No abstract

In 2020, the 32nd edition of BNAIC-the annual Benelux Conference on Artificial Intelligence-is organized together with the 29th edition of BeneLearn-the annual Belgian-Dutch Conference on Machine Learning-by the Leiden Institute of Advanced Computer Science (LIACS) of Leiden University, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI).The conference was scheduled to take place in Corpus, Leiden, but due to the corona virus pandemic and limitations on the organization of events, the conference was organized fully online, for the first time in its history. It took place on Thursday, November 19 and Friday, November 20, 2020. The conference included keynotes by invited speakers, so-called FACt talks, research presentations, a social programme, and a "society and business" afternoon.

To investigate whether a variable tends to be larger in one population than in another, the t test is the standard procedure. In some situations, the parametric t test is inappropriate, and a nonparametric procedure should be used instead. The default nonparametric procedure is Mann-Whitney’s U test. Despite being a nonparametric test, Mann-Whitney’s test is associated with a strong assumption, known as exchangeability. I demonstrate that if exchangeability is violated, Mann-Whitney’s test can lead to wrong statistical inferences even for large samples. In addition, I argue that in psychology, exchangeability is typically not met. As a remedy, I introduce Brunner-Munzel’s test and demonstrate that it provides good Type I error rate control even if exchangeability is not met and that it has similar power as Mann-Whitney’s test. Consequently, I recommend using Brunner-Munzel’s test by default. To facilitate this, I provide advice on how to perform and report on Brunner-Munzel’s test.

In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal data is observed. This casts doubt on the validity of group-statistics-based approaches to understand age-related changes on cognitive and neural levels. Recent progress in brain-computer interface research demonstrates the potential of machine learning techniques to derive reliable person-specific models, representing brain behavior mappings. The present study now proposes a supervised learning approach to derive person-specific models for the identification and quantification of interindividual differences in oscillatory EEG responses related to working memory selection and maintenance mechanisms in a heterogeneous lifespan sample. EEG data were used to discriminate different levels of working memory load and the focus of visual attention. We demonstrate that our approach leads to person-specific models with better discrimination performance compared to classical person-nonspecific models. We show how these models can be interpreted both on an individual as well as on a group level. One of the key findings is that, with regard to the time dimension, the between-person variance of the obtained person-specific models is smaller in older than in younger adults. This is contrary to what we expected because of increased behavioral and neuronal heterogeneity in older adults.