Approximate Number Recognition

Approximate Number Recognition is a computational method in the field of numeral recognition that enables the estimation of numerical quantities without exact symbolic representation, distinguishing itself by relying on analogical or perceptual magnitude processing rather than precise digit classification.

140

Publications

11.9K

Citations

318

Authors

188

Institutions

Publications per year

2017–2026

56

Authors

318

  1. JH
    Justin Halberda

    Johns Hopkins University, United States

    19 publications · 3.7K citations

    19

    h-index

  2. LF
    Lisa Feigenson

    Johns Hopkins University, United States

    15 publications · 3.4K citations

    15

    h-index

  3. ME
    Melissa E. Libertus

    University of Pittsburgh, United States

    14 publications · 1.4K citations

    13

    h-index

  4. DO
    Darko Odic

    University of British Columbia, Canada

    11 publications · 792 citations

    11

    h-index

  5. EM
    Elizabeth M. Brannon

    Duke University, United States

    8 publications · 774 citations

    8

    h-index

1–5 of 318

Venues

  1. Frontiers in Psychology

    17 publications · 593 citations

    15

    h-index

  2. Developmental Science

    14 publications · 1.5K citations

    14

    h-index

  3. PLoS ONE

    10 publications · 1.4K citations

    10

    h-index

  4. Journal of Experimental Child Psychology

    JECP · 8 publications · 498 citations

    8

    h-index

  5. Acta Psychologica

    6 publications · 350 citations

    6

    h-index

1–5