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October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Черты личности, измеренные эмодзи-лицами, и их связь с Большой Пятеркой и жизненными событиями

Новые психологические исследования. 2025. № 4. С. 80–102.
Ilichev N. R., Shchebetenko S.

Modern researchers are increasingly exploring alternative methods for describing personality differences, as traditional language-based self-report measures suffer from numerous limitations, including cultural bias, social desirability effects, measurement inaccuracies, and other issues. Recent international studies suggest that emojis hold significant potential as a tool for psychometric personality assessment and could serve as a valid alternative to natural language-based questionnaires.

The aim of our study was to explore the potential of emojis as an alternative to conventional text-based questionnaires for measuring personality traits.

Initially, we extracted a set of 128 facial expression Unicode 14.0 emojis, presumably conveying personality traits. We developed instructions to assess personality through emojis, based on instructions of existing textual personality inventories. A sample of 595 volunteer respondents from Russia completed an online survey which contained a set of the face emojis, the Big Five Inventory-2 (BFI-2), and a series of items questioning respondents about various life events.

Upon redundancy check among emojis via unique variable analysis, an exploratory graph analysis was administered, which revealed a five-dimensional personality model based on 68 emojis. The resulting inventory was named the Emoji Personality List (EPL). The EPL model included the following factors: (1) Positive Expressiveness, (2) Negative Emotionality, (3) Unconventionality, (4) Detachment, and (5) Sickness. The model's internal structure was confirmed via confirmatory factor analysis, which indicated good fit between the final structure and the collected data. The internal consistency of the obtained emoji-scales ranged from α = 0.70 to α = 0.80. Subsequently, we examined relations between the EPL dimensions and the Big Five domains. In addition, we investigated concurrent contribution of the emoji dimensions in predicting various life outcomes using logistic and multiple regression models.

The results of the study demonstrate the potential of emoji-based tools in measuring and conceptualizing personality traits.

Research target: Psychology
Language: Russian
Full text
DOI
Keywords: факторный анализfactor analysispersonality traitsчерты личностибольшая пятеркатаксономиипсихометрический анализtaxonomyэксплораторный анализ графовExploratory graph analysis psychometric propertiesЭмодзиemojitrait theorybig fiveemoji-factorsэмодзи-факторы
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