RandseqR: An R Package for Describing Performance on the Random Number Generation Task

Wouter Oomens; Joseph H R Maes; Fred Hasselman; Jos I M Egger

doi:10.3389/fpsyg.2021.629012

RandseqR: An R Package for Describing Performance on the Random Number Generation Task

Front Psychol. 2021 May 4:12:629012. doi: 10.3389/fpsyg.2021.629012. eCollection 2021.

Authors

Wouter Oomens^{1

2}, Joseph H R Maes², Fred Hasselman^{3

4}, Jos I M Egger^{1

2}

Affiliations

¹ Center of Excellence for Neuropsychiatry, Vincent van Gogh Institute for Psychiatry, Venray, Netherlands.
² Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands.
³ School for Pedagogical and Educational Science, Radboud University, Nijmegen, Netherlands.
⁴ Stevig, Specialized and Forensic Care for People With Intellectual Disabilities, Dichterbij, Oostrum, Netherlands.

Abstract

The Random Number Generation (RNG) task has a long history in neuropsychology as an assessment procedure for executive functioning. In recent years, understanding of human (executive) behavior has gradually changed from reflecting a static to a dynamic process and this shift in thinking about behavior gives a new angle to interpret test results. However, this shift also asks for different methods to process random number sequences. The RNG task is suited for applying non-linear methods needed to uncover the underlying dynamics of random number generation. In the current article we present RandseqR: an R-package that combines the calculation of classic randomization measures and Recurrence Quantification Analysis. RandseqR is an easy to use, flexible and fast way to process random number sequences and readies the RNG task for current scientific and clinical use.

Keywords: R-package; assessment; contextual neuropsychology; executive function; random number generation; recurrence qualification analysis.