Perceptual Template Learning

Perceptual Template Learning is a theoretical construct within perceptual learning that specifies how sensory inputs are differentially weighted and matched against internal representations to optimize discrimination performance based on experience.

16

Publications

969

Citations

42

Authors

27

Institutions

Publications per year

2017–2026

2

Authors

42

  1. ZL
    Zhong‐Lin Lu

    University of Southern California, United States

    4 publications · 350 citations

    4

    h-index

  2. BA
    Barbara Anne Dosher

    University of California, Irvine, United States

    3 publications · 156 citations

    3

    h-index

  3. DM
    Dennis M. Levi

    University of California, Berkeley, United States

    2 publications · 208 citations

    2

    h-index

  4. SA
    Stanley A. Klein

    University of California, Berkeley, United States

    2 publications · 208 citations

    2

    h-index

  5. YC
    Yamit Cohen

    Hebrew University of Jerusalem, Israel

    1 publications · 17 citations

    1

    h-index

1–5 of 42

Venues

  1. Journal of Vision

    3 publications · 139 citations

    3

    h-index

  2. Cognitive Science

    2 publications · 179 citations

    2

    h-index

  3. Vision Research

    2 publications · 46 citations

    2

    h-index

  4. Journal of Cognitive Neuroscience

    1 publications · 17 citations

    1

    h-index

  5. Nature Neuroscience

    1 publications · 134 citations

    1

    h-index

1–5