Figure—Ground Segregation in a Recurrent Network Architecture
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Here we propose a model of how the visual brain segregates textured scenes into figures and background. During texture segregation, locations where the properties of texture elements change abruptly are assigned to boundaries, whereas image regions that are relatively homogeneous are grouped together. Boundary detection and grouping of image regions require different connection schemes, which are accommodated in a single network architecture by implementing them in different layers. As a result, all units carry signals related to boundary detection as well as grouping of image regions, in accordance with cortical physiology. Boundaries yield an early enhancement of network responses, but at a later point, an entire figural region is grouped together, because units that respond to it are labeled with enhanced activity. The model predicts which image regions are preferentially perceived as figure or as background and reproduces the spatio-temporal profile of neuronal activity in the visual cortex during texture segregation in intact animals, as well as in animals with cortical lesions.
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Animal and human studies of vision support the hypothesis that predictive signals are sent from higher hierarchical levels predominantly via NMDA receptor signaling and prediction errors via AMPA receptors.
“Animal and human studies of vision support this hypothesis (16–19).”
From The Predictive Coding Account of Psychosis (2018) · cites this paper as [18]
The Feedback Pathway describes signals fed back from higher to lower visual levels, and the Discussion says physiological findings support aspects of that feedback model. Neither passage addresses predictive signals or prediction errors, or assigns them to NMDA and AMPA receptors; searches for those terms found no relevant passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredBack projections may be important for visual awareness and beyond tasks such as visual categorization for perceptual organization and figure-ground segmentation or curve tracing.
“In addition, back projections may be important for visual awareness and beyond tasks such as visual categorization for perceptual organization and figure-ground segmentation (48-50) or curve tracing (51).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [48]
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Self-citationNeuronal integration in visual cortex elevates face category tuning to conscious face perception
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Boundary assignment in a recurrent network architecture
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Receptive field focus of visual area V4 neurons determines responses to illusory surfaces
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Figure-ground interaction in the human visual cortex
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Self-citationShape Saliency Modulates Contextual Processing in the Human Lateral Occipital Complex
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Human perceptual and metacognitive decision-making rely on distinct brain networks
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End-to-end optimization of prosthetic vision
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Spike Synchrony Reveals Emergence of Proto-Objects in Visual Cortex
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Occipital network for figure/ground organization
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Feed-Forward Segmentation of Figure-Ground and Assignment of Border-Ownership
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Ultra-Rapid Scene Categorization with a Wave of Spikes
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Preattentive surface and contour grouping in Kanizsa figures: Evidence from parietal extinction
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Differential Contribution of Early Visual Areas to the Perceptual Process of Contour Processing
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The Segmentation of Proto-Objects in the Monkey Primary Visual Cortex
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Self-citationFeedforward object-vision models only tolerate small image variations compared to human
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Perceptual Continuity and the Emergence of Perceptual Persistence in the Ventral Visual Pathway
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A Contrast and Surface Code Explains Complex Responses to Black and White Stimuli in V1
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Visual Processing in Rapid-Chase Systems: Image Processing, Attention, and Awareness
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Self-citationThe contrasting impact of global and local object attributes on Kanizsa figure detection
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Neural dynamics of feedforward and feedback processing in figure-ground segregation
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Disentangling neural mechanisms for perceptual grouping
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A source for awareness-dependent figure–ground segregation in human prefrontal cortex
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Proline and COMT Status Affect Visual Connectivity in Children with 22q11.2 Deletion Syndrome
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A conceptual framework of computations in mid-level vision
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Competition-strength-dependent ground suppression in figure–ground perception
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- Authors
- Roelfsema, P. R., Lamme, V. A. F., Spekreijse, H., & Bosch, H.
- Affiliations
- University of Amsterdam (NL)
- University of Geneva (CH)
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press - Journals
- Published
- 2002-05
- Volume
- 14
- Issue
- 4
- Pages
- 525-537
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2109887449
- Cited by
- 254
- References
- 64
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 6 · 2022: 13 · 2021: 6 · 2020: 4 · 2019: 4 · 2018: 12 · 2017: 6 · 2016: 12 · 2015: 15 · 2014: 14 · 2013: 10 · 2012: 14
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Koninklijke Nederlandse Akademie van Wetenschappen
- Universiteit van Amsterdam
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- figure-ground segregation, texture segregation, recurrent neural networks, boundary detection, regional clustering, visual cortex, cortical lesions, neuronal activity dynamics, visual perception
- MeSH terms
- Feedback, Humans, Models, Neurological, Nerve Net, Visual Cortex, Visual Pathways, Visual Perception
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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