The Effect of Bilingualism on Executive Functioning Found in Young Adults: an eye-tracking study

The effect of bilingualism on executive functioning (EF) has long been a topic of discussion across the psycholinguistic field. It was previously assumed that acquiring two languages simultaneously may have an effect on the child’s cognitive development. This claim was later rejected and opposed by researchers who found that being fluent in two languages provides more cognitive benefits, than being fluent just in one language. Furthermore, neural processing in a bilingual brain influences several cognitive domains that were introduced by Miyake and Friedman’s framework, which explains the high inter-connectivity between specific executive functioning domains, such as inhibiting, monitoring, and updating. Aims and Methods: The current paper focused on establishing whether being bilingual aids executive functioning in a young adult population. Both monolingual (N = 16) and bilingual (N = 14) participants were tested on a number of cognitive tests. An eye-tracker was used to test inhibitory control, using pro- and anti-saccade conditions. Further, a multitasking and visuospatial working memory capacity task were completed using the press-pad. It was hypothesized that bilinguals will make less errors and initiate a faster response in comparison with monolinguals. However, no significant bilingual cognitive advantage was found in the three EFs components. However, bilinguals did initiate a saccade response faster in the inhibitory control task, while maintaining the same level of accuracy as the monolingual group. Future research should focus on improving the current paper design flaws as well as to include questionnaires for SES and IQ

Data_Sheet_1_Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset.PDF

IntroductionBrain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear.MethodsThe current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1,274 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and developed a deep convolutional neural network model.ResultsThe model’s training was done with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction.DiscussionThe architecture and training method of the proposed deep convolutional neural networks (DCNN) improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.</p