Perceptual segmentation and apparent tilt: psychophysical and computational analyses of neural grouping in tilt illusion

I propose a network model for the perception of the global orientation of a bar including the tilt illusion, the misjudgment of the bar orientation in the presence of another intersecting bar. It is most likely that the perception of global orientation, including tilt illusions, results from the grouping of V1 cell responses. I investigated what neural grouping is involved in the perception of global orientation. Psychophysical experiments were carried out in order to investigate phenomenological mechanisms for the neural grouping, and a level of cortical processing involved in the grouping. The specific aim of the experiments is to examine whether the amount of apparent tilt depends on the perceptual segmentation of the bars. The results show that apparent tilt is independent of that which bar the junction belongs to. This suggests that the visual system does not take into account the orientation information at the junction region for the determination of global orientation. In order to investigate the cortical mechanisms underlying the grouping, I developed a neural oscillator network based on colinear synchronization, and simulated the model with various bar junctions. The simulation results show that the intersection of the bars is not grouped together with either bar, which agrees with the phenomenological grouping observed in the psychophysical experiments. The model reproduces, with good quantitative agreement, the apparent tilt measured in comparable psychophysical experiments reported elsewhere.

[1]  L. Finkel,et al.  Characterization of the spatial-frequency spectrum in the perception of shape from texture. , 1995, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  C. Gilbert,et al.  Improvement in visual sensitivity by changes in local context: Parallel studies in human observers and in V1 of alert monkeys , 1995, Neuron.

[3]  Svein Magnussen,et al.  Linear summation of tilt illusion and tilt aftereffect , 1980, Vision Research.

[4]  G. Ermentrout,et al.  Symmetry and phaselocking in chains of weakly coupled oscillators , 1986 .

[5]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[6]  C. Blakemore,et al.  Lateral Inhibition between Orientation Detectors in the Human Visual System , 1970, Nature.

[7]  L. Finkel,et al.  Extraction of perceptually salient contours by striate cortical networks , 1998, Vision Research.

[8]  W. Singer,et al.  Stimulus‐Dependent Neuronal Oscillations in Cat Visual Cortex: Inter‐Columnar Interaction as Determined by Cross‐Correlation Analysis , 1990, The European journal of neuroscience.

[9]  Tomoki Fukai,et al.  A Simple Neural Network Exhibiting Selective Activation of Neuronal Ensembles: From Winner-Take-All to Winners-Share-All , 1997, Neural Computation.

[10]  Samuel Kaski,et al.  Winner-take-all networks for physiological models of competitive learning , 1994, Neural Networks.

[11]  H R Wilson Nonlinear processes in visual pattern discrimination. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

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

[13]  Alan L. Yuille,et al.  A Winner-Take-All Mechanism Based on Presynaptic Inhibition Feedback , 1989, Neural Computation.

[14]  Ko Sakai,et al.  A Cortical Mechanism Underlying the Perception of Bar Orientation in Tilt Illusions Based on Figure Segmentation and Population Coding , 1997, ICONIP.

[15]  Ko Sakai,et al.  Retinotopic coding and neural grouping in tilt illusion , 1999, Neurocomputing.

[16]  D. Heeger Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.