Feature-based Attention

Feature-Based Attention is a cognitive mechanism within the domain of visual attention that selectively enhances the processing of specific perceptual features, such as color, orientation, or motion, across the visual field, thereby prioritizing the neural representation of attended features relative to unattended ones.

2.9K

Publications

237.4K

Citations

8.7K

Authors

1.8K

Institutions

Publications per year

2017–2026

1.8K

Authors

8.7K

  1. MC
    Marisa Carrasco

    New York University, United States

    19 publications · 1.7K citations

    18

    h-index

  2. TL
    Taosheng Liu

    Michigan State University, United States

    16 publications · 1.3K citations

    15

    h-index

  3. OL
    Olivier Le Meur

    Université de Rennes, France

    13 publications · 781 citations

    13

    h-index

  4. LI
    Laurent Itti

    University of Southern California, United States

    14 publications · 2K citations

    13

    h-index

  5. MM
    Matthias M. Müller

    Leipzig University, Germany

    14 publications · 1.1K citations

    13

    h-index

1–5 of 8.7K

Venues

  1. 58

    h-index

  2. Journal of Vision

    120 publications · 8.4K citations

    41

    h-index

  3. arXiv (Cornell University)

    Repository · 119 publications · 6.8K citations

    38

    h-index

  4. IEEE Transactions on Image Processing

    54 publications · 6.1K citations

    36

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 49 publications · 5.4K citations

    33

    h-index

1–5