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July 6, 2026
Ancient Craniiform Brachiopod: A Newly Discovered Species with a Unique Shell Shape and Lifestyle
Scientists from HSE University, MSU, and Tallinn University of Technology have studied a fossil species of ancient brachiopods that lived in a warm sea in what is now northern Estonia more than 445 million years ago. These ancient brachiopods developed a cup-shaped shell with a protective 'cap' that shielded them from overgrowth by other marine organisms. The study has been published in Palaeogeography, Palaeoclimatology, Palaeoecology.
July 2, 2026
Researchers Discover How Spelling Errors Slow Down Reading in Russian
Psycholinguists from the Centre for Language and Brain at HSE University–St Petersburg have shown that words that are frequently misspelled are processed more slowly by readers, even when presented with the correct spelling. The researchers confirmed this effect for the first time using Russian-language materials and found that response speed is most strongly linked to how confidently individuals can distinguish the correct spelling of a word from an incorrect one. The study has been published in The Mental Lexicon.
July 2, 2026
HSE Develops App for Assessing Phonological Processing in Children
Researchers at the HSE Centre for Language and Brain have developed a new digital tool for assessing children's phonological processing skills—the ZARYA (Sound Analysis of the Russian Language) test battery. It is the first standardised application in Russia designed to provide a fast and reliable assessment of children's ability to distinguish speech sounds, retain them in working memory, and perform phonemic analysis. The app runs on Android tablets and smartphones and is available for download from RuStore. Details of the test validation have been published in the Journal of Speech, Language, and Hearing Research.

 

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Automated Analysis of Discourse Coherence in Schizophrenia: Approximation of Manual Measures

P. 98–107.
Ryazanskaya G., Khudyakova M.

Disorganized, or incoherent, speech is one of the important criteria for diagnosing schizophrenia. However, there is still a lack of a rather quick objective method of measuring speech coherence. Automated discourse analysis is a possible solution to this problem. We analyzed discourse coherence in a set of spoken narratives by people with schizophrenia and neurotypical speakers of Russian. All narratives were manually rated for violations of completeness, local, global and dimensional coherence. A number of automated vector semantics methods were used for approximation of the manual rating scores. The metrics used proved to be a good approximation for manual scoring, and a combination of them was efficient for classification narratives in schizophrenia and neurotypical groups.

Language: English
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Keywords: schizophreniaword2vecdiscourse coherenceAutomated Discourse Analysis

In book

Proceedings of the LREC 2020 Workshop on: Resources and Processing of Linguistic, Para-linguistic and Extra-linguistic Data from People with Various Forms of Cognitive/Psychiatric/Developmental Impairments (RaPID-3)
European Language Resources Association (ELRA), 2020.
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