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:
'Our Goal Is Not to Determine Which Version Is Correct but to Explore the Variability'
The International Linguistic Convergence Laboratory at the HSE Faculty of Humanities studies the processes of convergence among languages spoken in regions with mixed, multiethnic populations. Research conducted by linguists at HSE University contributes to understanding the history of language development and explores how languages are perceived and used in multilingual environments. George Moroz, head of the laboratory, shares more details in an interview with the HSE News Service.
Slim vs Fat: Overweight Russians Earn Less
Overweight Russians tend to earn significantly less than their slimmer counterparts, with a 10% increase in body mass index (BMI) associated with a 9% decrease in wages. These are the findings made by Anastasiia Deeva, lecturer at the HSE Faculty of Economic Sciences and intern researcher in Laboratory of Economic Research in Public Sector. The article has been published in Voprosy Statistiki.
Scientists Reveal Cognitive Mechanisms Involved in Bipolar Disorder
An international team of researchers including scientists from HSE University has experimentally demonstrated that individuals with bipolar disorder tend to perceive the world as more volatile than it actually is, which often leads them to make irrational decisions. The scientists suggest that their findings could lead to the development of more accurate methods for diagnosing and treating bipolar disorder in the future. The article has been published in Translational Psychiatry.
Scientists Develop AI Tool for Designing Novel Materials
An international team of scientists, including researchers from HSE University, has developed a new generative model called the Wyckoff Transformer (WyFormer) for creating symmetrical crystal structures. The neural network will make it possible to design materials with specified properties for use in semiconductors, solar panels, medical devices, and other high-tech applications. The scientists will present their work at ICML, a leading international conference on machine learning, on July 15 in Vancouver. A preprint of the paper is available on arxiv.org, with the code and data released under an open-source license.
HSE Linguists Study How Bilinguals Use Phrases with Numerals in Russian
Researchers at HSE University analysed over 4,000 examples of Russian spoken by bilinguals for whom Russian is a second language, collected from seven regions of Russia. They found that most non-standard numeral constructions are influenced not only by the speakers’ native languages but also by how frequently these expressions occur in everyday speech. For example, common phrases like 'two hours' or 'five kilometres’ almost always match the standard literary form, while less familiar expressions—especially those involving the numerals two to four or collective forms like dvoe and troe (used for referring to people)—often differ from the norm. The study has been published in Journal of Bilingualism.
Overcoming Baby Duck Syndrome: How Repeated Use Improves Acceptance of Interface Updates
Users often prefer older versions of interfaces due to a cognitive bias known as the baby duck syndrome, where their first experience with an interface becomes the benchmark against which all future updates are judged. However, an experiment conducted by researchers from HSE University produced an encouraging result: simply re-exposing users to the updated interface reduced the bias and improved their overall perception of the new version. The study has been published in Cognitive Processing.
Mathematicians from HSE Campus in Nizhny Novgorod Prove Existence of Robust Chaos in Complex Systems
Researchers from the International Laboratory of Dynamical Systems and Applications at the HSE Campus in Nizhny Novgorod have developed a theory that enables a mathematical proof of robust chaotic dynamics in networks of interacting elements. This research opens up new possibilities for exploring complex dynamical processes in neuroscience, biology, medicine, chemistry, optics, and other fields. The study findings have been accepted for publication in Physical Review Letters, a leading international journal. The findings are available on arXiv.org.
Mathematicians from HSE University–Nizhny Novgorod Solve 57-Year-Old Problem
In 1968, American mathematician Paul Chernoff proposed a theorem that allows for the approximate calculation of operator semigroups, complex but useful mathematical constructions that describe how the states of multiparticle systems change over time. The method is based on a sequence of approximations—steps which make the result increasingly accurate. But until now it was unclear how quickly these steps lead to the result and what exactly influences this speed. This problem has been fully solved for the first time by mathematicians Oleg Galkin and Ivan Remizov from the Nizhny Novgorod campus of HSE University. Their work paves the way for more reliable calculations in various fields of science. The results were published in the Israel Journal of Mathematics (Q1).
Large Language Models No Longer Require Powerful Servers
Scientists from Yandex, HSE University, MIT, KAUST, and ISTA have made a breakthrough in optimising LLMs. Yandex Research, in collaboration with leading science and technology universities, has developed a method for rapidly compressing large language models (LLMs) without compromising quality. Now, a smartphone or laptop is enough to work with LLMs—there's no need for expensive servers or high-powered GPUs.
AI to Enable Accurate Modelling of Data Storage System Performance
Researchers at the HSE Faculty of Computer Science have developed a new approach to modelling data storage systems based on generative machine learning models. This approach makes it possible to accurately predict the key performance characteristics of such systems under various conditions. Results have been published in the IEEE Access journal.