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Regular version of the site

Lectures and Seminars

A neuroscientific perspective on the shades of empathy


Gal Raz

PhD, Tel Aviv University, TAU School of Film and Television


Neuroscientific research in the past two decades has consistently pointed to the involvement of two separate factors in empathy (Shamay-Tsoory, 2011; Zaki & Ochsner, 2012). On the one hand, affect sharing (AS), is a vicarious resonance with others’ somatovisceral affective states. It is prompted by neural systems supporting affect perception, such as the sensory cortices and amygdala; salience detection, which involves anterior portions of the cingulate cortex (ACC) and the insula. On the other hand, theory-of-mind (ToM) denotes the attribution of mental states to others based on cognitive representation. It implicates a network including the medial prefrontal cortex, superior temporal sulcus, temporo-parietal junction, precuneus, posterior cingulate cortex, and the temporal poles. In my talk, I will review the evidence on the dissociation of these networks. I will also present neuroimaging findings about oppositional and complementary relations between them during the processing of naturalistic audiovisual content.

Shamay-Tsoory, S. G. (2011). The neural bases for empathy. Neuroscientist, 17(1), 18–24. https://doi.org/10.1177/1073858410379268

Zaki, J., & Ochsner, K. (2012). The neuroscience of empathy: progress, pitfalls and promise. Nature Neuroscience. https://doi.org/10.1038/nn.3085

Record of the seminar (Access code: %P3f2$uZ)

The "when" and "what" of episodic encoding


Aya Ben-Yaakov

Research Fellow at MRC Cognition and Brain Science Unit, University of Cambridge


In striving for experimental control, studies of human episodic memory have focused mainly on encoding of brief, stationary events. Such events, while providing a high degree of control, bear little resemblance to real-life memory and constrain the questions that can be asked. I will demonstrate how use of naturalistic stimuli enables us to address previously unaskable questions, discussing a set of fMRI studies in which we asked when episodic memories are formed. Using film clips as a proxy for real-life memory, we found that hippocampal activity time-locked to the offset of events, but not their onset or duration, is linked to subsequent memory. In a subsequent study we analysed brain activity of over 200 participants who viewed a naturalistic film and found that the hippocampus responded both reliably and specifically to shifts between scenes. Taken together, these results suggest that during encoding of a continuous experience, event boundaries drive hippocampal processing, potentially reflecting the encoding of bound representations to long-term memory. I will discuss how this surprising finding opened a new avenue of research, asking what is encoded in episodic encoding – is each element encoded independently, or is the entire episode encoded as a cohesive unit?

Record of the seminar (Access code: 8#LhnmE#)

The self- and other-referential processing


Georgiy Knyazev

Doctor of Biological Sciences, Head of the Laboratory of Differential Psychophysiology of the Scientific Research Institute of Neurosciences and Medicine


The nature of self is one of the most controversial questions throughout the history of philosophy and science. Different approaches emphasize different aspects of this construct including emotional, cognitive, and social self. Moreover, some authors deny its reality altogether claiming that the self is just an illusion. From the point of view of social science, the intersubjective or social aspects are the most important aspects of self. One of the most popular approaches to the study of these aspects is contrasting self-referential with other-referential processing in the trait adjective judgment task. In our study, using fMRI functional connectivity data, we aimed to directly compare the involvement of the default mode network (DMN) versus external attention-related task-positive networks (TPN) during self- and other- referential processing for different others varying in the degree of their closeness to the self. We hypothesized that the DMN versus TPN balance should linearly decrease during evaluation of self, close-other, distant-other, and an unpleasant person.

Record of the seminar (Access code: Q7+XkV&7)

Levels and Factors of Trust in Russian Regions: Results of an Online Experiment


Alexey Belyanin

PhD,Senior Research Fellow, International Laboratory for Experimental and Behavioral Economics,National Research University Higher School of Economics



Trust is known as the most important factor in the efficiency of economic and social processes, characterizing the level of development of public institutions (Knack & Keefer, 1997). Traditional methods of measuring trust in survey research (for example, using the World Value Survey methodology) show a rather low level of trust in Russia compared to other countries (Algan & Cahuc, 2013). Unlike survey methods, the trust investment game (Berg e.a., 1995; Johnson and Mislin, 2011) provides an opportunity to measure the level of trust people have in each other under real incentives.

In 2020, we conducted an online trust experiment in 12 Russian cities, representing all federal districts, with the participation of more than 2,000 people. The participants in the experiment - registered users of the Yandex-Toloka crowdsourcing platform - made decisions during the experiment with respect to participants from all cities (double strategic method), which made it possible to collect almost 25 thousand decisions and stimulated expectations regarding the decisions of opponents - the same participants from other cities.

The participants of the experiment – registered users of the Yandex-Toloka crowdsourcing platform-made decisions in the course of the experiment in relation to participants from all cities (double strategic method), which allowed us to collect almost 25 thousand decisions and stimulated expectations about the decisions of opponents – the same participants from other cities.The results showed that motivated measures of trust (trust) and its justifications (trustworthiness) may be more valid from an economic point of view than the survey ones - in particular, unlike the latter, they are correlated with GRP. The levels of motivated trust in Russia (58%) and its justification (44%) were even slightly higher than global trends, and generally meet the expectations of the participants themselves. At the same time, we find negative and positive discrimination between representatives of different regions in relation to each other, clustering of cities by levels of trust, and interregional differences in decision-making mechanisms regarding trust or distrust of counter-partners from other regions.

Record of seminar (Access code: 0?Khz%xx)

Brain activity foreshadows stock price dynamics


Mirre Stallen

PhD, Assistant Professor Institute of Psychology Leiden University, Senior Researcher Amsterdam University of Applied Sciences



Successful investing is challenging, since stock prices are difficult to consistently forecast. Recent neuroimaging evidence suggests, however, that activity in brain regions associated with anticipatory affect may not only predict individual choice, but also forecast aggregate behavior out-of-sample. 

Thus, in two experiments, we specifically tested whether anticipatory affective brain activity in healthy humans could forecast aggregate changes in stock prices. Using Functional Magnetic Resonance Imaging (FMRI), we found in a first experiment (n=34, 6 females; 140 trials per subject) that Nucleus Accumbens (NAcc) activity forecast stock price direction, whereas Anterior Insula (AIns) activity forecast stock price inflections. In a second preregistered replication experiment (n=39, 7 females) that included different subjects and stocks, AIns activity still forecast stock price inflections. Importantly, AIns activity forecast stock price movement even when choice behavior and conventional stock indicators did not (e.g., previous stock price movements), and classifier analysis indicated that forecasts based on brain activity should generalize to other markets. By demonstrating that AIns activity might serve as a leading indicator of stock price inflections, these findings imply that neural responses associated with anticipatory affect may extend to forecasting aggregate choice in dynamic and competitive environments such as stock markets.

Record of the seminar (Access code: v4@x#5Hu)


The role of inter- and intracultural mental variations


Vladimir Apanovich

Junior researcher, International Laboratory for Social Neurobiology,Institute for Cognitive Neurosciences


The concept of mentality in actual research is revealed. The importance of the category of analytic-holistic thinking is emphasized (including its manifestations at the inter- and intracultural level). The articles are analyzed, which describe the brain support of behavior in mental variations, including those related to various types of activity and forms of social interaction. The results are interpreted from the standpoint of a systemic evolutionary approach. The evolutionary aspect of the existence of intra- and intercultural variations is discussed.

Recording of the seminar. (Access code: LCQJ@9FV)

It’s not what you look at that matters, it’s what you see


Yaara Yeshurun

Director of Social and cognitive neuroscience laboratory, Tel-Aviv University


People frequently interpret the same information differently, based on their prior beliefs and views. This may occur in everyday settings, as when two friends are watching the same movie, but also in more consequential circumstances, such as when people interpret the same news differently based on their political views. The role of subjective knowledge in altering how the brain processes narratives has been explored mainly in controlled settings. Yaara Yeshurun presented two projects that examines neural mechanisms underlying narrative interpretation “in the wild” -- how responses differ between two groups of people who interpret the same narrative in two coherent, but opposing ways. In the first project were manipulated participant’s prior knowledge to make them interpret the narrative differently, and found that responses in high-order areas, including the default mode network, language areas and subsets of the mirror neuron system, tend to be similar among people who share the same interpretation, but different from people with an opposing interpretation. In contrast to the active manipulation of participants’ interpretation in the first study, in the second (ongoing) project were examined these processes in a more ecological setting. Taking advantage of people’s natural tendencies to interpret the world through their own (political) filters, we examine these mechanisms while measuring their brain response to political movie clips. These studies are intended to deepen our understanding of the differences in subjective construal processes, by mapping their underlying brain mechanisms.

Recording of the seminar. (Access code: #Y@S1mCT)

Machine learning for approximating endpoints in clinical neuroscience from heterogenous input data


Denis A. Engemann

Research Scientist, French National Institute of Computer Science (Inria-Saclay), Parietal Team.


Machine learning has pushed the study of brain health beyond comparing group averages. The statistical view of machine learning as function approximation in high dimensions is attractive for clinical neuroscience as it lends itself towards robust modeling of clinical endpoints from rich and heterogenous input data (Engemann et al., 2018). A full realization of this research program currently hinges upon meeting important challenges related to the reality of data in clinical neuroscience. In the small-sample regime, common to clinical neuroscience studies, pooling various imaging and electrophysiology modalities places a premium on strong domain knowledge, good priors and flexible handling of missing inputs (Engemann et al., 2020, Sabbagh et al., 2020). When the actual endpoint of interest, e.g. neuropsychiatric diagnosis, is expensive to acquire, the total number of labeled samples may not suffice to enable high-fidelity machine learning. In such situations, developing proxy measures derived from widely available labeled targets, such as age, provides a viable workaround (Engemann et al., 2020, Dadi et al., 2020). Even when data is available in abundance, the study of mental health often poses the additional challenge that, in contrast to somatic medicine no confirmatory measures exist such as plasma-assessment of insulin levels in diabetes. Here, building various proxy measures becomes a way of life and, depending on the target of interest, behavioral and sociodemographic input data can become the go-to modality for studying mental health (Dadi et al., 2020). This raises the question if machine learning itself can be used to automatically extract proxy measures from brain signals without explicit choice of one specific proxy target. Recent advances in deep learning and non-linear independent component analysis have given rise to the self-supervised learning paradigm. Applied to EEG data, SSL readily captures the structure of clinical recordings reflecting various factors such as the clinical operator, electrode selection, date, age, sleep stages and neuropsychiatric pathology (Banville et al.,2020). While causal mechanisms are still under investigation powered by experimental approaches, the statistical learning paradigm offers a viable tool for studying mental health using multiple imaging, electrophysiological and behavioral techniques capitalizing on all available data.

Recording of the seminar. (Access code: N73WeV!y)

Transcranial magnetic stimulation of the PPC leads to safer and more consistent choices under risk


Ksenia Panidi

Senior Research Fellow, Center for Cognition and Decision-Making, Institute of Cognitive Neurosciences, HSE


Recent economic theories of choice under risk postulate that the observed risk-taking behavior in monetary domain may be determined by the valuation of money (i.e. how much a person values one additional dollar, or a marginal utility of money) as well as specific perception of probabilities (i.e. probability weighting). However, existing neuroeconomic studies of risk taking usually focus on the analysis of the degree of the observed risk taking per se without disentangling its individual components. Recent neuroeconomic research suggests that posterior parietal cortex (PPC) may play a role in risky decision-making. In the present study, we employ transcranial magnetic stimulation to explore the effects of decreased PPC excitability on distinct components of risky choice. We report three main findings. First, participants tend to make safer choices after the stimulation of the left PPC. Second, this shift in preferences results from both decreased valuation of monetary rewards as well as a higher distortion of probabilities. Finally, because both of these changes shift preferences towards safer choices, this also makes decision-making process easier for participants which results in an increased consistency of choices.

Genetically informative study of the relationship between neuronal dynamics and cognitive functions


Ilya Zakharov, Anna Tobueva

Research Fellow of Developmental Behavioral Genetics Lab Psychological Institute of Russian Academy of Education


A powerful way to investigate the source of individual differences in various characteristics is genetically informative study. Starting from classical twin designs it nowadays incorporates not only quantitative genetics, but molecular genetics as well. In neuroscience genetically informative design can help to disentangle different factors underlying the relationship between neuronal dynamics and cognitive functions. In the present talk we will overview the current state in the genetically informative studies in neuroscience and present the design of the ongoing study carried out on the basis of MEG Center.

The biological underpinnings of economic behavior


Evgenia Lukinova

Postdoctoral Fellow, Erich Lab,Institute of Brain and Cognitive Sciences NYU-ECNU, New York, Shanghai, China


To what extent does genetic influence on economic behavior matter and can be enhanced or weakened by environmental factors? The answer to this question will help to understand the main biological pathways of economic behavior. Eugenia's report addressed this issue, taking it apart. She first showed some preliminary results from her Shanghai lab on how environmental factors caused by stress influence decision making. In this study, she used students and participants from China in general with economic tasks and stress questionnaires, and collected and analyzed saliva and hair samples. Stress questionnaires track signs of "stress" or measure perceived stress, saliva samples provide a measure of cortisol concentration at a specific point in time, and hair cortisol can correspond to chronic stress. She went on to share the work of her colleagues on how rodents can be stressed through physical restraint or pharmacology and whether this alters their behavior in the risk task. Finally, she discussed the existing genetic associations with risks and timing preferences, summarizing recent (GWAS studies across the entire genomic association) work.

Recording of the seminar (Access Password: 8a * i + * + 9)

Statistical analysis techniques for quantifying brain activity during naturalistic paradigms


Glerean Enrico

Research Fellow, International Laboratory of Social Neurobiology, Institute of Cognitive Neurosciences, HSE


The use of naturalistic stimuli while collecting data about the brain (for example, watching a feature film, listening to an audiobook, listening to music) has proven to be a very powerful tool for studying brain activity in a living setting. However, the additional uncontrollable complexity of naturalistic stimuli has posed new challenges for developing methods for understanding brain data as measured by naturalistic paradigms.In this talk, Enrico Glerean presented techniques such as intersubject correlation, intersubject phase synchronization, intersubject functional connectivity, and intersubject similarity analysis. These techniques have proven to be very useful in understanding the similarities and differences between individual brains processing complex naturalistic stimuli in healthy and clinical populations.

Recording of the seminar

The Impact of Media Literacy Skills on Narrative Interpretation: EEG Study


Olga Kuskova

Research assistant at the Laboratory of Social Neurobiology, Institute of Cognitive Neurosciences,
Master's Student of "Cognitive Sciences and Technologies: From Neuron to Cognition" program, HSE


In recent years there has been a large discussion on the influence media has on people’s views, attitudes, and beliefs. Advances in technologies and the increasing presence of mass media in people’s lives raise a question about the methods which could decrease susceptibility to media influence. Media literacy skills and the ability to critically analyze media narratives could be considered as such methods.

The aim of the proposed research is to look at media literacy skills as a possible cognitive tool which allows to decrease conformity in the context of media narrative interpretation. In order to do so, we are going to compare the differences in the narrative interpretations among those who apply media literacy skills and those who do not. We will consider the differences and similarities in brain activity during the narratives processing using electroencephalography and inter-subject correlation method.

How are sensory predictions modulated by behavior? A MEG study


Athina Tzovara

Assistent Professor, Institute for Computer Science, University of Bern, Switzerland


In our day to day lives we are constantly immersed in streams of sensory events like sounds or images, which very often follow repetitive patterns (Garrido et al., 2009). Because of these patterns, it is possible to use past experience to predict future events, before these occur, for example the sound of a siren might predict the arrival of an ambulance.

Cortical and subcortical brain regions allow us to extract patterns from repeating events, and form predictions about the future (Barascud et al., 2016). Forming predictions can take place either in cases where they are relevant to our actions i.e. while paying attention to environmental stimuli, but also in an automatic way, i.e. while our levels of arousal are low (Tzovara et al., 2015) and attention is distracted (Chouiter et al., 2015). Although actions and behavioural relevance have a strong effect on sensory processing, it still remains unknown how they may alter the generation of sensory predictions, and therefore the learning of new patterns.

In this study we will use magnetoencephalography (MEG), in combination with eye-tracking and behavioural metrics, in order to study how the formation of predictions is affected by participants’ behaviour.

Offline seminar: Armyansky, 4, 118.

In search for reliable individual differences in brain function: strengths and weaknesses of fMRI


Andrey P. Anokhin

Staff scientist, Department of Psychiatry, Medicine School, Washington University in St. Louis. USA


Identification of stable and heritable individual differences in functional brain organization is essential for understanding the biological bases of both normal-range individual differences (cognitive abilities, personality) and psychopathology. This lecture evaluated the utility of resting-state and task-related functional Magnetic Resonance Imaging (fMRI) measures as indicators of individual differences in brain function. In addition, a comparative analysis of strengths and weaknesses of fMRI and electrophysiology (EEG/ERP) methods was provided.

Offline seminar: Armyansky, 4, 118.

Applied scientometrics for modern researchers: some basic tools and metrics


Ivan Sterligov

Director of the HSE Scientometrics Centre


In the seminar, Ivan Sterligov, the director of the HSE Scientometrics Centre, gave a talk about the main bibliometric indicators of journals, scientists, and organizations, and will pay attention to wrong indicators usage in Russian and international practice. The talk covered the pros and cons of impact-factor, SNIP, SJR, CiteScore, Eigenfactor, citation normalization, H-index and its derivatives,  and expert’s journal ratings. Also, there was a demonstration of sources of scientometric information: analytical tools (SciVal and InCites) and VoSviewer, an instrument for scientometric network visualization. Attendees had an opportunity to ask any questions about the use of scientometrics in HSE, including staff research.

Offline seminar: Armyansky, 4, 118.


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