Cognitive difficulties are argued to be common in patients with Noonan syndrome spectrum disorders (NSSDs), but findings are based on studies in which patients with variants in PTPN11 (prevalence ~50%) were overrepresented. The current study, using a structured clinical approach, describes the cognitive phenotype and psychopathology of 100 patients (aged 6 to 61 years) with nine different gene variants in the Ras/MAPK pathway underlying NSSDs (PTPN11n = 61, PTPN11 Noonan syndrome with multiple lentigines n = 3, SOS1n = 14, KRASn = 7, LZTR1n = 5, RAF1n = 4, SHOC2n = 2, CBLn = 2, SOS2n = 2). After weighted assessment and bootstrapping of the results of individual neuropsychological assessments and measures of psychopathology, cognitive performances in most variant groups were within the ranges of expectation. IQs were significantly lower in patients with variants in PTPN11, KRAS, RAF1, and SHOC2, but no specific cognitive impairments were found. The performances of younger participants (<16 years of age) did not differ from those of adults. Alexithymia and internalizing problems were more frequent in patients with variants in PTPN11 and SOS1, while PTPN11 patients also showed higher levels of externalizing problems. These results stress the need to take intelligence into account when interpreting lower cognitive performances in individual neuropsychological assessments, which is crucial for an adequate understanding and guidance of patients with NSSDs.

The concept of executive functions plays a prominent role in contemporary experimental and clinical studies on cognition. One paradigm used in this framework is the random number generation (RNG) task, the execution of which demands aspects of executive functioning, specifically inhibition and working memory. Data from the RNG task are best seen as a series of successive events. However, traditional RNG measures that are used to quantify executive functioning are mostly summary statistics referring to deviations from mathematical randomness. In the current study, we explore the utility of recurrence quantification analysis (RQA), a nonlinear method that keeps the entire sequence intact, as a better way to describe executive functioning compared to traditional measures. To this aim, 242 first- and second-year students completed a non-paced RNG task. Principal component analysis of their data showed that traditional and RQA measures convey more or less the same information. However, RQA measures do so more parsimoniously and have a better interpretation.

Objectives: 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.Methods: 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.Results: The traditional measures yield inconsistent results with increasing sequences length, both for computer‐generated and human‐generated sequences, whereas the RQA measures do not.Conclusion: 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.