Deep Neural Networks as a Computational Model for Human Shape Sensitivity
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Title
Deep Neural Networks as a Computational Model for Human Shape Sensitivity
Authors
Keywords
Human performance, Object recognition, Vision, Sensory perception, Neural networks, Behavior, Monkeys, Visual cortex
Journal
PLoS Computational Biology
Volume 12, Issue 4, Pages e1004896
Publisher
Public Library of Science (PLoS)
Online
2016-04-29
DOI
10.1371/journal.pcbi.1004896
References
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Note: Only part of the references are listed.- Dissociations and Associations between Shape and Category Representations in the Two Visual Pathways
- (2016) S. Bracci et al. JOURNAL OF NEUROSCIENCE
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- Orientation-Cue Invariant Population Responses to Contrast-Modulated and Phase-Reversed Contour Stimuli in Macaque V1 and V2
- (2014) Xu An et al. PLoS One
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- (2014) D. L. K. Yamins et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Greater sensitivity to nonaccidental than metric shape properties in preschool children
- (2014) Ori Amir et al. VISION RESEARCH
- A conceptual framework of computations in mid-level vision
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- A framework for streamlining research workflow in neuroscience and psychology
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- scikit-image: image processing in Python
- (2014) Stéfan van der Walt et al. PeerJ
- Greater sensitivity to nonaccidental than metric changes in the relations between simple shapes in the lateral occipital cortex
- (2012) Jiye G. Kim et al. NEUROIMAGE
- The Role of Attention in Figure-Ground Segregation in Areas V1 and V4 of the Visual Cortex
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- A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations.
- (2012) Johan Wagemans et al. PSYCHOLOGICAL BULLETIN
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- The neural basis for shape preferences
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- Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey
- (2008) Nikolaus Kriegeskorte et al. NEURON
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- (2008) Johan Wagemans et al. PERCEPTION
- Pigeons and humans are more sensitive to nonaccidental than to metric changes in visual objects
- (2007) Olga F. Lazareva et al. BEHAVIOURAL PROCESSES
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