Scientists Find Out Why Aphasia Patients Lose the Ability to Talk about the Past and Future
An international team of researchers, including scientists from the HSE Centre for Language and Brain, has identified the causes of impairments in expressing grammatical tense in people with aphasia. They discovered that individuals with speech disorders struggle with both forming the concept of time and selecting the correct verb tense. However, which of these processes proves more challenging depends on the speaker's language. The findings have been published in the journal Aphasiology.
Aphasia is a severe speech disorder, often resulting from a stroke, in which individuals lose the ability to speak coherently. In particular, this can manifest as incorrect use of verb tenses, making it difficult for patients to talk about past or future events, significantly complicating everyday communication.
To investigate the origins of these difficulties, researchers from universities in Russia, Greece, Italy, the US, and Norway conducted an experiment. They hypothesised that tense expression impairments could stem from two distinct processes: encoding and retrieval. During encoding, a speaker forms the concept of time (for example, whether an action occurred in the past, present, or future). During retrieval, they select the correct verb form to express that concept. To understand the impact of each process, the scientists carried out experiments with aphasia patients speaking four different languages: Greek, Russian, Italian, and English. These languages were chosen because they structure verb tenses differently, allowing the researchers to examine how language-specific features influence encoding and retrieval of tense in aphasia patients.
To aid in diagnosis, the researchers designed two sentence-completion tasks. The first task asked participants to fill in blanks in sentences, requiring both processes—encoding and retrieval. They had to complete the sentence according to the model, considering the change in the tense form of the verb. For example: ‘Yesterday, the gardener watered the flowers. Tomorrow, the gardener will... the flowers.’ The second tasks expected participants to complete sentences without changing the verb tense. They were given the phrase ‘to water the plants’ and heard the example sentence ‘The gardener is currently collecting mushrooms.’ Then they were then prompted to begin a sentence with ‘The gardener is currently...’ and complete it with the phrase ‘watering the plants’ in the correct form, resulting in ‘is watering the plants.’
By comparing the results from these tasks, the researchers could determine whether the primary difficulties arose during encoding or retrieval.
The study revealed that most participants experienced impairments in both encoding and retrieval, but the severity of these issues varied depending on the language and the individual. For instance, Russian- and English-speaking participants struggled more with the retrieval task, while Greek- and Italian-speaking participants faced challenges primarily during encoding. Interestingly, difficulties in expressing time were selective. Some patients had trouble referencing the past, while others struggled with the future.
‘These findings are crucial for understanding how aphasia patients lose the ability to express time differently, depending on the characteristics of their language,’ explained Olga Buivolova, Research Fellow at the HSE Centre for Language and Brain and one of the study’s authors. ‘We can now better evaluate which aspects of time pose the greatest challenges for patients and begin developing more tailored therapeutic approaches.’
As researchers note, the main conclusions of the study may also have practical implications for neurorehabilitation. Firstly, this experimental method can help identify the underlying causes of difficulties with using verb tenses. This means that speech therapists and neuropsychologists will be able to work more thoroughly and effectively with patients on speech recovery.
Secondly, the study helps to understand how differences between languages can affect the symptoms of aphasia. This is important for developing standardised tests and methods that consider the specifics of a speaker's native language, ultimately leading to more accurate and comprehensive diagnosis of patients with aphasia.
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.