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Cited by 534Visual perception and processing mechanismsFace Recognition and PerceptionVisual Attention and Saliency Detection

The role of the primary visual cortex in higher level vision

Open access
Lee, Tai Sing, Mumford, David, Romero, Richard, Lamme, Victor A.F.·1998·Vision Research
DOI 10.1016/s0042-6989(97)00464-1
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Abstract

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons' responses reflecting higher order perceptual computations related to Ullman's (Cognition 1984; 18:97-159) visual routines and Marr's (Vision NJ: Freeman 1982) full primal sketch, 2 1/2D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons' precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later part of its neurons' activities.

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  • The only remedy found in the computational literature is not to jump to conclusions but to allow multiple high-probability values for features or hypotheses to stay alive until longer feedback loops have had a chance to exert an influence.

    “The only remedy that has been found in the computational literature is not to jump to conclusions but to allow multiple high-probability values for the features or hypotheses to stay alive until longer feedback loops have had a chance to exert an influence.”

    From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [21]

    Section 2 (page unavailable) says visual processes must operate concurrently through feed-forward and feedback loops, supporting the feedback aspect of the claim. But the copy does not say that the only computational remedy is to keep multiple high-probability feature values or hypotheses alive while longer loops act. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The authors suggest that these ideas could be formulated as a hierarchical Bayesian system and that Bayesian belief propagation and particle filtering are relevant to understanding interactive computations in the visual cortex.

    “Here we suggest that these ideas could be formulated in the form of a hierarchical Bayesian system and that ideas from Bayesian belief propagation 19 and particle filtering 21,23,25 are relevant to understanding these interactive computations in the visual cortex.”

    From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [21]

    Section 2 describes visual processing as concurrent interactions through feed-forward and feedback loops across the visual hierarchy, supporting the interactive-computation premise. The cited paper does not propose a hierarchical Bayesian system or identify Bayesian belief propagation or particle filtering as ways to understand those computations; those are additions in the citing sentence. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Bayesian belief propagation and particle filtering are the most successful computer vision algorithms to date.

    “Bayesian belief propagation 18 -20 and particle filtering 21,22 are the most successful computer vision algorithms to date.”

    From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [21]

    Section 4.3, “Computational interpretations,” discusses interactive surface reconstruction, but does not evaluate Bayesian belief propagation or particle filtering. Searches for those terms and for the claimed ranking found no passage in our copy calling them the most successful computer vision algorithms. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Lee, T. S., Mumford, D., Romero, R., & Lamme, V. A. F.
Affiliations
  • Center for the Neural Basis of Cognition (US)
  • Brown University (US)
  • University of Amsterdam (NL)
Corresponding author
Lee, Tai Sing
ORCID
Venue
Vision Research
Publisher
Elsevier BV
Published
1998-08
Volume
38
Issue
15-16
Pages
2429-2454
ISSN
0042-6989 (ISSN-L)
DOI
10.1016/s0042-6989(97)00464-1
OpenAlex
W2137105523
Cited by
534
References
2
Citations by year
2026: 11 · 2025: 20 · 2024: 5 · 2023: 10 · 2022: 10 · 2021: 8 · 2020: 14 · 2019: 16 · 2018: 15 · 2017: 18 · 2016: 17 · 2015: 24 · 2014: 33 · 2013: 12 · 2012: 25
FWCI
13.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Vision Research (publishedVersion)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • UvA-DARE (University of Amsterdam) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
Funders
  • National Science Foundation
  • National Institutes of Health
  • National Eye Institute (EY 00676)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
primary visual cortex, area V1, visual routines, primal sketch, 3D model, object recognition, spatial precision, extrastriate cortex
MeSH terms
Action Potentials, Animals, Brain Mapping, Cues, Fixation, Ocular, Form Perception, Fovea Centralis, Macaca mulatta, Models, Neurological, Saccades, Visual Cortex

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