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News
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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Brain routes for reading in adults with and without autism: EMEG evidence

Journal of Autism & Developmental Disorders. 2014. Vol. 44. No. 1. P. 137–153.
Moseley R., Pulvermuller F., Mohr B., Lombardo M., Baron-Cohen S., Shtyrov Y.

Reading utilises at least two neural pathways. The temporal lexical route visually maps whole words to their lexical entries, whilst the nonlexical route decodes words phonologically via parietal cortex. Readers typically employ the lexical route for familiar words, but poor comprehension plus precocity at mechanically 'sounding out' words suggests that differences might exist in autism. Combined MEG/EEG recordings of adults with autistic spectrum conditions (ASC) and controls while reading revealed preferential recruitment of temporal areas in controls and additional parietal recruitment in ASC. Furthermore, a lack of differences between semantic word categories was consistent with previous suggestion that people with ASC may lack a 'default' lexical-semantic processing mode. These results are discussed with reference to dual-route models of reading.

Research target: Philology and Linguistics Psychology Basic Medicine Health Studies Earth Sciences Biology
Priority areas: law
Language: English
Full text
Text on another site
Keywords: lexical semanticsMEGautism
Publication based on the results of:
Neurocognitive mechanisms of decision making and social influence (2014)
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