A time-series perspective on executive functioning: The benefits of a dynamic approach to random number generation.

Abstract

Executive functioning (EF) is a key topic in neuropsychology. A multitude of underlying processes and constructs have been suggested to explain EF, which are measured by at least as many different neuropsychological tests. However, these tests often refer to summary statistics to quantify the construct under study, failing to capture the dynamic nature of EF. An alternative to these summary statistics is a time-series approach that quantifies all the available temporal information. We used recurrence quantification analysis (RQA) to quantify the characteristics of any temporal pattern in random number generation data and we compared RQA to the traditional and static analysis of random number sequences. The traditional measures yield inconsistent results with increasing sequences length, both for computer-generated and human-generated sequences, whereas the RQA measures do not. The results suggest that a time-series approach does a better job at modelling what is happening on different time-scales, and, therefore, is better at explaining how EF is changing in the course of the random number generation task. We argue that it is likely that these findings also apply to other neuropsychological EF tests, and that a time-series approach is an important addition to the study of EF.