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Contemporary psychology and cognitive neuroscience create many opportunities for studying the brain functions, but also generate numerous challenges. To date, scientists face common conceptual problems which are relevant to almost every research study/ case such as: classification of functions, unified methodological approaches, explanation of the psychological phenomenon etc. The Sino-German Workshop which took place in Hamburg in 2013 aimed to address unasked questions and unused answers, attracting scientists from different countries and different fields of psychology, neuroscience, medicine, history, and philosophy. The present discussion on the 9 unasked questions was initiated by Professor Ernst Poeppel and was held on by Russian participants from various academic institutions.

In this paper the authors put polysemy research into the broader context of research into the mental lexicon, cognitive context conceptual priming, and probabilistic conceptual modelling. The article adopts a novel approach to the resolution of polysemy and puts it to an empirical test. The authors argue that priming plays a key role in the activation of an adequate meaning of a polysemous word. Mental structures, represented by a prime lexical unit, contain relevant conceptual information on the target word meaning. The prime triggers a cognitive context that influences the selection process of the target word sense.

This study describes the experimentally developed technique of sentiment analysis in an automatic semantic parser that processes daily news input blocks. The 2022 time period was used to analyze news in Telegram channels published by twelve official news agencies and five influential non-official news sources. In the advanced analytical system that we present, automatic learning is combined with a frame-based approach to identify patterns that influence judgments based on a frame representation of the text. The parser analyzes the syntactic structure of each sentence input, constructs its semantic representation, and associates it with one of 3800 semantic frames extracted from a large text corpus. The research revealed that the relative frequency and combination of frames varied between the two groups of news sources. The results prove that news is conceptualized and represented by various media sources in very different ways with the potential to affect the recipient's beliefs and emotions.

The study presents the experimentally developed technique to determine how and where semantic categories are represented in the human brain. For this purpose, the combination of linguistic annotation of oral texts in Russian with the registration of BOLD signal in functional MRI subjects’ testing was applied. The goal is to correlate groups of words in input texts and patterns of brain activation. The algorithm of the texts’ markup consisted of four sequential procedures: (1) time annotation, (2) lemmatization, (3) vectorization, (4) indexation of words-attributes.Regarding the issue of brain activity allocation, the corresponding activation distributed nature can be noted. It proves our hypothesis that conceptual information during text comprehension is processed in different (however specific) areas of the brain and is not compactly localized.The main result is that the allocated lexical clusters are composed of paradigmatically close words (synonyms, hyponyms) and situationally related words.

The results were obtained in the course of a two-stage study. At the first stage (2018) linguists analyzed the conceptualdomain sociocultural challenges on the basis of purposely elaborated Russian language THREAT-corpus (10.4 m words)and built a frame of the domain. At the second stage (2018-2019) the research was carried out with methods of automatedtopic modeling for two Russian language corpora: THREAT-corpus and alternative corpus collected using WebBootCaTtool in the SketchEngine corpus management system. Methods of topic modeling (PLSA, LDA, BigARTM et al.) allowedeliciting thematic profiles for texts of both corpora. Comparison of two datasets was carried out by applying set theory,graph theory, and probabilistic analysis. Combining topic modeling with linguistic frame analysis resulted in more pre-cise configurations of cognitive models in the conceptual domain sociocultural challenges. Word frequency for lexemesmanifesting sociocultural challenges proved to be an important factor of conceptual structures representation.

Aggression speech patterns (ASP) strongly influence modern culture and ideology, they are regular source domain forconceptual metaphors. The study was based on THREAT-corpus (Russian language, 5 mln words) which was constructedto study ASP and contains fiction, non-fiction, news texts. The aim of the study is to investigate non-metaphoric andmetaphoric ASP in Russian. A semantic parser was designed to automatically process texts and construct conceptualrepresentations: They killed all the enemies (non-metaphoric)/He killed the time (metaphoric) [Ag-CAUSE HARM-Pat]. After extracting conceptual representations the parser evaluates them as aggressive or non-aggressive. An exampleMechanical toys pushed forward the imagination of scientists is evaluated as aggressive. Although this evaluation is false-positive, it reveals the conceptual metaphor where mental causation is described as voluntary action. This set of methodsmakes possible to collect, detect and describe ASP in diverse types of discourse and, consequently, to analyze the cognitivenature of aggression.

Chaining the sequence of events through awareness of their temporal relations is an important aspect of text un-derstanding. As a rule, text provides only partial knowledge of event unfolding however various types of additional sources(documents, personal diaries, etc.) provide an added knowledge to make chronology more precise.The paper argues the novel approach to automated retrieval of information on temporal relations between events marked inthe text. The data retrieved will provide additions to computer ontology which formally represents the actual events chronology.A system of linguistic algorithms for analyzing the contexts with specific verbal (or linguistic situations) inputs is suggestedwithin the present approach. We use syntactic graphs of the sentences and some grammatical characteristics of the wordsproduced by the system for the automatic syntactic analysis of Russian texts.

The article demonstrates the application of the modular competence-based approach for the design of a Master degree programme in translation studies. The case study is based on output materials produced during the lifetime of the Tuning Russia project involving a number of Russian universities, one of which is the Russian State University for the Humanities (RSUH). The module in question -- the LSP (language for special purposes) translation module -- emphasizes interaction between the related disciplines on the basis of the common subject-matter, i.e. the translation of special purpose texts. The modular approach sets out to achieve key competences required for professional qualifications. In addition, the module considers teaching methods, ECTS and assessment tools. Attention is given to the concept of competence-based approach in contemporary education. The authors argue that the competence-based approach introduced in Russia at the national (Ministerial) level in 2016 facilitates Russia's interactive alignment with the main principles of the Bologna Process adopted by the European Higher Education Area.

This is the claim: Human knowledge expresses itself in three different modes: explicit, implicit, and sensory (e.g., visual) knowledge. The reference to knowledge as explicit knowledge only (as it is usually done) neglects the other modes of knowledge. Instead of using the noun “knowledge,” it is suggested that we use “knowing” as a term to stress the active mode in cognition. The three modes of knowing are reflected in different mechanisms of cognitive and neural processing; these can already be observed in classical documents of humankind. This unity of the different modes of knowing can also be extracted from cultural artifacts such as poetry. Although possessing different characteristics, the three modes of knowing are related to each other in a complementary way, thus providing an integrative network for cognition. We suggest that a unifying frame of the modes of knowing lies in aesthetic principles. These different modes of knowing with their unifying frame reflect some general features of cognition. We stress that basic concepts in “artificial or machine intelligence” do not match at all human modes of knowing. [ABSTRACT FROM AUTHOR]