Perceptual Encoding

Perceptual Encoding is a method in statistical graphics that belongs to the class of visual representation techniques, distinguished by its systematic translation of data attributes into visual variables—such as position, length, color, or area—according to principles of human perception to optimize accuracy and efficiency in data interpretation.

500

Publications

34.4K

Citations

1.2K

Authors

517

Institutions

Publications per year

2017–2026

97

Authors

1.2K

  1. DL
    Daniel L. Schacter

    Harvard University, United States

    6 publications · 920 citations

    6

    h-index

  2. JD
    John D. E. Gabrieli

    Massachusetts Institute of Technology, United States

    4 publications · 461 citations

    4

    h-index

  3. NW
    Neil W. Mulligan

    University of North Carolina at Chapel Hill, United States

    3 publications · 145 citations

    3

    h-index

  4. AK
    Anil K. Seth

    University of Sussex, United Kingdom

    3 publications · 196 citations

    3

    h-index

  5. DL
    Douglas L. Nelson

    University of South Florida, United States

    3 publications · 322 citations

    3

    h-index

1–5 of 1.2K

Venues

  1. 16

    h-index

  2. Psychological Science

    14 publications · 3.2K citations

    13

    h-index

  3. 13

    h-index

  4. Journal of Vision

    14 publications · 440 citations

    12

    h-index

  5. Journal of Neuroscience

    12 publications · 767 citations

    11

    h-index

1–5