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Directional shifts in the barber pole illusion: Effects of spatial frequency, spatial adaptation, and lateral masking

Published online by Cambridge University Press:  04 October 2006

CHRISTOPHE LALANNE  and
JEAN LORENCEAU
CHRISTOPHE LALANNE
Affiliation:
LENA, CNRS UPR 640, Paris, France
JEAN LORENCEAU
Affiliation:
LENA, CNRS UPR 640, Paris, France
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Abstract

We report the results of psychophysical experiments with the so-called barber pole stimulus providing new insights on the neuronal processes underlying the analysis of moving features such as terminators or line-endings. In experiment 1, we show that the perceived direction of a barber pole stimulus, induced by line-ending motion, is highly dependent on the spatial frequency and contrast of the grating stimulus: perceived direction is shifted away from the barber pole illusion at high spatial frequency in a contrast dependent way, suggesting that line-ends are not processed at high spatial scales. In subsequent experiments, we use a contrast adaptation paradigm and a masking paradigm in an attempt to assess the spatial structure and location of the receptive fields that process line-endings. We show that the adapting stimulus that weakens most the barber pole illusion is localized within the barber pole stimulus and not at line-endings' locations. Current models of line-endings' motion processing are discussed in the light of these psychophysical results.

Keywords

Motion integrationNeuronal adaptationLateral maskingSurround suppression
Type
Research Article
Information
Visual Neuroscience , Volume 23 , Issue 5 , September 2006 , pp. 729 - 739
Copyright
2006 Cambridge University Press

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References

REFERENCES

Adelson, E.H. & Bergen, J.R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A 2, 284–299.CrossRefGoogle Scholar
Adelson, E.H. & Movshon, J.A. (1982). Phenomenal coherence of moving visual patterns. Nature 300, 523–525.CrossRefGoogle Scholar
Barlow, H.B. & Hill, R.M. (1963). Evidence for a physiological explanation for the waterfall illusion and figural after effects. Nature 200, 1345–1347.CrossRefGoogle Scholar
Campbell, F.W. & Robson, J.G. (1968). Application of Fourier analysis to the visibility of gratings. Journal of Physiology 197, 551–566.CrossRefGoogle Scholar
Castet, E., Charton, V., & Dufour, A. (1999). The extrinsic/intrinsic classification of two-dimensional motion signals with barber-pole stimuli. Vision Research 39, 915–932.CrossRefGoogle Scholar
Castet, E., Lorenceau, J., Shiffrar, M., & Bonnet, C. (1993). Perceived speed of moving lines depends on orientation, length, speed and luminance. Vision Research 33, 1921–1936.CrossRefGoogle Scholar
DeAngelis, G.C., Freeman, R.D., & Ohzawa, I. (1994). Length and width tuning of neurons in the cat's primary visual cortex. Journal of Neurophysiology 71, 347–374.Google Scholar
De Valois, R.L., Albrecht, D.G., & Thorell, L.G. (1982). Spatial frequency selectivity of cells in macaque visual cortex. Vision Research 22, 545–559.CrossRefGoogle Scholar
Dobbins, A., Zucker, S.W., & Cynader, M.S. (1987). Endstopped neurons in the visual cortex as a substrate for calculating curvature. Nature 329, 438–441.CrossRefGoogle Scholar
Duncan, R.O., Albright, T.D., & Stoner, G.R. (2000). Occlusion and the interpretation of visual motion: Perceptual and neuronal effects of context. The Journal of Neuroscience 20, 5885–5897.Google Scholar
Fennema, C.L. & Thompson, W.B. (1979). Velocity determination in scenes containing several moving objects. Computer Graphics and Image Processing 9, 301–315.CrossRefGoogle Scholar
Fisher, N. & Zanker, J.M. (2001). The directional tuning of the barber-pole illusion. Perception 30, 1321–1336.CrossRefGoogle Scholar
Fletcher, R. & Powell, M.J.D. (1963). A rapidly convergent descent method for minimization. Computer Journal 6, 163–168.CrossRefGoogle Scholar
Freeman, R.D., Ohzawa, I., & Walker, G. (2001). Beyond the classical receptive field in the visual cortex. Progress in Brain Research 134, 157–170.CrossRefGoogle Scholar
Geisler, W.S. (1999). Motion streaks provide a spatial code for motion direction. Nature 400, 65–69.CrossRefGoogle Scholar
Gilbert, C.D. (1977). Laminar differences in receptive fields properties of cells in cat primary visual cortex. Journal of Physiology 268, 391–421.CrossRefGoogle Scholar
Heitger, F., Rosenthaler, L., von der Heydt, R., Peterhans, E., & Kubler, O. (1992). Simulation of neural contour mechanisms: From simple to end-stopped cells. Vision Research 32, 963–981.CrossRefGoogle Scholar
Haegerstrom-Portnoy, G. & Brown, B. (1979). Contrast effects on smooth-pursuit eye movement velocity. Vision Research 19, 169–174.CrossRefGoogle Scholar
Hubel, D.H. & Wiesel, T.N. (1965). Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. Journal of Neurophysiology 28, 229–289.Google Scholar
Hupé, J.M. & Rubin, N. (2003). The dynamics of bi-stable alternation in ambiguous motion displays: A fresh look at plaids. Vision Research 43, 531–548.CrossRefGoogle Scholar
Kelly, D.H. (1979). Motion and vision. II. Stabilized spatio-temporal threshold surface. Journal of the Optical Society of America 69, 1340–1349.Google Scholar
Kooi, F.L. (1993). Local direction of edge motion causes and abolishes the barberpole illusion. Vision Research 33, 2347–2351.CrossRefGoogle Scholar
Líden, L. & Pack, C. (1999). The role of terminators and occlusion cues in motion integration and segmentation: A neural network model. Vision Research 39, 3301–3320.CrossRefGoogle Scholar
Líden, L. & Mingolla, E. (1998). Monocular occlusion cues alter the influence of terminator motion in the barber pole phenomenon. Vision Research 38, 3883–3898.CrossRefGoogle Scholar
Löffler, G. & Orbach, H.S. (1999). Computing feature motion without feature detectors: A model for terminator motion without end-stopped cells. Vision Research 39, 859–871.CrossRefGoogle Scholar
Lorenceau, J. (1987). Recovery from contrast adaptation: Effects of spatial and temporal frequency. Vision Research 27, 2185–2191.CrossRefGoogle Scholar
Lorenceau, J., Shiffrar, M., Wells, N., & Castet, E. (1993). Different motion sensitive units are involved in recovering the direction of moving lines. Vision Research 33, 1207–1217.CrossRefGoogle Scholar
Majaj, N., Smith, M.A., Kohn, A., Bair, W., & Movshon, J.A. (2002). A role for terminators in motion processing by macaque MT neurons? Journal of Vision 2, 415a.Google Scholar
Marr, D. & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society of London B 207, 187–217.CrossRefGoogle Scholar
Masson, G.S., Rybarzcyk, Y., Castet, E., & Mestre, D.R. (2000). Temporal dynamics of motion integration for the initiation of tracking eye movements at ultra-short latencies. Visual Neuroscience 17, 753–767.CrossRefGoogle Scholar
Movshon, A., Adelson, E., Gizzi, M., & Newsome, W. (1986). The analysis of moving visual patterns. Experimental Brain Research 11, 117–152.Google Scholar
Nakayama, K. & Silverman, G.H. (1988). The aperture problem II: Spatial integration of velocity information along contours. Vision Research 28, 747–753.CrossRefGoogle Scholar
Orban, G.A., Kato, H., & Bishop, P.O. (1979). Dimensions and properties of end-zone inhibitory areas in receptive fields of hypercomplex cells in cat striate cortex. Journal of Neurophysiology 42, 833–849.Google Scholar
Orban, G.A. (1991). Quantitative electrophysiology of visual cortical neurons. In Vision and visual dysfunction, Vol 4, The neural basis of visual function, ed. Leventhal, A.G., pp. 173–222. Boca Raton, FL: CRC Press.
Pack, C.C., Gartland, A.J., & Born, R.T. (2004). Integration of contour and terminator signals in visual area MT of alert macaque. The Journal of Neuroscience 24, 3268–3280.Google Scholar
Pack, C.C., Livingstone, M.S., Duffy, K.R., & Born, R.T. (2003). End-stopping and the aperture problem: Two-dimensional motion signals in macaque V1. Neuron 39, 667–680.CrossRefGoogle Scholar
Pack, C.C. & Born, R.T. (2001). Temporal dynamics of a neural solution to the aperture problem in macaque visual area MT. Nature 409, 1040–1042.CrossRefGoogle Scholar
Power, R.P. & Moulden, B. (1992). Spatial gating effects on judged motion of gratings in apertures. Perception 21, 449–463.CrossRefGoogle Scholar
Sanchez-Vives, M.V., Nowak, L.G., & McCormick, D.A. (2000). Membrane mechanisms underlying contrast adaptation in cat area 17 in vivo. The Journal of Neuroscience 20, 4267–4285.Google Scholar
Sceniak, M.P., Hawken, M.J., & Shapley, R. (2001). Visual spatial characterization of macaque V1 neurons. Journal of Neurophysiology 85, 1873–1887.Google Scholar
Sceniak, M.P., Ringach, D.L., Hawken M.J., &Shapley R. (1999). Contrast's effect on spatial summation by macaque V1 neurons. Nature Neuroscience 2, 733–739.CrossRefGoogle Scholar
Shimojo, S., Silverman, G.H., & Nakayama, K. (1989). Occlusion and the solution to the aperture problem for motion. Vision Research 29, 619–626.CrossRefGoogle Scholar
Sillito, A.M. & Versiani, V. (1977). The contribution of excitatory and inhibitory inputs to the length preference of hypercomplex cells in layers II and III of the cat's striate cortex. Journal of Physiology 273, 775–790.CrossRefGoogle Scholar
Skottun, B.C. (1998). A model for end-stopping in the visual cortex. Vision Research 38, 2023–2035.CrossRefGoogle Scholar
Smith, A.T. & Edgar, G.K. (1990). The influence of spatial frequency on perceived temporal frequency and perceived speed. Vision Research 30, 1467–1474.CrossRefGoogle Scholar
Stone, L.S. & Krauzlis, R.J. (2003). Shared motion signals for human perceptual decisions and oculomotor actions. Journal of Vision 3, 725–736.Google Scholar
Stone, L.S. & Thompson, P. (1992). Human speed perception is contrast dependent. Vision Research 32, 1539–1549.CrossRefGoogle Scholar
van Santen, J.P. & Sperling, G. (1985). Elaborated Reichardt detectors. Journal of the Optical Society of America A 2, 300–321.CrossRefGoogle Scholar
Vezzani, S. & Bressan, P. (1999). The Barber pole illusion depends on contrast. Ricerche di Psicologia 23, 69–81.Google Scholar
Wallach, H. (1935). Über visuell wahrgenommene Bewegungsrichtung. Psychologische Forschnung 20, 325–380.CrossRefGoogle Scholar
Watson, A.B. & Ahumada, A.J.Jr. (1985). Model of human visual-motion sensing. Journal of the Optical Society of America A 2, 322–341.CrossRefGoogle Scholar
Watson, A.B. (1983). Detection and recognition of simple spatial forms. In Physical and biological processing of images, eds. Braddick, O.J. & Sleigh, A.C., pp. 100–114. Berlin: Springer-Verlag.CrossRef
Wuerger, S., Shapley, R., & Rubin, N. (1996). On the visually perceived direction of motion by Hans Wallach: 60 years later. Perception 25, 1317–1367.CrossRefGoogle Scholar
Zetsche, C. & Barth, E. (1990). Fundamental limit of linear filters in the visual processing of two dimensional signals. Vision Research 30, 1111–1117.CrossRefGoogle Scholar