Lutz Jäncke

With the worldwide increase in people speaking more than one language, a better understanding of the behavioural and neural mechanisms governing lexical selection, lexical access in multiple languages and code switching has attracted widespread interest from several disciplines. Previous studies documented higher costs when processing a non‐native (L2) than a native (L1) language or when switching from L2 to L1. However, studies on auditory language reception are still scarce and did not take into account the degree of switching experience. Accordingly, in the present study, we combined behavioural and electrophysiological measurements to assess lexical access in L1 and L2 as well as code switching in professional simultaneous interpreters, trainee interpreters, foreign language teachers and Anglistics students, while the participants performed a bilingual auditory lexical decision task. The purpose of this study was to expand the knowledge on code switching in auditory language processing and examine whether the degree of simultaneous interpretation experience might reduce switching costs. As a main result, we revealed that L2 compared to L1 trials, as well as switch compared to non‐switch trials, generally resulted in lower accuracies, longer reaction times and increased N400 amplitudes in all groups of participants. Otherwise, we did not reveal any influence of switching direction and interpretation expertise on N400 parameters. Taken together, these results suggest that a late age of L2 acquisition leads to switching costs, irrespective of proficiency level. Furthermore, we provided first evidence that simultaneous interpretation training does not diminish switching costs, at least when focusing on lexical access.

We analysed a dataset comprising 118 subjects who were scanned three times (at baseline, 1‐year follow‐up, and 7‐year follow‐up) using structural magnetic resonance imaging (MRI) over the course of 7 years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume [ICV], total cortical thickness [CT], total cortical surface area [CA], cortical grey matter [CoGM], cerebral white matter [CeWM], cerebellar cortex [CBGM], cerebellar white matter [CBWM], subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala and accumbens] and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF) and a newly developed rule‐based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if–then rules and compared to the same set of neuroanatomical features derived from the 7‐year follow‐up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some four‐feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain‐fingerprinting techniques.