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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="review-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">I.P. Pavlov Journal of Higher Nervous Activity</journal-id><journal-title-group><journal-title xml:lang="en">I.P. Pavlov Journal of Higher Nervous Activity</journal-title><trans-title-group xml:lang="ru"><trans-title>Журнал высшей нервной деятельности им. И.П. Павлова</trans-title></trans-title-group></journal-title-group><issn publication-format="print">0044-4677</issn><issn publication-format="electronic">3034-5316</issn><publisher><publisher-name xml:lang="en">The Russian Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">652096</article-id><article-id pub-id-type="doi">10.31857/S0044467724020028</article-id><article-categories><subj-group subj-group-type="toc-heading"><subject>ОБЗОРЫ И ТЕОРЕТИЧЕСКИЕ СТАТЬИ</subject></subj-group><subj-group subj-group-type="article-type"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Search for neurophysiological mechanisms of configurational learning</article-title><trans-title-group xml:lang="ru"><trans-title>Поиск нейрофизиологических механизмов конфигурационного обучения</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Chernyshev</surname><given-names>B. V.</given-names></name><name xml:lang="ru"><surname>Чернышев</surname><given-names>Б. В.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Department of Higher Nervous Activity</p></bio><bio xml:lang="ru"><p>кафедра высшей нервной деятельности</p></bio><email>b_chernysh@mail.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Ushakov</surname><given-names>V. L.</given-names></name><name xml:lang="ru"><surname>Ушаков</surname><given-names>В. Л.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>b_chernysh@mail.ru</email><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff4"/><xref ref-type="aff" rid="aff5"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Poznyak</surname><given-names>L. A.</given-names></name><name xml:lang="ru"><surname>Позняк</surname><given-names>Л. А.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>b_chernysh@mail.ru</email><xref ref-type="aff" rid="aff3"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Lomonosov Moscow State University</institution></aff><aff><institution xml:lang="ru">Московский государственный университет им. М.В. Ломоносова</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Institute for Advanced Brain Studies, Lomonosov Moscow State University</institution></aff><aff><institution xml:lang="ru">Институт перспективных исследований мозга, Московский государственный университет им. М.В. Ломоносова</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education</institution></aff><aff><institution xml:lang="ru">Центр нейрокогнитивных исследований (МЭГ-центр), Московский государственный психолого-педагогический университет</institution></aff></aff-alternatives><aff-alternatives id="aff4"><aff><institution xml:lang="en">National Research Nuclear University MEPhI</institution></aff><aff><institution xml:lang="ru">НИЯУ МИФИ</institution></aff></aff-alternatives><aff-alternatives id="aff5"><aff><institution xml:lang="en">N.A. Alekseev Psychiatric Clinical Hospital No. 1 of the Moscow City Health Department</institution></aff><aff><institution xml:lang="ru">ГБУЗ “Психиатрическая клиническая больница № 1 им. Н.А. Алексеева Департамента здравоохранения города Москвы”</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-08-21" publication-format="electronic"><day>21</day><month>08</month><year>2024</year></pub-date><volume>74</volume><issue>2</issue><fpage>150</fpage><lpage>166</lpage><history><date date-type="received" iso-8601-date="2025-02-02"><day>02</day><month>02</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Russian Academy of Sciences</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Российская академия наук</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Russian Academy of Sciences</copyright-holder><copyright-holder xml:lang="ru">Российская академия наук</copyright-holder></permissions><self-uri xlink:href="https://innoscience.ru/0044-4677/article/view/652096">https://innoscience.ru/0044-4677/article/view/652096</self-uri><abstract xml:lang="en"><p>Configural learning is a form of associative learning in which the conditioned stimulus is a holistic set of stimulus elements rather than individual stimuli or their isolated properties. Successfully solving the task of such associative learning requires a holistic analysis of the entire configuration as a whole. The ability to analyze not only individual physical aspects of a stimulus or single objects in a visual scene, but also their holistic combinations, offers significant evolutionary advantages, as configurations often have substantially greater predictive power compared to individual stimulus elements or features. Moreover, the ability to holistically analyze combinations of stimulus field elements or features can be considered an initial, primitive manifestation of consciousness. In the present review, we consider the history of the development of the concept of configural learning, the main methodological avenues of investigation, and currently available neurophysiological data on the putative neural basis of this phenomenon. We find it most interesting to study the processes of configural learning in humans using modern neuroimaging methods, as they provide a glimpse into the holistic brain functioning. Finally, we consider the future tasks aimed to provide a more complete understanding of the neurophysiology of the configural learning phenomenon.</p></abstract><trans-abstract xml:lang="ru"><p>Конфигурационным обучением называют такую форму ассоциативного обучения, при которой условным стимулом выступает целостный комплекс стимульных элементов, а не отдельные стимулы или их изолированные свойства. Для успешного решения задачи такого ассоциативного обучения требуется холистический анализ всей конфигурации в целом. Возможность анализировать не только отдельные физические аспекты стимула или отдельные объекты зрительной сцены, но и их целостные комбинации дает существенные эволюционные преимущества, поскольку часто конфигурации обладают существенно большей предсказательной силой в сравнении с отдельными элементами или признаками стимула. Более того, возможность холистического анализа комбинаций элементов или признаков стимульного поля может считаться начальным, примитивным проявлением сознания. В настоящем обзоре мы рассмотрим историю разработки концепции конфигурационного обучения, основные методические пути исследования и имеющиеся на настоящий момент нейрофизиологические данные о предполагаемых нейрональных основах этого феномена. Наиболее интересными нам представляются исследования процессов конфигурационного обучения у человека с помощью современных методов нейровизуализации, поскольку они дают возможность заглянуть в работу целостного мозга. В заключение мы рассмотрим, какие проблемы в имеющихся исследованиях должны быть преодолены в будущем, чтобы обеспечить более полное понимание нейрофизиологии феномена конфигурационного обучения.</p></trans-abstract><kwd-group xml:lang="en"><kwd>associative learning</kwd><kwd>configural learning</kwd><kwd>gestalt</kwd><kwd>functional magnetic resonance imaging</kwd><kwd>electrophysiology</kwd><kwd>electroencephalography</kwd><kwd>magnetoencephalography</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>ассоциативное обучение</kwd><kwd>конфигурационное обучение</kwd><kwd>гештальт</kwd><kwd>функциональная магнитно-резонансная томография</kwd><kwd>электрофизиология</kwd><kwd>электроэнцефалография</kwd><kwd>магнитоэнцефалография</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution xml:lang="ru">Российский Научный Фонд</institution></institution-wrap><institution-wrap><institution xml:lang="en">Russian Science Foundation</institution></institution-wrap></funding-source><award-id>23-78-00010</award-id></award-group></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Ивашкина О.И., Торопова К.А., Рощина М.А., Анохин К.В. 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