Crowding and Binding: Not All Feature Dimensions Behave in the Same Way

Humans often fail to identify a target because of nearby flankers. The nature and stage(s) at which this “crowding” occurs are unclear, and whether crowding operates via a common mechanism across visual dimensions is unknown. Using a dual estimation report, we quantitatively assessed the processing of each feature alone and in conjunction with another feature both within and between dimensions. Crowding emerged due to confusion between orientations or colors of target and flankers, but averaging of their spatial frequencies (SFs). Furthermore, crowding of orientation and color were independent, but crowding of orientation and SF were interdependent. This qualitative difference of crowding errors across dimensions revealed a tight link between crowding and ‘illusory conjunctions’ (mis-binding of feature dimensions). These results and a computational model suggest that crowding and illusory conjunction in the visual periphery are due to pooling across a joint coding of orientation and spatial frequencies but not of color.

[1]  Timothy F. Brady,et al.  Modeling visual working memory with the MemToolbox. , 2013, Journal of vision.

[2]  Nancy Kanwisher,et al.  Feature-Binding Errors After Eye Movements and Shifts of Attention , 2014, Psychological science.

[3]  S. Klein,et al.  Positional uncertainty in peripheral and amblyopic vision , 1987, Vision Research.

[4]  RussLL L. Ds Vnlos,et al.  SPATIAL FREQUENCY SELECTIVITY OF CELLS IN MACAQUE VISUAL CORTEX , 2022 .

[5]  David Whitney,et al.  Multi-level Crowding and the Paradox of Object Recognition in Clutter , 2018, Current Biology.

[6]  Hans Strasburger,et al.  Source confusion is a major cause of crowding. , 2013, Journal of vision.

[7]  M. Carrasco,et al.  Exogenous attention and color perception: Performance and appearance of saturation and hue , 2006, Vision Research.

[8]  Eero P. Simoncelli,et al.  Origin and Function of Tuning Diversity in Macaque Visual Cortex , 2015, Neuron.

[9]  H. BOUMA,et al.  Interaction Effects in Parafoveal Letter Recognition , 1970, Nature.

[10]  S J Anderson,et al.  Peripheral spatial vision: limits imposed by optics, photoreceptors, and receptor pooling. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[11]  S. Luck,et al.  Discrete fixed-resolution representations in visual working memory , 2008, Nature.

[12]  Anirvan S. Nandy,et al.  Saccade-confounded image statistics explain visual crowding , 2012, Nature Neuroscience.

[13]  Marisa Carrasco,et al.  Rapid and long-lasting reduction of crowding through training. , 2015, Journal of vision.

[14]  Ruth Kimchi,et al.  Multiple Level Crowding: Crowding at the Object Parts Level and at the Object Configural level , 2015, Perception.

[15]  D. Pelli,et al.  Are faces processed like words? A diagnostic test for recognition by parts. , 2005, Journal of vision.

[16]  Ruth Kimchi,et al.  Crowding and perceptual organization: Target's objecthood influences the relative strength of part-level and configural-level crowding. , 2017, Journal of vision.

[17]  S. Klein,et al.  Sampling in spatial vision , 1986, Nature.

[18]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[19]  R. Hess,et al.  The functional area for summation to threshold for sinusoidal gratings , 1978, Vision Research.

[20]  J. Robson,et al.  Probability summation and regional variation in contrast sensitivity across the visual field , 1981, Vision Research.

[21]  D. Tolhurst,et al.  On the variety of spatial frequency selectivities shown by neurons in area 17 of the cat , 1981, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[22]  Steven C Dakin,et al.  Positional averaging explains crowding with letter-like stimuli , 2009, Proceedings of the National Academy of Sciences.

[23]  D. Levi,et al.  The two-dimensional shape of spatial interaction zones in the parafovea , 1992, Vision Research.

[24]  Nicole C. Rust,et al.  Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.

[25]  Edward Awh,et al.  Spatial attention, preview, and popout: which factors influence critical spacing in crowded displays? , 2007, Journal of vision.

[26]  V. Lollo The feature-binding problem is an ill-posed problem , 2012, Trends in Cognitive Sciences.

[27]  D. Pelli,et al.  The uncrowded window of object recognition , 2008, Nature Neuroscience.

[28]  Edward F. Ester,et al.  Substitution and pooling in visual crowding induced by similar and dissimilar distractors. , 2015, Journal of vision.

[29]  A. Treisman,et al.  Illusory conjunctions in the perception of objects , 1982, Cognitive Psychology.

[30]  Johan Wagemans,et al.  Crowding with conjunctions of simple features. , 2007, Journal of vision.

[31]  Terry Caelli,et al.  Discrimination thresholds in the two-dimensional spatial frequency domain , 1983, Vision Research.

[32]  Richard D. Morey,et al.  Confidence Intervals from Normalized Data: A correction to Cousineau (2005) , 2008 .

[33]  P. Lennie,et al.  Chromatic mechanisms in lateral geniculate nucleus of macaque. , 1984, The Journal of physiology.

[34]  Frans W Cornelissen,et al.  On the generality of crowding: visual crowding in size, saturation, and hue compared to orientation. , 2007, Journal of vision.

[35]  S. Dakin,et al.  Crowding follows the binding of relative position and orientation. , 2012, Journal of vision.

[36]  I. Rentschler,et al.  Contrast thresholds for identification of numeric characters in direct and eccentric view , 1991, Perception & psychophysics.

[37]  D. Levi,et al.  Visual crowding: a fundamental limit on conscious perception and object recognition , 2011, Trends in Cognitive Sciences.

[38]  D. Pelli Crowding: a cortical constraint on object recognition , 2008, Current Opinion in Neurobiology.

[39]  Paul M Bays,et al.  The precision of visual working memory is set by allocation of a shared resource. , 2009, Journal of vision.

[40]  Edward Awh,et al.  Visual crowding cannot be wholly explained by feature pooling. , 2014, Journal of experimental psychology. Human perception and performance.

[41]  P. H. Schiller,et al.  Spatial frequency and orientation tuning dynamics in area V1 , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[42]  J. Lund,et al.  Compulsory averaging of crowded orientation signals in human vision , 2001, Nature Neuroscience.

[43]  E. Vul,et al.  Independent Sampling of Features Enables Conscious Perception of Bound Objects , 2010, Psychological science.

[44]  D. Levi Crowding—An essential bottleneck for object recognition: A mini-review , 2008, Vision Research.

[45]  D. Pelli,et al.  Crowding is unlike ordinary masking: distinguishing feature integration from detection. , 2004, Journal of vision.

[46]  R. Rosenholtz,et al.  Pooling of continuous features provides a unifying account of crowding , 2016, Journal of vision.

[47]  Isabelle Mareschal,et al.  Cortical distance determines whether flankers cause crowding or the tilt illusion. , 2010, Journal of vision.

[48]  Steven C. Dakin,et al.  Crowding Changes Appearance , 2010, Current Biology.

[49]  P. Bex,et al.  A Unifying Model of Orientation Crowding in Peripheral Vision , 2015, Current Biology.

[50]  Eero P. Simoncelli,et al.  Metamers of the ventral stream , 2011, Nature Neuroscience.

[51]  Anke Huckauf,et al.  What various kinds of errors tell us about lateral masking effects , 2002 .

[52]  Emma Wu Dowd,et al.  Object-Feature Binding Survives Dynamic Shifts of Spatial Attention , 2018, Psychological science.

[53]  Jos B. T. M. Roerdink,et al.  A Neurophysiologically Plausible Population Code Model for Feature Integration Explains Visual Crowding , 2010, PLoS Comput. Biol..