• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Neuroscientists from HSE University Learn to Predict Human Behaviour by Their Facial Expressions

Neuroscientists from HSE University Learn to Predict Human Behaviour by Their Facial Expressions

© iStock

Willingness to donate to charity is written all over the face.

Researchers at the Institute for Cognitive Neuroscience at HSE University are using automatic emotion recognition technologies to study charitable behaviour. In an experiment, scientists presented 45 participants with photographs of dogs in need and invited them to make donations to support these animals. Emotional reactions to the images were determined through facial activity using the FaceReader program. It turned out that the stronger the participants felt sadness and anger, the more money they were willing to donate to charity funds, regardless of their personal financial well-being. The study was published in the journal Heliyon.

The research was supported by a mega-grant from the Russian government as part of the national project ‘Science and Universities.’

Charitable behaviour is a complex psychological process with motives that are not fully understood. Emotional state is one of the key factors influencing the willingness to give one's resources to another. However, the question of how specific emotions are related to donations remains a subject of debate, as previous studies on this topic have yielded conflicting results.

One of the main reasons is methodological limitations in data collection. Most studies on charitable behaviour use subjective methods: volunteers themselves report their emotions and how they affect donations. However, participants do not always understand their feelings or are not willing to speak about them openly. Moreover, depending on the direction of charitable activity (helping sick people, helping animals, supporting environmental initiatives, and others), the same emotions can differently influence the willingness to donate. Therefore, to develop effective charity advertising, it is important to have objective data on the influence of emotions on the willingness to donate, taking into account the goals of fundraising.

Researchers Anna Shepelenko, Vladimir Kosonogov, and Anna Shestakova from the HSE Institute for Cognitive Neuroscience were the first in the world to use an automatic emotion recognition program to evaluate the effectiveness of charity appeals. In the experiment, scientists used FaceReader to record the facial expressions of volunteers while viewing photos of dogs in various conditions, including homeless, domestic, sick, healthy, adult, and puppies. Participants were given 320 rubles, which they could fully or partially donate to the animal aid fund during the experiment. The remaining amount could be kept for themselves.

Dogs were specifically chosen for the study because society's attitude towards them is ambiguous: they can be perceived as both victims and threats. In these conditions, charity appeals can evoke contradictory or hidden emotions that are difficult to detect through surveys or focus groups but can be recognised by facial expressions.

Anna Shepelenko

Anna Shepelenko

Co-author of the study, Junior Research Fellow at the Centre for Cognition & Decision Making at the Institute for Cognitive Neuroscience, HSE University

Currently, affective computing is still in the early stages of its development and is not widely used in the industry. However, in the coming years, its widespread implementation in various business and scientific fields can be expected. Technologies like FaceReader are already being used for diagnosing mental disorders, improving the quality of online education, assessing customer satisfaction, and in some other areas. Our study clearly shows the prospects of using affective computing to predict the effectiveness of social advertising, creating a basis for further research and practical application.

The main results of the study showed that people are inclined to donate more when charity appeals provoke unpleasant emotions in them. In particular, participants gave larger amounts to animals whose images evoked sadness and anger. These results are confirmed by both self-reports of emotions and affective computing data.

At the same time, for some other emotions, researchers found differences between survey data and FaceReader data. According to self-reports, surprise, disgust, and fear also contributed to increased donations, but FaceReader data did not confirm this. According to the authors, these differences may have arisen due to the timing of emotion recording: the program analysed reactions during the first presentation of pictures and immediately before the donation decision, whereas self-reports were made after a second viewing of the images. This time gap could have changed participants' perception of their emotions.

Additionally, the formulation of questions about specific emotions could have caused cognitive biases and influenced respondents' answers so that they indicated not only the emotions they experienced but also those they considered appropriate depending on the context of the image. Based on this, researchers conclude that affective computing complements self-report data, allowing for a more accurate identification of emotions at the moment of decision-making and reducing the influence of experimental procedures or cognitive biases on research results.

It is noteworthy that donations were associated with the individual perception of the value of money: the higher the subjective significance of the reward for participating in the experiment (320 rubles), the less willing participants were to donate to charity, while their personal level of financial well-being did not affect the amount of donations. This data clarifies the results of earlier publications, which showed contradictions regarding the relationship between financial well-being and the willingness to make donations.

Anna Shepelenko

Co-author of the study, Junior Research Fellow at the Centre for Cognition & Decision Making at the Institute for Cognitive Neuroscience, HSE University

This study contributes to the understanding of charitable behaviour, and we hope that the obtained results will be useful for non-profit organisations. At the same time, it is important to note some limitations: our results do not take into account the long-term impact of charity advertising on the audience.

Indeed, unpleasant emotions can increase the amount of donations during initial contact, but regularly repeated negative charity appeals can provoke empathy fatigue or avoidance—people may simply exclude such messages from their information field. Thus, to further study the impact of emotions on charity, longitudinal studies should be conducted, in which changes in donors' behaviour can be registered over several months or even years.

See also:

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.

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.

Trend-Watching: Radical Innovations in Creative Industries and Artistic Practices

The rapid development of technology, the adaptation of business processes to new economic realities, and changing audience demands require professionals in the creative industries to keep up with current trends and be flexible in their approach to projects. Between April and May 2025, the Institute for Creative Industries Development (ICID) at the HSE Faculty of Creative Industries conducted a trend study within the creative sector.

From Neural Networks to Stock Markets: Advancing Computer Science Research at HSE University in Nizhny Novgorod

The International Laboratory of Algorithms and Technologies for Network Analysis (LATNA), established in 2011 at HSE University in Nizhny Novgorod, conducts a wide range of fundamental and applied research, including joint projects with large companies: Sberbank, Yandex, and other leaders of the IT industry. The methods developed by the university's researchers not only enrich science, but also make it possible to improve the work of transport companies and conduct medical and genetic research more successfully. HSE News Service discussed work of the laboratory with its head, Professor Valery Kalyagin.

Children with Autism Process Sounds Differently

For the first time, an international team of researchers—including scientists from the HSE Centre for Language and Brain—combined magnetoencephalography and morphometric analysis in a single experiment to study children with Autism Spectrum Disorder (ASD). The study found that children with autism have more difficulty filtering and processing sounds, particularly in the brain region typically responsible for language comprehension. The study has been published in Cerebral Cortex.

HSE Scientists Discover Method to Convert CO₂ into Fuel Without Expensive Reagents

Researchers at HSE MIEM, in collaboration with Chinese scientists, have developed a catalyst that efficiently converts CO₂ into formic acid. Thanks to carbon coating, it remains stable in acidic environments and functions with minimal potassium, contrary to previous beliefs that high concentrations were necessary. This could lower the cost of CO₂ processing and simplify its industrial application—eg in producing fuel for environmentally friendly transportation. The study has been published in Nature Communications. 

HSE Scientists Reveal How Staying at Alma Mater Can Affect Early-Career Researchers

Many early-career scientists continue their academic careers at the same university where they studied, a practice known as academic inbreeding. A researcher at the HSE Institute of Education analysed the impact of academic inbreeding on publication activity in the natural sciences and mathematics. The study found that the impact is ambiguous and depends on various factors, including the university's geographical location, its financial resources, and the state of the regional academic employment market. A paper with the study findings has been published in Research Policy.

Group and Shuffle: Researchers at HSE University and AIRI Accelerate Neural Network Fine-Tuning

Researchers at HSE University and the AIRI Institute have proposed a method for quickly fine-tuning neural networks. Their approach involves processing data in groups and then optimally shuffling these groups to improve their interactions. The method outperforms alternatives in image generation and analysis, as well as in fine-tuning text models, all while requiring less memory and training time. The results have been presented at the NeurIPS 2024 Conference.

When Thoughts Become Movement: How Brain–Computer Interfaces Are Transforming Medicine and Daily Life

At the dawn of the 21st century, humans are increasingly becoming not just observers, but active participants in the technological revolution. Among the breakthroughs with the potential to change the lives of millions, brain–computer interfaces (BCIs)—systems that connect the brain to external devices—hold a special place. These technologies were the focal point of the spring International School ‘A New Generation of Neurointerfaces,’ which took place at HSE University.

New Clustering Method Simplifies Analysis of Large Data Sets

Researchers from HSE University and the Institute of Control Sciences of the Russian Academy of Sciences have proposed a new method of data analysis: tunnel clustering. It allows for the rapid identification of groups of similar objects and requires fewer computational resources than traditional methods. Depending on the data configuration, the algorithm can operate dozens of times faster than its counterparts. Thestudy was published in the journal Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia.