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Beyond Bouma's window: How to explain global aspects of crowding?
Journal article

Beyond Bouma's window: How to explain global aspects of crowding?

  • Doerig A Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Bornet A Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Rosenholtz R Department of Brain and Cognitive Sciences, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States of America.
  • Francis G Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States of America.
  • Clarke AM Laboratory of Computational Vision, Psychology Department, Bilkent University, Ankara, Turkey.
  • Herzog MH Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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  • 2019-05-11
Published in:
  • PLoS computational biology. - 2019
Computer Simulation
Crowding
Discrimination, Psychological
Humans
Pattern Recognition, Visual
Perceptual Masking
Space Perception
Visual Fields
English In crowding, perception of an object deteriorates in the presence of nearby elements. Although crowding is a ubiquitous phenomenon, since elements are rarely seen in isolation, to date there exists no consensus on how to model it. Previous experiments showed that the global configuration of the entire stimulus must be taken into account. These findings rule out simple pooling or substitution models and favor models sensitive to global spatial aspects. In order to investigate how to incorporate global aspects into models, we tested a large number of models with a database of forty stimuli tailored for the global aspects of crowding. Our results show that incorporating grouping like components strongly improves model performance.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://roar.hep-bejune.ch/global/documents/190778
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