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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
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‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Switching Attention Deficits in Post-Stroke Individuals with Different Aphasia Types

Aphasiology. 2023. Vol. 37. No. 2. P. 260–287.
Kuptsova S., Dragoy O., Ivanova M.

Background: Previous studies have shown that individuals with aphasia have impairments in switching attention compared to healthy controls. However, there is insufficient information about the characteristics of switching attention within one task and whether attention deficits vary depending on aphasia type and lesion location. We aimed to address these knowledge gaps by investigating characteristics of switching attention within one type of task in participants with different types of aphasia and distinct lesion sites.

Method: Forty individuals with post-stroke aphasia (20 with non-fluent aphasia and frontal lobe damage, and 20 with fluent aphasia and temporal lobe damage) and twenty neurologically healthy age-matched individuals performed an attention switching task. They listened to sequences of high-pitched and low-pitched tones that were presented to them one by one, tallied them separately, and, at the end of each sequence, had to say how many high- and low-pitched tones they had heard.

Results: Participants with aphasia performed significantly worse on the task compared to healthy controls, and the performance of two aphasia groups also differed. Specifically, individuals with both aphasia types made more errors than healthy individuals; and the participants with non-fluent aphasia responded more slowly than controls, while reaction times of the participants with fluent aphasia did not differ significantly from those of controls. Also, the two groups of participants with aphasia differed significantly in accuracy, with individuals in the non-fluent group making more errors.

Conclusions: The data demonstrated that people with different types of aphasia have distinct impairments in switching attention. Since cognitive deficits impact language performance, this information is important for differentially addressing their language problems and selecting more specific and optimal rehabilitation programs that target different underlying mechanisms.

Research target: Psychology Philology and Linguistics Clinical Medicine
Language: English
DOI
Text on another site
Keywords: вниманиеattentionафазияпереключение вниманияfluent aphasiaбеглая афазиянебеглая афазияaphasianon-fluent aphasiaswitching attention
Publication based on the results of:
Язык и мозг: предотвращение, диагностика и коррекция речевых расстройств (2020)
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