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Regular version of the site

Thesis Topics and Regulations

Thesis Regulations

Thesis Rules and Regulations 

Rules App 1: Statement CW Topic 

Rules App 2: Statement CW Topic Exchange 

Rules App 3: Statement MT Topic

Rules App 4: Statement MT Topic Exchange 

Rules App 5: Thesis Manual 

Rules App 6: Supervisor Evaluation Form 

Rules App 7:Review Form 

Thesis Topics 

Vasily Klucharev, Faculty of Social Sciences | School of Psychology: Professor 

  1. Neuroscience of social influence, persuasion, propaganda   
  2. Neuroeconomics of the decisions under risk
  3. Neuroeconomics studies of the cognitive dissonance 
  4. Neuroeconomics of the financial decisions 

Anna Shestakova, PhD, Director of the Centre for Cognitive and Decision Making

        1. Neurocognitive mechanisms of consumer behavior. Neuromarketing.
        2. Cognitive function in individuals with motor deficits. 

Yulia Kovas, Professor of Genetics and Psychology, Goldsmiths, University of London, Director of InLab
  1. Genetically informative studies of cognitive abilities and other individual characteristics related to mathematical performance. A twin study is under way, collecting a wide range of measures from the twin pairs. Participants of the study perform behavioral tests and questionnaires, and then take part in EEG experiment. We also collect their saliva and anthropological measures. There is also a test-retest singleton (non-twin) group of the participants who take part in the study twice so that we can measure the stability of the tests.
  2. Simulation study of the bias introduced by confounding variables in twin research. Adjustment of the phenotypic variables for the effects of sex and age is widely accepted practice in twin research (McGue & Bouchard, 1984). Such variables may increase phenotypic similarity of twins introducing bias to the estimates of genetic and environmental effects. The aim of this study is to estimate the bias under various conditions (sample size, genetic and environmental effects, effect of confounding variable).
  3. Machine learning methods in analysis of brain activity data. There is a new field of Machine Learning, Deep Learning, which can take advantage of the graphical nature of EEG derived data and which has shown to outperform many methods for a wide range of applications (REF). ML methods and Artificial Intelligence methods including Deep Learning will allow researchers world-wide to make better use of current and future EEG derived data. We have samples big enough and computational capacities (cluster computer resources) to address this problem.  The project involves working with EEG analyses experts to refine existing and develop new methods of multi-channel data analyses.
  4. Cross-cultural studies of executive functions. The project aims to collect executive functions data in Russia, UK, Kyrgisia, China and other countries to understand the nature of cross-cultural differences in variability and/or average performance in these abilities.
  5. Transcranial electric stimulation (TES) studies with control twin method. Electric Brain Stimulation is an invaluable tool in research into brain function.  Applying this method in combination with using MZ twins as participants in experimental and placebo-groups provides a powerful test of potential effects. For such test we developed a TES protocol promising for improvement of learning skills.
  6. Executive functions and brain activity in children with local brain lesions. Together with clinicians we are investigating the psychological and psychophysiological states of children who came through brain surgery. We use neuropsychological test batteries, eye-tracking and EEG to assess the effectiveness of recovery and training procedures these children undergo.
Any questions can be forwarded to Ilya Zakharov (iliazaharov@gmail.com), research associate at Russian-British Laboratory for Behavioural Genetics at the Psychological Insititue of the Russian Academy of Education.
All the work is supervised by Yulia Kovas and Sergey Malykh, Director of the Russian-British Laboratory for Behavioural Genetics at the Psychological Insititue of the Russian Academy of Education, member of Russian Academy of Education.

W.Joseph MacInnes   Assistant Professor, Faculty of Social Sciences / School of Psychology
  1. To what degree to eye movements and attention overlap? (Eye tracking, computational modelling, Inhibition of return, remapping).
  2. Top down and bottom up influences in visual search and how do they combine? (Eye tracking, computational modelling, Inhibition of return)
  3. Computational models of task and attentional state: Can we model top down control without a homunculus? (computational modelling, behavioural experiments)
  4. What is the time course of exogenous attention and re-entrant processing in early visual areas (Eye tracking, computational modelling, facilitation, possible collaborations with Neuroscience group)

Yury ShtyrovCentre for Cognition & Decision Making: Leading Research Fellow:
  1. Neural dynamics of language comprehension and production
  2. Cognitive control in communication
  3. Sensory-motor integration and embodied cognition
  4. Psychological and psychophysiological bases of numeracy
  5. Interactions between domain-specific and domain-general cognitive systems
  6. Language acquisition
  7. Communication deficits (e.g. aphasia)
  8. Cognitive and neural mechanisms of bilingualism

Tadamasa Sawada, Faculty of Social Sciences | School of Psychology: Associate Professor    
  1. Haptic/Tactile perception: perception of a shape of an object (haptic) and of texture (tactile)
  2. Visual perception of a 3D shape
  3. Visual perception of depth (e.g. stereo, familiarity, texture-gradient)
  4. Practical problems in analysis and statistics   
                       
Matteo FeurraFaculty of Social Sciences | School of Psychology: Associate Professor
  1. Long-term Memory Processes     
  2. Motor Control and Mirror Neurons System by Non Invasive Brain Stimulation (TMS, tDCS, tACS)      
  3. Disentangling Working Memory System trough sub-components investigation           
  4. Non invasive Brain Stimulation and Decision Making   
  5. Methods: Testing Transcranial Electrical Stimulation effects by different stimulation waveforms     
  6. Methods: Combining Non Invasive Brain Stimulation with EEG       

Boris GutkinCentre for Cognition & Decision Making: Leading Research Fellow:
  1. Modelling the mechanism and functional significance of neuronal oscillations in cognitive tasks
  2. Modelling complexity in neural dynamics
  3. Modelling drug addiction
  4. Modelling decision processes and their modulation by supply and demand
  5. Developing novel financial market modelling methods
                       
Marie Arsalidou, Faculty of Social Sciences | School of Psychology: Associate Professor
  1. Development of cognitive abilities in multiple domains.
  2. Functional neuroimaging of cognitive and emotional processes.
  3. Quantitative meta-analyses of fMRI data.  
  4. Relations between cognitive load and eye-movements.
  5. Bilingualism and cognitive control.         
                       
Vladimir F. Spiridonov, Scientific-Educational Laboratory for Cognitive Research: Chief Research Fellow
  1. Problem solving: strategies, representation, psychological mechanisms of mistakes and successful solving;      
  2. Differences between experts and novices in problem solving (algebra, chess, biochemistry, puzzles, etc.);      
  3. Interaction of several languages in the cognitive system of polyglots (bilinguals, trilinguals, and so on);      
  4. Psychological nature of Insight.  
                       
Dmirty V. Lyusin , Scientific-Educational Laboratory for Cognitive Research: Leading Research Fellow     
        1.      Congruency and complementarity effects in processing of emotional information.     
                 Research questions -  
                 What are the relationships between specific emotional states of an individual and a facilitation or an inhibition        
                (1) of the processing of various types of emotional stimuli   
                (2) or of the recognition of particular emotions?       
         2.    Effects of moods on the scope of cognition.
                Research questions -  
                What are the mood dimensions that influence the scope of cognition? Are they valence, arousal, motivational intensity or their combinations?           
                What is the mood influence on specific aspects of cognition – namely, focus of attention, global vs. local processing, categorization?   
                       
Igor S. Utochkin, Faculty of Social Sciences | School of Psychology: Associate Professor
        1.      Ensemble perception in vision       
Our capacity to process information deeply (pay attention to objects, maintain them in working memory, recognize, and record to long-term memory) is severely limited to a small handful of objects at one time - probably no more than 2-4 items. However, our visual experience is much broader than just a few objects every moment. Basically, we believe that we are aware of everything our eyes are currently looking at. How can our visual system deals with this? One promising theory says that this ability is supported by efficient computation of summary statistics across big collections of objects and their features. The representation of multiple objects in the form of statistical summaries is often referred to as ensemble perception. If I show you, say, 20 circles with various sizes for just 1/5 sec and then show you a single circle and ask “Did you see a circle of exactly that size a moment ago?”, you will likely have no idea - because of your limited capacities. But if I ask you “Is this circle larger or smaller than the mean size of all circles?”, your answer will be surprisingly accurate. Our lab does a lot of work on ensemble perception trying to understand how the visual system is capable of doing so efficient computations so quickly.
        2.     Visual working memory and long-term memory

We are extremely interested in how people store and retrieve information about objects they have seen. Visual working-memory is a limited-capacity and relatively short-term storage actively maintaining information about a few objects that are necessary for an ongoing task. Long-term-memory is a much bigger and long-lasting storage for objects that we have seen before and left working memory (if not forgotten). In our lab, we study many intriguing questions about these memory systems. What is the format of stored representations - are objects stored as whole units or their features are stored separately (e.g., is shape of an object is stored separately from its color)? How do objects remembered together affect and probably distort each other’s representation in memory? Are real-world objects (things that we see and use every day) stored differently than simple meaningless lab objects (like simple geometric shapes of random colors)?

      3.      Visual search and selective attention
Within this topic, we investigate how people exploit their limited-capacity attentional mechanisms to find things they want to find (targets) among the irrelevant stuff (distractors). How do the physical properties of the scene affect the difficulty of search? How does the knowledge of target features “guide” attention towards a potential target and allows to bypass a lot of things without even looking at them? What if you look for several different things at one time? How does your previous experience in a search task change your ability to find things quicker and accurately (the question relevant for some occupations such as radiology diagnostics or security check in the airport)?

Alexei OssadtchiCentre for Cognition & Decision Making, Leading Research Fellow; Director of Centre for Bioelectric Interfaces:
  1. Optimization of neurofeedback performance
  2. From dipoles to networks: novel analytic methods for detection of networks in EEG and MEG data
  3. The use of optimal signal processing methods in brain-computer interfaces
                        
Boris V. Chernyshev, Faculty of Social Sciences | School of Psychology | Department of Psychophysiology: Associate Professor 
  1. Electrophysiological markers and brain mechanisms of spontaneous attentional lapses   
  2. Psychophysiology of individual differences in fluctuations of attention          
  3. Mind-wandering as a possible cause of spontaneous attentional lapses
  4. Psychophysiology of intra-modal and inter-modal feature binding: the relative role of automatic and attentional processes.       
  5. Relationship between attention and awareness studied with visual evoked potentials.    
  6. Brain mechanisms of Kanizsa illusion: MEG study.       
  7. Brain mechanisms of ultrarapid acquisition of "embodied" word meaning: MEG study.

Vadim NikulinCentre for Cognition & Decision Making: Leading Research Fellow:
  1. Functional significance of neuronal oscillations in sensory, motor and cognitive tasks
  2. Complexity in neural dynamics
  3. Cortico-muscular interactions in normal subjects and patients (e.g. stroke)
  4. Development of the novel analytic and recording techniques for EEG/MEG                 

Olga V. DragoyFaculty of Humanities, Center for Language and Brain: Director

  1. Neural foundations of language processing (MRI, electrophysiological and neurostimulation evidence)       
  2. Psycholinguistic studies (behavioral and eye-tracking evidence)
  3. Bilingualism and crosslinguistic studies
  4. Language assessment and rehabilitation in brain-damaged populations (with aphasia, tumors, epilepsy)
  5. Normal and pathological language acquisition
  6. Interaction between language and other cognitive functions 
                     
Alexey A. Kotov, Scientific-Educational Laboratory for Cognitive Research: Senior Research Fellow       
  1. Contemporary versions the hypothesis of linguistic relativity: the influence of language on categorization      
  2. Development of early childhood concepts from perceptual categories to inductive inferences
  3. Early strategies in mapping new words onto objects by children from 2 to 4 years      
  4. Models of multiple systems of categorical learning: parallel or sequential functioning                      

Victoriya V. Ovsyannikova
, Scientific-Educational Laboratory for Cognitive Research: Senior Research Fellow  
  1. The influence of emotional states and traits on emotion recognition.    
  2. Individual differences in visual search of emotional stimuli.     
  3. Effects of attention on emotional information processing.


Beatriz Bermúdez-Margaretto, Research Fellow, Center for Cognition and Decision Making, HSE

1. Brain dynamics of novel word learning in spoken and written language. 2. Biliteracy advantage: behavioral, oculomotor and EEG signatures of novel word learning in L1 (Cyrillic) and L2 (Roman) orthographic scripts. 3.
Cross-linguistic transfer between L1 and L2 during novel word learning.
4.
 Disentangling temporal and spatial correlates of novel word learning by mean of electrophysiological and neuromodulatory techniques. 

 Beatriz Martin-Luengo, Senior Research Fellow, Center for Cognition and Decision Making, HSE

1. Metacognition and second language acquisition.
2. Neural correlates of false memories.

Projects available in the MEG lab of the Higher School of Economics

 P1. Clinical biomarkers for epilepsy surgery in intracranial stereo EEG

 P2. HFO as clinical biomarkers for epilepsy surgery in MEG

 P3. HFO as clinical biomarkers for epilepsy surgery in MEG

 P4 Characterization of the temporal and spatial dynamics of the nested oscillations in human NREM sleep: sEEG study

 P5 The relation between mesial temporal lobe and posterior parietal cortex along the span of working memory capacity

 P6 Spatial and temporal working memory identified by distinct oscillatory activity

 P7 How are sensory predictions modulated by behaviour? A MEG study

 P8 Cortico-hippocampal connectivity in associative memory processing: a stereo-EEG study

 P9. What are experts made of? Uncovering expertise in motor sequence learning

 P10. Increasing motor sequence learning expertise: A meditation approach

 

Olga Sysoeva, Leading researcher, Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences
 
1.     Rapid brain system for semantic analysis of written words. 

How quickly can we extract semantic information during reading? The study of brain activity provides the answer that we can’t get with any behavioral technique. Indeed, while it takes quite a lot for you to read this text (about 10 seconds on average), your brain needs less than 100 ms to distinguish whether the word is abstract or concrete (Sysoeva et al., 2007). How can we use this intriguing finding to improve the reading skills? In your thesis this topic will be expanding into subconscious analysis of written words. The goal is to describe the neurophysiological mechanisms of rapid semantic analysis using EEG/MEG technique.

2.     Time perception: gene-brain-behavior. 

Our subjective perception of time can be studied by objective methods of psychophysics and EEG/MEG. Moreover, we can track individual differences in this ability into genetic polymorphisms (Sysoeva et al., 2010). The topic of your thesis might be related to neurophysiological mechanisms of time perception and its influence by external factors (Sysoeva et al., 2013; Portnova et al.,2010). How other psychological traits (e.g. impulsivity) correlates with individual differences in time perception being related to the same neurogenetic characteristics. Alternation in the subjective time flow in different neuropsychiatric conditions might be also considered. Why the main part of the work will be based on human studies, we might be able to test some of our hypothesis in the experiments with rodents.

3.     Psychophysiological profile of autism spectrum disorders and associated genetic syndromes

By recording the EEG and extracting its activity related to particular sensory and cognitive abilities we aim to construct a non-invasive objective psychophysiological profile of different subtypes of autism spectrum disorders. The thesis topic might be linked to different aspects within the above identified framework, including application of advanced EEG analysis technique (e.g. machine learning) to the unique EEG datasets.

For inquiries: olga.v.sysoeva@gmail.com