Probabilistic Sequence Learning

Probabilistic Sequence Learning is a form of sequence learning in which the acquisition of sequential patterns occurs under conditions of uncertainty, distinguished by the learner’s implicit inference of statistical regularities and transitional probabilities among elements in a sequence.

98

Publications

7.5K

Citations

318

Authors

157

Institutions

Publications per year

2017–2026

25

Authors

318

  1. DN
    Dezső Németh

    Eötvös Loránd University, Hungary

    17 publications · 1.3K citations

    16

    h-index

  2. KJ
    Karolina Janacsek

    Eötvös Loránd University, Hungary

    17 publications · 1.3K citations

    16

    h-index

  3. JH
    James H. Howard

    Catholic University of America, United States

    7 publications · 571 citations

    7

    h-index

  4. DV
    Darlene V. Howard

    Georgetown University, United States

    7 publications · 571 citations

    7

    h-index

  5. GR
    Gilbert Remillard

    Morehead State University, United States

    5 publications · 309 citations

    5

    h-index

1–5 of 318

Venues

  1. PLoS ONE

    8 publications · 327 citations

    8

    h-index

  2. 4

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 1.3K citations

    4

    h-index

  4. Journal of Experimental Psychology General

    3 publications · 346 citations

    3

    h-index

  5. The Journals of Gerontology Series B

    3 publications · 169 citations

    3

    h-index

1–5