scholarly journals Global shape processing: A behavioral and electrophysiological analysis of both contour and texture processing

2015 ◽  
Vol 15 (13) ◽  
pp. 18 ◽  
Author(s):  
Vanessa K. Bowden ◽  
J. Edwin Dickinson ◽  
Allison M. Fox ◽  
David R. Badcock
Keyword(s):  
Global Shape ◽  
Electrophysiological Analysis ◽  
Shape Processing ◽  
Texture Processing

scholarly journals Global shape processing: Which parts form the whole?

2010 ◽  
Vol 10 (6) ◽  
pp. 16-16 ◽  
Author(s):  
J. Bell ◽  
S. Hancock ◽  
F. A. A. Kingdom ◽  
J. W. Peirce
Keyword(s):  
Global Shape ◽  
Shape Processing

scholarly journals Capsule Networks as Recurrent Models ofGrouping and Segmentation

2019 ◽  
Author(s):  
Adrien Doerig ◽  
Lynn Schmittwilken ◽  
Bilge Sayim ◽  
Mauro Manassi ◽  
Michael H. Herzog
Keyword(s):  
Neural Networks ◽  
Visual System ◽  
Convolutional Neural Networks ◽  
Human Vision ◽  
Global Shape ◽  
Grouping And Segmentation ◽  
Segmentation Process ◽  
Shape Processing ◽  
Visual Crowding ◽  
Recurrent Connections

AbstractClassically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we previously showed that no classic model of vision, including ffCNNs, can explain human global shape processing (1). Here, we show that Capsule Neural Networks (CapsNets; 2), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. We also show that ffCNNs and standard recurrent CNNs do not, suggesting that the grouping and segmentation capabilities of CapsNets are crucial. Furthermore, we provide psychophysical evidence that grouping and segmentation are implemented recurrently in humans, and show that CapsNets reproduce these results well. We discuss why recurrence seems needed to implement grouping and segmentation efficiently. Together, we provide mutually reinforcing psychophysical and computational evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.Author SummaryFeedforward Convolutional Neural Networks (ffCNNs) have revolutionized computer vision and are deeply transforming neuroscience. However, ffCNNs only roughly mimic human vision. There is a rapidly expanding body of literature investigating differences between humans and ffCNNs. Several findings suggest that, unlike humans, ffCNNs rely mostly on local visual features. Furthermore, ffCNNs lack recurrent connections, which abound in the brain. Here, we use visual crowding, a well-known psychophysical phenomenon, to investigate recurrent computations in global shape processing. Previously, we showed that no model based on the classic feedforward framework of vision can explain global effects in crowding. Here, we show that Capsule Neural Networks (CapsNets), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. ffCNNs and recurrent CNNs with lateral and top-down recurrent connections do not, suggesting that grouping and segmentation are crucial for human-like global computations. Based on these results, we hypothesize that one computational function of recurrence is to efficiently implement grouping and segmentation. We provide psychophysical evidence that, indeed, grouping and segmentation is based on time consuming recurrent processes in the human brain. CapsNets reproduce these results too. Together, we provide mutually reinforcing computational and psychophysical evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.

scholarly journals Global shape processing involves a hierarchy of integration stages

2011 ◽  
Vol 51 (15) ◽  
pp. 1760-1766 ◽  
Author(s):  
Jason Bell ◽  
Elena Gheorghiu ◽  
Robert F. Hess ◽  
Frederick A.A. Kingdom
Keyword(s):  
Global Shape ◽  
Shape Processing

Global Shape Processing Deficits Are Amplified by Temporal Masking in Migraine

2013 ◽  
Vol 54 (2) ◽  
pp. 1160 ◽  
Author(s):  
Doreen Wagner ◽  
Velitchko Manahilov ◽  
Gael E. Gordon ◽  
Gunter Loffler
Keyword(s):  
Global Shape ◽  
Shape Processing ◽  
Temporal Masking ◽  
Processing Deficits

scholarly journals Global shape processing involves feature-selective and feature-agnostic coding mechanisms

2014 ◽  
Vol 14 (11) ◽  
pp. 12-12
Author(s):  
J. Bell ◽  
M. Forsyth ◽  
D. R. Badcock ◽  
F. A. A. Kingdom
Keyword(s):  
Global Shape ◽  
Shape Processing

scholarly journals Emergence of Global Shape Processing Continues Through Adolescence

2009 ◽  
Vol 80 (1) ◽  
pp. 162-177 ◽  
Author(s):  
K. Suzanne Scherf ◽  
Marlene Behrmann ◽  
Ruth Kimchi ◽  
Beatriz Luna
Keyword(s):  
Global Shape ◽  
Shape Processing

scholarly journals Missing the big picture: impaired development of global shape processing in autism

2008 ◽  
Vol 1 (2) ◽  
pp. 114-129 ◽  
Author(s):  
K. Suzanne Scherf ◽  
Beatriz Luna ◽  
Ruth Kimchi ◽  
Nancy Minshew ◽  
Marlene Behrmann
Keyword(s):  
Global Shape ◽  
Shape Processing ◽  
Big Picture

scholarly journals Patch-clamp and multi-electrode array electrophysiological analysis in acute mouse brain slices

2021 ◽  
Vol 2 (2) ◽  
pp. 100442
Author(s):  
Kevin M. Manz ◽  
Justin K. Siemann ◽  
Douglas G. McMahon ◽  
Brad A. Grueter
Keyword(s):  
Patch Clamp ◽  
Mouse Brain ◽  
Brain Slices ◽  
Electrode Array ◽  
Electrophysiological Analysis ◽  
Multi Electrode Array
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