Researchers Present the Rating of Ideal Life Partner Traits

An international research team surveyed over 10,000 respondents across 43 countries to examine how closely the ideal image of a romantic partner aligns with the actual partners people choose, and how this alignment shapes their romantic satisfaction. Based on the survey, the researchers compiled two ratings—qualities of an ideal life partner and the most valued traits in actual partners. The results have been published in the Journal of Personality and Social Psychology.
For many years, researchers have believed that satisfaction in a romantic relationship depends on how well one’s partner fits the ideal image of them, including factors such as intelligence, sense of humor, and appearance. This idea is supported by the ‘matching hypothesis.’ Scientists have repeatedly tested this theory, but results proved to be contradictory. Perhaps this is due to differences in the participants’ marital status. As a rule, the hypothesis was confirmed in studies with people in long-term relationships, but failed in experiments with participants who have not yet found a partner.
An international team of scientists from more than 40 countries, including researchers from HSE University, conducted the largest-scale verification of the matching hypothesis. The global survey involved 10,358 respondents from 43 countries, including Russia.
The researchers asked the participants to rate those traits they considered most desirable in an ideal partner and then to apply these criteria to real people they knew personally. People in a relationship described their current partner, while singles described a person with whom they would like to be in a romantic relationship.
Based on the results, the authors compiled a rating of ideal partner traits (stated preferences) and a rating of traits that influence the evaluation of a real romantic partner (revealed preferences).
It turned out that the stated and revealed preferences mostly coincided, albeit with some interesting discrepancies. For instance, such qualities as ‘confident,’ ‘a good listener,’ ‘patient,’ and ‘calm’ showed a significantly higher rating in the list of stated preferences vs revealed ones. On the other hand, such attributes as ‘attractive,’ ‘a good lover,’ ‘beautiful body,’ ‘sexy,’ and ‘smells good’ have a much higher rating among the revealed preferences. Moreover, the ‘good lover’ attribute was rated highest in terms of revealed preferences, while holding the 12th position out of 35 in terms of ideal preferences.
The researchers also looked into the differences between men and women in categories most important to people: attractiveness (the average of ‘attractive,’ ‘beautiful body,’ and ‘sexy’) and earning potential (the average of ‘ambitious,’ ‘financially secure,’ and ‘good job’). As a rule, men underestimated the importance they attached to concepts such as ‘attractiveness,’ ‘beautiful body,’ and ‘sexuality’ by about six ranks, while women underestimated these three traits by 13 ranks. As for ‘ambition,’ ‘financial security,’ and ‘good job,’ men undervalued them by an average of four ranks in their rating of ideal traits, while women, on the contrary, overvalued these traits to the same degree.

‘It turns out that both sexes underestimate the importance of attractiveness, but women much more so than men: the features they do not consider important turn out to be among the highest priorities in real life. At the same time, men underestimate—while women, on the contrary, overestimate—the importance of such qualities as ambition, financial security, and having a good job. As a result, despite the differences in the stated attitudes, in real life, men’s and women’s preferences are largely the same,’ explains Albina Gallyamova, a junior research fellow at the HSE Centre for Sociocultural Research. ‘However, the question remains: are our real preferences being adjusted due to the changing social structure, or are we actually much less different from each other in terms of basic attitudes than we think?’
The data obtained will help to better understand how people establish and maintain relationships. Therefore, the impact of perfect matching is slightly lower for long-term partners than for those seeking a relationship. ‘Our research shows that while matching one’s ideal does play a role, it should not be overestimated. People can form successful relationships with partners who do not fully meet their ideal criteria,’ Albina Gallyamova explains.
See also:
How to Assess Students’ Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.
Physicists Discover What Happens Inside a Stable Vortex
Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.


