Crowding Models

Crowding Models is a theoretical framework in visual perception research that specifies the mechanisms and constraints by which nearby visual elements interfere with object recognition in peripheral vision, distinguishing it from general models of visual processing by focusing on the spatial interactions underlying crowding phenomena.

136

Publications

8.3K

Citations

291

Authors

192

Institutions

Publications per year

2017–2026

38

Variants

Crowding Model

Authors

291

  1. MH
    Michael H. Herzog

    École Polytechnique Fédérale de Lausanne, Switzerland

    13 publications · 733 citations

    12

    h-index

  2. PJ
    Peter J. Bex

    Harvard University, United States

    6 publications · 348 citations

    6

    h-index

  3. MM
    Mauro Manassi

    University of California, Berkeley, United States

    6 publications · 452 citations

    6

    h-index

  4. BS
    Bilge Sayim

    Centre National de la Recherche Scientifique, France

    5 publications · 626 citations

    5

    h-index

  5. AB
    Alban Bornet

    École Polytechnique Fédérale de Lausanne, Switzerland

    5 publications · 130 citations

    5

    h-index

1–5 of 291

Venues

  1. Journal of Vision

    33 publications · 2.3K citations

    25

    h-index

  2. Scientific Reports

    5 publications · 126 citations

    5

    h-index

  3. Journal of Parasitology

    4 publications · 171 citations

    4

    h-index

  4. Journal of Personality and Social Psychology

    4 publications · 517 citations

    4

    h-index

  5. Vision Research

    4 publications · 315 citations

    4

    h-index

1–5