vision Res, 2004.44(12): p. 1257-71.
84. Arrighi, R., D. Alais, and D. Burr, Perceived timing of first- and second-order changes in vision and hearing. Exp Brain Res, 2005. 166(3-4): p. 445-54.
85. Gold, J., P.J. Bennett, and A.B. Sekuler, Signal but not noise changes with perceptual learning. Nature, 1999. 402(6758): p. 176-8.
86. Dao, D.Y., Z.L. Lu, and B.A. Dosher, Adaptation to sine-wave gratings selectively reduces the contrast gain of the adapted stimuli. J Vis, 2006.6(7): p. 739-59.
87. Kobatake, E., G. Wang, and K. Tanaka, Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. J Neurophysiol, 1998.80(1): p. 324-30.
88. Eckstein, M. P. and A. J. Ahumada, Jr., Classification images: a tool to analyze visual strategies. J Vis, 2002.2(1): p. ⅸ.
89. Levi, D. M., S. Hariharan, and S. A. Klein, Suppressive and facilitatory spatial interactions in amblyopic vision. Vision Res, 2002.42(11): p. 1379-94.
90. Chung, S.T., et al., Spatial-frequency properties of letter identification in amblyopia. Vision Res, 2002. 42(12): p. 1571-81.
91. Ahissar, M. and S. Hochstein, The reverse hierarchy theory of visual perceptual learning. Trends Cogn Sci, 2004. 8(10): p. 457-64.
92. Brainard, D., The Psychophysics Toolbox. Spat Vis, 1997.10(4): p. 433-6.
93. Li, X., et al., Generating high gray-level resolution monochrome displays with conventional computer graphics cards and color monitors. J Neurosci Methods, 2003.130(1): p. 9-18.
94. Hess, R. F., et al., Integration of local motion is normal in amblyopia. J Opt Soe Am A Opt Image Sci Vis, 2006.23(5): p. 986-92.
95. Maloney, L.T., Confidence intervals for the parameters of psychometric functions. Percept Psychophys, 1990.47(2): p. 127-34.
96. Shadlen, M. N. and W.T. Newsome, The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci, 1998.18(10): p. 3870-96.
97. Tolhurst, D. J., J.A. Movshon, and A.F. Dean, The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res, 1983.23(8): p. 775-85.
98. Hess, R. F., Developmental sensory impairment: amblyopia or tarachopia? Hum Neurobiol, 1982. 1(1): p. 17-29.
99. Hess, R. F. and I. E. Holliday, The spatial localization deficit in amblyopia. Vision Res, 1992. 32(7): p. 1319-39.
100. Gingras, G., D. E. Mitchell, and R. F. Hess, Haphazard neural connections underlie the visual deficits of cats with strabismic or deprivation amblyopia. Eur J Neurosci, 2005. 22(1): p. 119-24.
101. Jeter, P. E., et al., Identical Transfer of Perceptual Learning Following Easy and Difficult Task Training. 2005-Abstract.
102. Posner, M. I. and S.W. Keele, On the genesis of abstract ideas. Journal of Experimental Psychology, 1968.77(3): p. 353-363.
103. Smith, E. E. and D.L. Medin, Categories and Concepts. 1981, Cambridge, MA: Harvard University Press.
104. Nosofsky, R.M., Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition., 1984.10(1): p. 104-114.
105. Hintzman, D., "Schema abstraction" in a multiple-trace memory model. Psychological Review, 1986.93(4): p. 411-428.
106. McKee, S. P. and G. Westheimer, Improvement in vernier acuity with practice. Perception & Psychophysics, 1978, 24(3): p. 258-262.
107. Ramachandran, V. S. and O. Braddick, Orientation-specific learning in stereopsis. Perception, 1973.2(3): p. 371-376.
108. Poggio, T., M. Fahle, and S. Edelman, Fast perceptual learning in visual hyperacuity. Science, 1992. 256(5059): p. 1018-1021.
109. Karni, A. and D. Sagi, Where Practice Makes Perfect in Texture-Discrimination-Evidence for Primary Visual-Cortex Plasticity. Proceedings of the National Academy of Sciences of the United States of America, 1991.88(11): p. 4966-4970.
110. Fiorentini, A. and N. Berardi, Perceptual learning specific for orientation and spatial frequency. Nature, 1980. 287(5777): p. 43-44.
111. Sagi, D. and D. Tanne, Perceptual learning: learning to see. Curt Opin Neurobiol, 1994.4(2): p. 195-9.
112. Gibson, J.J., Principles of perceptual learning and development. 1967, New York: Appleton Century Crofts.
113. Ball, K. and R. Sekuler, A specific and enduring improvement in visual motion discrimination. Science, 1982. 218(4573): p. 697-698.
114. Crist, R.E., W. Li, and C. D. Gilbert, Learning to see.: Experience and attention in primary visual cortex. Nature Neuroscience, 2001.4(5): p. 519-525.
115. Yang, T. and J.H. Maunsell, The effect of perceptual learning on neuronal responses in monkey visual area V4. J Neurosci, 2004. 24(7): p. 1617-26.
116. Recanzone, G.H., C.E. Schreiner, and M.M. Merzenich, Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. Journal of Neuroscience, 1993.13(1): p. 87-103.
117. Ashby, F.G., et al., A neuropsychological theory of multiple systems in categorical learning. Psychological Review, 1998. 105: p. 442-481.
118. Ashby, F.G. and S. W. Ell, The neurobiological basis of categoriy learning. Trends in Cognitive Science, 2001. 5: p. 204-210.
119. Maddox, W.T. and F.G. Ashby, Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 2004.66: p. 309-332.
120. Erikson, M.A. and J.K. Kruschke, Rules and examplars in category learning. Journal of Experimental Psychology: General, 1998. 127: p. 107-140.
121. Reber, P. J. and L.R. Squire, Parallel brain systems for learning with and without awareness. Learning and Memory, 1994.1: p. 217-229.
122. Reber, P.J., C.E.L. Stark, and L.R. Squire, Cortical areas supporting category learning identified using functional magnetic resonance imaging. Proceedings of the National Academy of Sciences of the United States of Arnerica, 1998. 95(747-750.).
123. Ashby, F.G. and J.B. O'Brien, Category learning and multiple memory systems. Trends in Cognitive Sciences, 2005.9(2): p. 83-89.
124. Squire, L. R. and D.L. Shacter, The Neuropsychology of Memory. 3rd ed. 2002, New York, NY: Guilford Press.
125. Dosher, B. A. and Z.-L. Lu, Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences of the United States of America, 1998.95(23): p. 13988-13993.
126. Ghose, G. M., T. Yang, and J. H. Maunsell, Physiological correlates of perceptual learning in monkey V1 and V2. J Neurophysiol, 2002.87(4): p. 1867-88.
127. Schoups, A., et al., Practising orientation identification improves orientation coding in V1 neurons. Nature, 2001. 412(6846): p. 549-553.
128. Wilson SAK, Progressive lenticular degeneration. A familial nervous disease associated with cirrhosis of the liver. Brain, 1912.34: p. 295-307.
129. Bull, P. C., et al., The Wilson disease gene is a putative copper transporting P-type APTase similar to the Menkes gene. Nat Genet, 1993.5: p. 327-337.
130. Walshe, J. M., Wilson disease. Handbook of clinical neurology: extrapyramidal disorders, ed. B.G. Vinken PJ, Klawans HL. Vol. 5. 1986, Amsterdam: Elsevier Science. 223-238.
131. Starosta-Rubinstein, S., A.B. Young, and K. Kluin, Clinical assessment of 31 patients with Wilson disease. Arch Neurol, 1987. 44: p. 365-370.
132. Hawkins, R.A., J.C. Maziotta, and M. E. Phelps, Wilson disease studied with FDG and positron emission tomography. Neurology, 1987.37: p. 1707-1711.
133. Gajda, J., A. Czlonkowska, and T. Kryst, Correlation of clinical manifestation and MRI findings in Wilson disease. J Neurol, 1994. suppl. 1: p. 161.
134. Knehr, C. A. and A. G. Beam, Psychological impairment in Wilson disease. J Nerv Ment Disord, 1956. 156: p. 251-255.
135. Cummings, J. L. and d. Subcortical, Neuropsychology, neuropsychiatry and pathophysiology. Br J Psychiatry, 1986. 149: p. 682-697.
136. Joanna, S., et al., Cognitive Functioning in Neurologically Symptomatic and Asymptomatic Forms of Wilson Disease. Movement Disorders, 2002. 17(5): p. 1077-1083.
137. Ashby, F.G., et al., Category learning deficits in Parkinson's Disease. Neuropsychology, 2003. 17(1): p. 115-124.
138. Schacter, D.L., A.D. Wagner, and R.L. Buckner, Memory systems of 1999, in The Oxford Handbook of Memory, E. a. C. Tulving, F. I. M., Editor. 2000, Oxford University Press: New York, NY.
139. Goldstone, R. L., Perceptual learning. Annual Review of Psychology, 1998.49: p. 585-612.
140. Dosher, B. A. and Z.L. Lu, Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proe Natl Acad Sci U S A, 1998. 95(23): p. 13988-93.
141. Hess, R. F., W. Mcllhagga, and D. J. Field, Contour integration in strabismic amblyopia: the sufficiency of an explanation based on positional uncertainty. Vision Res, 1997. 37(22): p. 3145-61.
142. Hess, R. F. and R. Demanins, Contour integration in anisometropic amblyopia. Vision Res, 1998. 38(6): p. 889-94.
143. Simmers, A. J., T. Ledgeway, and R.F., Hess, The influences of visibility and anomalous integration processes on the perception of global spatial form versus motion in human amblyopia. Vision Res, 2005.45(4): p. 449-60.
144. Wong, E. H., D.M. Levi, and P.V. McGraw, Is second-order spatial loss in amblyopia explained by the loss of first-order spatial input? Vision Res, 2001.41(23): p. 2951-60.
145. Baker, J. E., et al., The unique properties of tonic smooth muscle emerge from intrinsic as well as intermolecular behaviors of Myosin molecules. J Biol Chem, 2003. 278(31): p. 28533-9.
146. Kubova, Z. and M. Kuba, Clinical application of motion-onset visual evoked potentials. Doc Ophthalmol, 1992.81(2): p. 209-18.
147. Campbell, F. W., et al., Preliminary results of a physiologically based treatment of amblyopia. Br J Ophthalmol, 1978. 62(11): p. 748-55.