Language competition with bilinguals in social networks

Several models have been proposed to study the dynamics of competition between languages. Among them, and starting from the dynamics of endangered languages, recent approaches have addressed the issue of bilingualism. Along these lines we consider the dynamics of language use, allowing for bilingualism, within a social network in the case where the two languages are equivalent. Understanding this case is a first step to describe the case of an endangered language competing against a language with higher status. Local effects are analyzed, studying interface dynamics and growth laws of the system. Power laws for the decay of interface density and bilingual population density are obtained. A final state is reached, where one of the languages disappears. We also study the stability of bilingual communities, which suggests possible explanations for the difficulty of coexistence of languages in the long term.Complex systems, Language competition, social networks

Brain enhancement through cognitive training: A new insight from brain connectome

Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function