Abstract
In a previous study, we found that subjects' performance in a task of direction discrimination in stochastic motion stimuli shows fast improvement in the absence of feedback and the learned ability is retained over a period of time. We model this learning using two unsupervised approaches: a clustering model that learns to accommodate the motion noise, and an averaging model that learns to ignore the noise. Extensive simulations with the models show performance similar to psychophysical results.
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References
Adelson E, Bergen JR (1986) The extraction of spatio-temporal energy in human and machine vision. IEEE Workshop on Motion, pp 151–155
Ball K, Sekuler R (1987) Direction-specific improvement in motion discrimination. Vision Res 27:953–965
Britten KH, Shadlen NM, Newsome WT (1992) The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12:4745–4765
Downing CJ, Movshon JA (1989) Spatial and temporal summation in the detection of motion in stochastic random dot displays. Invest Ophthalmol Vis Sci [Suppl] 30:72
Durbin R, Rumelhart DE (1989) Product units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput 1:133–142
Fendick M, Westheimer G (1983) Effects of practice and the separation of test targets on foveal and perifoveal hyperacuity. Vision Res 23:145–150
Fiorentini A, Berardi N (1980) Perceptual learning specific for orientation and spatial frequency. Nature 287:43–44
Fleet DJ, Jepson AD (1990) Computation of component image velocity from local phase information. Int J Comput Vis 5:77–104
Frégnac Y, Shulz D, Thorpe S, Bienstock E (1988) A cellular analogue of visual cortical plasticity. Nature 333:367–370
Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233:1416–1419
Gilbert CD, Wiesel TN (1992) Receptive field dynamics in adult primary visual cortex. Nature 356:150–152
Grzywacz NM, Yuille AL (1990) A model for the estimate of local image velocity by cells in the visual cortex. Proc R Soc Lond A 239:129–161
Heeger DJ (1987) Optical flow using spatiotemporal filters. Int J Comput Vis 1:279–302
Heeger DJ, Jepson AD (1992) Subspace methods for recovering rigid motion. I. Algorithm and implementation. Int J Comput Vis 7:95–117
Hertz J, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley, Reading, Mass
Hubel D, Wiesel T (1962) Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex. J Physiol (Lond) 160:106–154
Hummel R, Sundareswaran V (1993) Motion parameter estimation from global flow field data. IEEE Trans Pattern Analysis Machine Intell 15:459–476
Karni A, Sagi D (1991) Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proc Natl Acad Sci USA 88:4966–4970
Lehky SR, Sejnowski T (1990) Neural model of stereoacuity and depth inerpolation based on a distributed representation of stereo disparity. J Neurosci 10:2281–2299
Maunsell JHR, Essen DC van (1983) Functional properties of neurons in the middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation. J Neurophysiol 49:1127–1147
McKee SP, Westheimer G (1978) Improvement in vernier acuity with practice. Percept Psychophys 24:258–262
Moody J, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1:281–294
Newsome WT, Paré EB (1988) A selective impairment of motion perception following lesions of the middle temporal visual area. J Neurosci 8:2201–2211
Newsome WT, Britten KH, Movshon JA (1989a) Neuronal correlates of a perceptual decision. Nature 341:52–54
Newsome WT, Britten KH, Movshon JA, Shadlen NM (1989b) Single neurons and the perception of visual motion. In: Lam DMK, Gilbert CD (eds) Neural mechanisms of visual perception: proceedings of the retinal research foundation. Portfolio Publishing, Huntington, NY, pp 171–198
Poggio T (1990) A theory of how the brain might work. Cold Spring Harbor Symp Quant Biol 55:899–910
Poggio T, Girosi F (1990) Networks for approximation and learning. Proc IEEE 78:1481–1497
Poggio T, Fahle M. Edelman S (1992) Fast perceptual learning in visual hyperacuity. Science 256:1018–1021
Prazdny K (1980) Egomotion and relative depth from optical flow. Biol Cybern 36:87–102
Ramachandran VS, Braddick O (1973) Orientation-specific learning in stereopsis. Perception 2:371–376
Rieger JH, Lawton DT (1985) Processing differential image motion. J Opt Soc Am A 2:354
Salzman CD, Newsome WT (1994) Neural mechanisms for forming a perceptual decision. Science 264:231–237
Sundareswaran V, Vaina LM (1995) Learning direction in global motion: two classes of psychophysically-motivated models. In: Tesauro G, Touretzky D, Leen T (eds) Advances in neural information processing systems 7. 7:917–924, The MIT Press, Cambridge, Mass.
Vaina LM, Sundareswaran V, Harris J (1995) Learning to ignore: psychophysics and computational modeling of fast learning of direction in noisy motion stimuli. Cogn Brain Res 2:155–163
Vogels R, Orban GA (1985) The effect of practice on the oblique effect in line orientation judgements. Vis Res 25:1679–1687
Weiss Y, Edelman S, Fahle M (1993) Models of perceptual learning in vernier hyperacuity. Neural Comput 5:695–718
Zohary E, Celebrini S, Britten KH, Newsome WT (1994) Neuronal plasticity that underlies improvement in perceptual performance. Science 263:1289–1292
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Sundareswaran, V., Vaina, L.M. Adaptive computational models of fast learning of motion direction discrimination. Biol. Cybern. 74, 319–329 (1996). https://doi.org/10.1007/BF00194924
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DOI: https://doi.org/10.1007/BF00194924