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1. Language of instruction affects language interference in the third language

   https://doi.org/10.1017/S1366728921000043  

   Tomoschuk, Brendan ; Duyck, Wouter ; Hartsuiker, Robert J. ; Ferreira, Victor S. ; Gollan, Tamar H. ( August 2021 , Bilingualism: Language and Cognition)

   Abstract Applied linguistic work claims that multilinguals’ non-native languages interfere with one another based on similarities in cognitive factors like proficiency or age of acquisition. Two experiments explored how trilinguals regulate control of native- and non-native-language words. Experiment 1 tested 46 Dutch–English–French trilinguals in a monitoring task. Participants decided if phonemes were present in the target language name of a picture, phonemes of non-target language translations resulted in longer response times and more false alarms compared to phonemes not present in any translation (Colomé, 2001). The second language (English) interfered more than the first (Dutch) when trilinguals monitored in their third language (French). In Experiment 2, 95 bilinguals learned an artificial language to explore the possibility that the language from which a bilingual learns a third language provides practice managing known-language interference. Language of instruction modulated results, suggesting that learning conditions may reduce interference effects previously attributed to cognitive factors.
2. Language Shift, Language Technology, and Language Revitalization: Challenges and Possibilities for St. Lawrence Island Yupik

   Schwartz, Lane ( December 2019 , Proceedings of the International Conference Language Technologies for All (LT4All): Enabling Linguistic Diversity and Multilingualism Worldwide)

   St. Lawrence Island Yupik is a polysynthetic language indigenous to St. Lawrence Island, Alaska, and the Chukotka Peninsula of Russia. While the vast majority of St. Lawrence Islanders over the age of 40 are fluent L1 Yupik speakers, rapid language shift is underway among younger generations; language shift in Chukotka is even further advanced. This work presents a holistic proposal for language revitalization that takes into account numerous serious challenges, including the remote location of St. Lawrence Island and Chukotka, the high turnover rate among local teachers, socioeconomic challenges, and the lack of existing language learning materials.
3. Language Growth in Young Children with Autism: Interactions Between Language Production and Social Communication

   https://doi.org/10.1007/s10803-020-04576-3  

   Blume, Jessica ; Wittke, Kacie ; Naigles, Letitia ; Mastergeorge, Ann M. ( June 2020 , Journal of Autism and Developmental Disorders)
4. Native language inhibition predicts more successful second language learning: Evidence of two ERP pathways during learning

   https://doi.org/10.1016/j.neuropsychologia.2020.107732  

   Pulido, Manuel F. ( February 2021 , Neuropsychologia)
5. The simulation experiment description markup language (SED-ML): language specification for level 1 version 4

   https://doi.org/10.1515/jib-2021-0021  

   Smith, Lucian P. ; Bergmann, Frank T. ; Garny, Alan ; Helikar, Tomáš ; Karr, Jonathan ; Nickerson, David ; Sauro, Herbert ; Waltemath, Dagmar ; König, Matthias ( June 2021 , Journal of Integrative Bioinformatics)

   Abstract Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/ .