Surface regions of illusory images are detected with a slower processing speed than those of luminance-defined images.

Research has shown that the processing time for discriminating illusory contours is longer than for real contours. We know, however, little whether the visual processes, associated with detecting regions of illusory surfaces, are also slower as those responsible for detecting luminance-defined images. Using a speed-accuracy trade-off (SAT) procedure, we measured accuracy as a function of processing time for detecting illusory Kanizsa-type and luminance-defined squares embedded in 2D static luminance noise. The data revealed that the illusory images were detected at slower processing speed than the real images, while the points in time, when accuracy departed from chance, were not significantly different for both stimuli. The classification images for detecting illusory and real squares showed that observers employed similar detection strategies using surface regions of the real and illusory squares. The lack of significant differences between the x-intercepts of the SAT functions for illusory and luminance-modulated stimuli suggests that the detection of surface regions of both images could be based on activation of a single mechanism (the dorsal magnocellular visual pathway). The slower speed for detecting illusory images as compared to luminance-defined images could be attributed to slower processes of filling-in of regions of illusory images within the dorsal pathway.

[1]  N. Rubin,et al.  fMRI Activation in Response to Illusory Contours and Salient Regions in the Human Lateral Occipital Complex , 2003, Neuron.

[2]  M. Seghier,et al.  Functional Neuroimaging Findings on the Human Perception of Illusory Contours , 2022 .

[3]  A. Ahumada.  Classification image weights and internal noise level estimation. , 2002, Journal of vision.

[4]  T. S. Lee,et al.  Dynamics of subjective contour formation in the early visual cortex. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  R. Gregory,et al.  Cognitive Contours , 1972, Nature.

[6]  S. Dakin,et al.  Natural image statistics mediate brightness ‘filling in’ , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[7]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[8]  A. Dale,et al.  The Representation of Illusory and Real Contours in Human Cortical Visual Areas Revealed by Functional Magnetic Resonance Imaging , 1999, The Journal of Neuroscience.

[9]  R. Shapley,et al.  Spatial and Temporal Properties of Illusory Contours and Amodal Boundary Completion , 1996, Vision Research.

[10]  R. von der Heydt,et al.  Illusory contours and cortical neuron responses. , 1984, Science.

[11]  Richard F Murray,et al.  Optimal methods for calculating classification images: weighted sums. , 2002, Journal of vision.

[12]  L. Spillmann,et al.  Phenomena of Illusory Form: Can We Bridge the Gap between Levels of Explanation? , 1995, Perception.

[13]  B. McElree,et al.  The locus of lexical preference effects in sentence comprehension , 1993 .

[14]  John J. Foxe,et al.  Boundary Completion Is Automatic and Dissociable from Shape Discrimination , 2006, The Journal of Neuroscience.

[15]  Erin Shubel,et al.  The spatiotemporal properties of visual completion measured by response classification. , 2006, Journal of vision.

[16]  Asaid Khateb,et al.  Neural processing of illusory and real contours revealed by high‐density ERP mapping , 2002, Neuroreport.

[17]  Barbara Anne Dosher,et al.  The retrieval of sentences from memory: A speed-accuracy study , 1976, Cognitive Psychology.

[18]  Nava Rubin,et al.  Differences in real and illusory shape perception revealed by backward masking , 2005, Vision Research.

[19]  H. Wagner,et al.  Perception and neuronal coding of subjective contours in the owl , 1999, Nature Neuroscience.

[20]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[21]  Y. Benjamini,et al.  Adaptive linear step-up procedures that control the false discovery rate , 2006 .

[22]  R. C. Oldfield.  THE ASSESSMENT AND ANALYSIS OF HANDEDNESS , 1971 .

[23]  John J. Foxe,et al.  Early processing in the human lateral occipital complex is highly responsive to illusory contours but not to salient regions , 2009, The European journal of neuroscience.

[24]  R. Tennant,et al.  Inhibition of leukaemia virus replication by polyadenylic acid. , 1972, Nature: New biology.

[25]  C. Schroeder,et al.  A spatiotemporal profile of visual system activation revealed by current source density analysis in the awake macaque. , 1998, Cerebral cortex.

[26]  L. Welch,et al.  The Effect of Inducer Polarity and Contrast on the Perception of Illusory Figures , 1997, Perception.

[27]  Neil A. Macmillan,et al.  Detection theory: A user's guide, 2nd ed. , 2005 .

[28]  M. Carrasco,et al.  The temporal dynamics of visual search: evidence for parallel processing in feature and conjunction searches. , 1999, Journal of experimental psychology. Human perception and performance.

[29]  John J. Foxe,et al.  The Spatiotemporal Dynamics of Illusory Contour Processing: Combined High-Density Electrical Mapping, Source Analysis, and Functional Magnetic Resonance Imaging , 2002, The Journal of Neuroscience.

[30]  R. Reynolds,et al.  Perception of an Illusory Contour as a Function of Processing Time , 1981, Perception.

[31]  P. Bennett,et al.  Deriving behavioural receptive fields for visually completed contours , 2000, Current Biology.

[32]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. II. Contours bridging gaps , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[33]  Aapo Hyvärinen,et al.  Visual Features Underlying Perceived Brightness as Revealed by Classification Images , 2009, PloS one.

[34]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[35]  O. D. Creutzfeldt,et al.  Neuronal responses to borders with and without luminance gradients in cat visual cortex and dorsal lateral geniculate nucleus , 2004, Experimental Brain Research.

[36]  Philip J. Kellman,et al.  Classification images reveal spatiotemporal contour interpolation , 2007, Vision Research.

[37]  A. Ahumada,et al.  Stimulus Features in Signal Detection , 1971 .

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

[39]  C. Hung,et al.  Real and illusory contour processing in area V1 of the primate: a cortical balancing act. , 2001, Cerebral cortex.

[40]  Ennio Mingolla,et al.  Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations , 1985 .

[41]  D. Field,et al.  Integration of contours: new insights , 1999, Trends in Cognitive Sciences.

[42]  A V Reed,et al.  Speed-Accuracy Trade-Off in Recognition Memory , 1973, Science.

[43]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[44]  Jon Driver,et al.  Parallel detection of Kanizsa subjective figures in the human visual system , 1994, Nature.

[45]  Barbara Anne Dosher,et al.  Empirical approaches to information processing: Speed-accuracy tradeoff functions or reaction time — a reply , 1979 .

[46]  S. Hochstein,et al.  Perceiving illusory contours: figure detection and shape discrimination. , 2008, Journal of vision.

[47]  John C. Schmitt,et al.  Empirical approaches to information processing: Speed-accuracy tradeoff functions or reaction time , 1977 .

[48]  Denis G. Pelli,et al.  Accurate control of contrast on microcomputer displays , 1991, Vision Research.

[49]  Miguel P Eckstein,et al.  Spatial profiles of local and nonlocal effects upon contrast detection/discrimination from classification images. , 2005, Journal of vision.

[50]  R. Gurnsey,et al.  There is No Evidence That Kanizsa-Type Subjective Contours Can Be Detected in Parallel , 1996, Perception.

[51]  J. Bakin,et al.  Visual Responses in Monkey Areas V1 and V2 to Three-Dimensional Surface Configurations , 2000, The Journal of Neuroscience.

[52]  Markus Conci,et al.  The contrasting impact of global and local object attributes on Kanizsa figure detection , 2007, Perception & psychophysics.