A moving-barber-pole illusion.

In the barber-pole illusion (BPI), a diagonally moving grating is perceived as moving vertically because of the shape of the vertically oriented window through which it is viewed-a strong shape-motion interaction. We introduce a novel stimulus-the moving barber pole-in which a diagonal, drifting sinusoidal carrier is windowed by a raised, vertical, drifting sinusoidal modulator that moves independently of the carrier. In foveal vision, the moving-barber-pole stimulus can be perceived as several active barber poles drifting horizontally but also as other complex dynamic patterns. In peripheral vision, pure vertical motion (the moving-barber-pole illusion [MBPI]) is perceived for a wide range of conditions. In foveal vision, the MBPI is observed, but only when the higher-order modulator motion is masked. Theories to explain the BPI make indiscriminable predictions in a standard barber-pole display. But, in moving-barber-pole stimuli, the motion directions of features (e.g., end stops) of the first-order carrier and of the higher-order modulator are all different from the MBPI. High temporal frequency stimuli viewed peripherally greatly reduce the effectiveness of higher-order motion mechanisms and, ideally, isolate a single mechanism responsible for the MBPI. A three-stage motion-path integration mechanism that (a) computes local motion energies, (b) integrates them for a limited time period along various spatial paths, and (c) selects the path with the greatest motion energy, quantitatively accounts for these high-frequency data. The MBPI model also accounts for the perceived motion-direction in peripherally viewed moving-barber-pole stimuli that do and do not exhibit the MBPI over the entire range of modulator (0-10 Hz) and carrier (2.5-10 Hz) temporal frequencies tested.

[1]  Christopher C. Pack,et al.  The role of V1 surround suppression in MT motion integration. , 2010, Journal of neurophysiology.

[2]  Frans A. J. Verstraten,et al.  Spatial summation and its interaction with the temporal integration mechanism in human motion perception , 1994, Vision Research.

[3]  Frans A. J. Verstraten,et al.  An analysis of the temporal integration mechanism in human motion perception , 1994, Vision Research.

[4]  Hans Wallach.  Über visuell wahrgenommene Bewegungsrichtung , 1935 .

[5]  A. T. Smith,et al.  Human visual area V6 is selectively responsive to egomotion-compatible optic flow , 2009 .

[6]  David R. Badcock,et al.  Second-order orientation cues to the axis of motion , 2009, Vision Research.

[7]  F. Kooi.  Local direction of edge motion causes and abolishes the barberpole illusion , 1993, Vision Research.

[8]  Jonathan A. Marshall,et al.  Self-organizing neural networks for perception of visual motion , 1990, Neural Networks.

[9]  Leslie G. Ungerleider.  Two cortical visual systems , 1982 .

[10]  G Sperling,et al.  Second-order reversed phi , 1999, Perception & psychophysics.

[11]  S. Wuerger,et al.  Perception of Moving Lines: Interactions Between Local Perpendicular Signals and 2D Motion Signals , 1997, Vision Research.

[12]  N. Kanwisher,et al.  Activation in Human MT/MST by Static Images with Implied Motion , 2000, Journal of Cognitive Neuroscience.

[13]  Wilson S. Geisler,et al.  Motion streaks provide a spatial code for motion direction , 1999, Nature.

[14]  G. Sperling,et al.  The functional architecture of human visual motion perception , 1995, Vision Research.

[15]  D Jancke,et al.  Orientation Formed by a Spot's Trajectory: A Two-Dimensional Population Approach in Primary Visual Cortex , 2000, The Journal of Neuroscience.

[16]  Timothy Ledgeway,et al.  The spatial frequency and orientation selectivity of the mechanisms that extract motion-defined contours , 2006, Vision Research.

[17]  Robert F Hess,et al.  Rules for combining the outputs of local motion detectors to define simple contours , 2002, Vision Research.

[18]  P.-J. Hsieh,et al.  The infinite regress illusion reveals faulty integration of local and global motion signals , 2006, Vision Research.

[19]  K. Nakayama,et al.  Occlusion and the solution to the aperture problem for motion , 1989, Vision Research.

[20]  G Sperling,et al.  Two motion perception mechanisms revealed through distance-driven reversal of apparent motion. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[21]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[22]  O. Andreassen,et al.  Mice Deficient in Cellular Glutathione Peroxidase Show Increased Vulnerability to Malonate, 3-Nitropropionic Acid, and 1-Methyl-4-Phenyl-1,2,5,6-Tetrahydropyridine , 2000, The Journal of Neuroscience.

[23]  L. Stone,et al.  The Barberplaid Illusion: Plaid Motion is Biased by Elongated Apertures , 1996, Vision Research.

[24]  J. Zanker,et al.  The Directional Tuning of the Barber-Pole Illusion , 2001, Perception.

[25]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[26]  K. Nakayama,et al.  Visual thresholds for shearing motion in monkey and man , 1985, Vision Research.

[27]  J. Koenderink,et al.  Spatiotemporal integration in the detection of coherent motion , 1984, Vision Research.

[28]  Margaret S Livingstone,et al.  End-Stopping and the Aperture Problem Two-Dimensional Motion Signals in Macaque V1 , 2003, Neuron.

[29]  Eero P. Simoncelli,et al.  A model of neuronal responses in visual area MT , 1998, Vision Research.

[30]  John Ross,et al.  Direct Evidence That “Speedlines” Influence Motion Mechanisms , 2002, The Journal of Neuroscience.

[31]  L. Bowns.  Evidence for a Feature Tracking Explanation of Why Type II Plaids Move in the Vector Sum Direction at Short Durations , 1996, Vision Research.

[32]  S. McKee,et al.  Detecting a trajectory embedded in random-direction motion noise , 1995, Vision Research.

[33]  I. Ohzawa,et al.  Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. , 1993, Journal of neurophysiology.

[34]  D. G. Albrecht,et al.  Motion direction signals in the primary visual cortex of cat and monkey. , 2001, Visual neuroscience.

[35]  John A. Perrone,et al.  A visual motion sensor based on the properties of V1 and MT neurons , 2004, Vision Research.

[36]  E. Adelson,et al.  Phenomenal coherence of moving visual patterns , 1982, Nature.

[37]  Allison M McKendrick,et al.  Pattern cues disambiguate perceived direction in simple moving stimuli , 2003, Vision Research.

[38]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[39]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[40]  G. Mather,et al.  Psychophysical evidence for interactions between visual motion and form processing at the level of motion integrating receptive fields , 2012, Neuropsychologia.

[41]  Ennio Mingolla,et al.  Monocular occlusion cues alter the influence of terminator motion in the barber pole phenomenon , 1998, Vision Research.

[42]  Christopher C. Pack,et al.  Integration of Contour and Terminator Signals in Visual Area MT of Alert Macaque , 2004, The Journal of Neuroscience.

[43]  D. C. Van Essen,et al.  Concurrent processing streams in monkey visual cortex , 1988, Trends in Neurosciences.

[44]  H. Barlow.  Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .

[45]  Luigi F. Cuturi,et al.  The effect of spatial orientation on detecting motion trajectories in noise , 2011, Vision Research.

[46]  S. McKee,et al.  Temporal coherence theory for the detection and measurement of visual motion , 1995, Vision Research.

[47]  Gregory C. DeAngelis,et al.  Receptive-field dynamics in the central visual pathways , 1995, Trends in Neurosciences.

[48]  Eric Castet,et al.  The extrinsic/intrinsic classification of two-dimensional motion signals with barber-pole stimuli , 1999, Vision Research.

[49]  T. Lombrozo,et al.  Flexibility of spatial averaging in visual perception , 2005, Proceedings of the Royal Society B: Biological Sciences.

[50]  G. Sperling,et al.  The dimensionality of texture-defined motion: a single channel theory , 1993, Vision Research.

[51]  J. Robson.  Spatial and Temporal Contrast-Sensitivity Functions of the Visual System , 1966 .

[52]  Colin W. G. Clifford,et al.  Corrections to: gaze categorization under uncertainty: psychophysics and modeling , 2013 .

[53]  M. Shiffrar,et al.  Different motion sensitive units are involved in recovering the direction of moving lines , 1993, Vision Research.

[54]  H. Wilson,et al.  A psychophysically motivated model for two-dimensional motion perception , 1992, Visual Neuroscience.

[55]  Philipp Berens,et al.  CircStat: AMATLABToolbox for Circular Statistics , 2009, Journal of Statistical Software.

[56]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[57]  Stephen Grossberg,et al.  Neural dynamics of motion integration and segmentation within and across apertures , 2001, Vision Research.

[58]  D J Heeger,et al.  Model for the extraction of image flow. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[59]  A J Ahumada,et al.  Model of human visual-motion sensing. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[60]  Gianluca Campana,et al.  Interactions between motion and form processing in the human visual system , 2013, Front. Comput. Neurosci..

[61]  H. Barlow,et al.  Convergent evidence for the visual analysis of optic flow through anisotropic attenuation of high spatial frequencies. , 2004, Journal of vision.

[62]  J. Perrone.  A neural-based code for computing image velocity from small sets of middle temporal (MT/V5) neuron inputs. , 2012, Journal of vision.

[63]  Mark Edwards,et al.  Motion streaks improve motion detection , 2007, Vision Research.

[64]  Linda Bowns.  Taking the energy out of spatio-temporal energy models of human motion processing: The Component Level Feature Model , 2011, Vision Research.

[65]  Paul R. Schrater,et al.  Mechanisms of visual motion detection , 2000, Nature Neuroscience.

[66]  Frank Bremmer,et al.  Neural correlates of implied motion , 2003, Nature.

[67]  John H. R. Maunsell,et al.  Visual processing in monkey extrastriate cortex. , 1987, Annual review of neuroscience.

[68]  D. Burr,et al.  Spatial summation properties of directionally selective mechanisms in human vision. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[69]  Shaul Hochstein,et al.  Isolating the effect of one-dimensional motion signals on the perceived direction of moving two-dimensional objects , 1993, Vision Research.

[70]  J. van Santen,et al.  Temporal covariance model of human motion perception. , 1984, Journal of the Optical Society of America. A, Optics and image science.

[71]  Rosilari Bellacosa Marotti,et al.  Motion-form interactions beyond the motion integration level: evidence for interactions between orientation and optic flow signals. , 2013, Journal of vision.

[72]  David R. Badcock,et al.  Coherent global motion in the absence of coherent velocity signals , 2000, Current Biology.

[73]  J. Lorenceau,et al.  Directional shifts in the barber pole illusion: Effects of spatial frequency, spatial adaptation, and lateral masking , 2006, Visual Neuroscience.

[74]  J. van Santen,et al.  Elaborated Reichardt detectors. , 1985, Journal of the Optical Society of America. A, Optics and image science.