Keynote Lecture 3 – Discussion Rounds

Originally, this slot was intended to host a keynote lecture by Georg von Wichert. Unfortunately, this had to be cancelled. Instead, we offer:

Lecture Hall 2 – Small discussion rounds on the current war situation

Lecturers: Ihor Arkhypchuk & Jutta Kretzberg

After two years of pandemic, the world faces a new challenge, the war in Ukraine. Many of us feel helpless in view of the shocking news of millions of people suffering. In this meeting, we want to provide a forum to the IK participants for speaking in small groups about our personal perspectives on the current situation: How do I feel in this situation and what is my way of coping with it? What can I do to improve the situation for myself or / and for other people?
In this way, we hope to encourage the IK participants to think about their own ways out of that paralyzing feeling of helplessness.
To get the discussion started, Ihor will share his perspective of a Ukrainian student in Germany and give some practical advice on how to welcome refugees from his country.

Lecture Hall 3 – Artificial Intelligence in Education

Lecturer: Benjamin Paaßen

Teachers are notoriously overworked and yet are confronted with ever higher demands. To support teachers, AI researchers have tried to automate parts of the teaching process for 40 years now. Yet, education remains a challenging application domain for AI. I will provide a brief overview over the vision of AI in education, introduce some of the typical tools, and provide some insight on the practical and ethical challenges. Then, I want to open the floor to discussion, e.g. with respect to the following questions:

  • Do you think AI could/should be applied in your own educational context?
  • What are the practical and ethical challenges to achieve high-quality AI systems for education?
  • In which direction should AI in education research go in the future?

Rainbow Course 3 – The Flexibility of fMRI Results

Lecturer: Nina Demšar
Fields: Neuroscience

Content

The course is a combination of a theoretical and practical look into functional magnetic resonance imaging (fMRI) analysis, with an emphasis on comparing the different approaches via various software tools.

Functional magnetic resonance imaging is the most commonly used method for imaging brain activity today. The method is based on the BOLD signal that then goes through a complex analysis process to create images locating brain activity. There are various tools that can be used for this data analysis; the most commonly used being Analysis of Functional Images – AFNI (Cox, 1996), FMRIB Software Library – FSL (Smith et al., 2004) and Statistical Parametric Mapping – SPM (Friston et al., 1995; Penny et al., 2006). The whole process of analysis is made up of many steps, decisions concerning the order of said steps and specific parameter values. Since each tool uses different settings and code, it is possible for the results to be different depending on the tool used.

The first part of the course is an introduction to fMRI and, specifically, analysis of the data – going through the most common steps of preprocessing, first-level and second-level analysis. This is followed by a short demonstration of a small dataset being analyzed using the three most common software tools. The demonstration allows the participants to better understand the process of analysis, while also seeing the different ways researchers can approach an fMRI experiment. This practical understanding allows for the final part of the course to be more easily understandable. This part focuses on a few studies which made comparisons of various analyses and showed the differences between the approaches and the results that came out of them (e.g. Botvinik-Nezer et al., 2020; Carp, 2012a; Carp, 2012b; Poline et al., 2006) . The course is concluded with an open question, whether the analytical flexibility of fMRI studies undermines its results and what that means for neuroscience.

Literature

  • Botvinik-Nezer, R., et al. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature 583, 84-88. https://doi.org/10.1038/s41586-020-2314-9
  • Carp, J. (2012a). On the plurality of (methodological) worlds: estimating the analytic flexibility of fMRI experiments. Frontiers in Neuroscience, 6(149). https://doi.org/10.3389/fnins.2012.00149
  • Carp, J. (2012b). The secret lives of experiments: methods reporting in the fMRI literature. Neuroimage, 63(1), 289-300. https://doi.org/10.1016/j.neuroimage.2012.07.004
  • Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162-173. https://doi.org/10.1006/cbmr.1996.0014
  • Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. P., Frith, C. D. & Frackowiak, R. S. J. (1995). Statistical parametric maps in functional neuroimaging: a general linear approach. Human Brain Mapping, 4(2), 189-210. https://www.fil.ion.ucl.ac.uk/~karl/Statisticalparametricmapsinfunctionalimaging.pdf
  • Penny, W. D., Friston, K. J. Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. (2006). Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic press.
  • Poline, J., Strother, S. C., Dehaene-Lambertz, G., Egan, G. F. & Lancaster, J. L. (2006). Motivation and synthesis of the FIAC experiment: Reproducibility of fMRI results across expert analyses. Human Brain Mapping, 27(5), 351-359. https://doi.org/10.1002/hbm.20268
  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M. & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(1), 208-219. https://doi.org/10.1016/j.neuroimage.2004.07.051

Lecturer

Nina Demšar

Nina Demšar received her BSc in Biopsychology at the University of Primorska in 2016 and then completed her MSc in Cognitive Science in 2020 at the University of Ljubljana, University of Vienna, Comenius University in Bratislava and Eötvös Loránd University Budapest. During that time she focused on fMRI methodology and wrote her thesis on the Results of functional magnetic resonance imaging analysis with different software tools – a comparison. She is now working on her PhD in Biomedicine (Neuroscience) at the University of Ljubljana and working as a young researcher at the Center for Clinical Physiology at the Faculty of Medicine there.

Affiliation: Faculty of Medicine, University of Ljubljana

Rainbow Course 2 – Speech decoding for brain-computer interfaces

Lecturer: Julia Berezutskaya
Fields: Neurotechnology, Artificial Intelligence, Speech, Invasive Brain Recordings

Content

Brain-computer interfaces (BCI) is a field of assistive technology that can provide severely paralyzed patients with means of communication. In this talk I will focus on invasive BCIs with a long-term goal to bring the developed technology to clinical application in severely paralyzed patients (i.e. patients with a “locked-in” syndrome due to a neuron motor disease or brainstem stroke). I will give an overview of the state-of-the-art in the BCI field and discuss the most recent developments in decoding and reconstruction of speech signals directly from the brain activity. Specifically, I will talk about two case studies by Vansteensel et al. (2016) and Moses et al. (2020) that both demonstrated the proof of concept in development of invasive BCIs for communication, successful implantation of the device in a paralyzed individual and validation of the assistive technology for communication over a long period of time. Then, I will discuss three main approaches to decoding of speech signals from intracranial data based on 1) the language perspective (decoding language elements of speech: words, phonemes, sentences), 2) the acoustic perspective (decoding of acoustic properties of speech signal and direct speech synthesis from brain data), and 3) the motor perspective (decoding of articulatory motor programs from brain activity). Relevant work from the labs worldwide will be discussed (groups led by E. Chang, P. Kennedy, J. Brumberg, N. Mesgarani in USA; N. Ramsey, C. Herff, B. Yvert in Europe).

Next to reviewing the advances in speech decoding from brain data, I will also cover specifics of intracranial recordings, discuss the difference between overt (spoken out loud) and covert (imagined) speech (relevant for decoding in paralyzed subjects as opposed to able-bodied individuals), and talk about the methodology of decoding approaches (machine learning and deep learning details). At the end of the talk, I will initiate a discussion with the attendees about the future of the BCI technology, next steps needed to bring BCI research to real-world applications, and controversies and challenges still remaining in the field.

Literature

  • Vansteensel, M. J., Pels, E. G., Bleichner, M. G., Branco, M. P., Denison, T., Freudenburg, Z. V., … & Ramsey, N. F. (2016). Fully implanted brain–computer interface in a locked-in patient with ALS. New England Journal of Medicine, 375(21), 2060-2066.
  • Moses, D. A., Metzger, S. L., Liu, J. R., Anumanchipalli, G. K., Makin, J. G., Sun, P. F., … & Chang, E. F. (2021). Neuroprosthesis for decoding speech in a paralyzed person with anarthria. New England Journal of Medicine, 385(3), 217-227.
  • Herff, C., Heger, D., De Pesters, A., Telaar, D., Brunner, P., Schalk, G., & Schultz, T. (2015). Brain-to-text: decoding spoken phrases from phone representations in the brain. Frontiers in neuroscience, 9, 217. Berezutskaya, J. (2020). Data-driven modeling of the neural dynamics underlying language processing (Doctoral dissertation, University Utrecht).
  • Berezutskaya J., Ramsey N.F. & van Gerven M.A.J (in prep) Best practices in speech reconstruction from intracranial brain data.

Lecturer

Julia Berezutskaya

Julia Berezutskaya is a postdoctoral researcher at the Artificial Intelligence department of Radboud University (affiliated with Donders Center for Brain, Behavior and Cognition). In collaboration with University Medical Center in Utrecht (UMCU) she works on computational modeling of speech processes in the human brain. In 2020 she completed her PhD on “Data-driven modelling of speech processes in intracranial data” at UMCU. After the PhD she became a coordinating postdoc within the Language in Interaction consortium (https://www.languageininteraction.nl/) focusing on application of artificial intelligence methods to brain data underlying speech. In 2021 she joined the European consortium on Neurotechnology INTENSE, where she develops models for speech decoding and reconstruction from brain activity. She is a postdoc representative of the BCI society and a member of the UMCU Young Academy. In 2021 she was a recipient of the prestigious Trainee and Professional Development award from Society of Neuroscience. Julia is focused on bringing together people who work on natural language processing, machine learning, computational neuroscience and clinical neuroscience so that together they can build powerful models of speech production and perception in the brain. Not only are such models important for our fundamental understanding of how the brain works, they are essential for development of assistive neurotechnology and brain-computer interfaces that can restore cognitive function in patients, such as communication via decoding of attempted speech in paralyzed individuals.

Affiliation: Donders institute for Brain, Cognition and Behaviour
Homepage: https://www.juliaberezutskaya.com/

Rainbow Course 1 – Why Computational Neurophenomenology? Questions for bridging First and Third Person Science

Lecturer: Jelena Rosic & Moritz Kriegleder
Fields: Neuroscience, Phenomenology, Computer Science

Content

The research program of neurophenomenology targets the epistemological and methodological challenges of relating the neurophysiological third-person measures to descriptions of subjective phenomena in first-person experience (Varela 1996). Stemming from the gap between (neuro)behavioural and phenomenological data in addressing mental phenomena in cognitive science, neurophenomenology considers the phenomenality of lived experience necessary in any current theory of cognition. In our talk, we will start by reviewing the basic arguments from the founding work on methodological commitments to naturalise phenomenology – making phenomenological properties and constraints continuous with those of natural sciences to provide an explanatory framework (Roy et al., 1999).
In this light, we examine the notion of phenomenological data and the methods to obtain such data through systematic practice of phenomenological reduction leading to descriptive invariants (Depraz, Varela & Vermersch 2000). We point to the pragmatics of second-person methods as in-depth interview techniques that stand in contrast to naïve (self-reported) introspection or to typical approaches in cognitive science that collect first-person data through pre-defined questionnaires, scales, and ratings as easily quantifiable measures yet lacking complexity and dynamics of subjective experience. In particular, the interview and analysis method of Micro-phenomenology (Petitmengin 2006) promises a systematic and detailed collection of first-person data. The aim is to establish the structure of experience rather than representational content that doesn’t reflect the lived character of experience and is considered purely qualitative and non-verifiable while the structure of experience aims to provide invariants for generalisable results (Petitmengin, Remillieux & Valenzuela-Moguillansky 2019).
Looking into the growing field of neurophenomenological research that aims to provide non-naïve and reliable experiential data with rigorous methods, we will continue with the approach to naturalise phenomenology and review recent theories of cognition and how they aim to formally model experience. Computationally grasping phenomenological phenomena is a significant step in bridging the gap between first and third-person perspective. Predictive processing, a highly influential framework of the last decade, casts the mind as a dynamical prediction generator that constantly updates its best guesses in coordination with the senses and environment (Hohwy, 2013). We are going to present arguments why or why not these processes could be sufficient to explain lived experience and act as stepping stone to mathematically trace phenomenology.
Related to predictive processing, the free energy principle is a more general framework which unifies all living systems under one organisational principle. The mathematical formalism builds on the assumption that all organisms minimise free energy, a measure of surprise about internal and external states. Recent works have acknowledged the role of embodiment and action more and recast the theory as active inference, with direct links to enactivism (Ramstead, 2020). We summarise the connection between predictive processing, the free energy principle and a naturalised phenomenology to model experience and explore possible future directions of computational neurophenomenology.

Literature

  • Depraz, N., Varela, F. J., & Vermersch, P. (2000). The gesture of awareness: An account of its structural dynamics. Investigating phenomenal consciousness, 121-136.
  • Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.
  • Petitmengin, C. (2006). Describing one’s subjective experience in the second person: An interview method for the science of consciousness. Phenomenology and the Cognitive sciences, 5(3), 229-269.
  • Petitmengin, C., Remillieux, A., & Valenzuela-Moguillansky, C. (2019). Discovering the structures of lived experience. Phenomenology and the Cognitive Sciences, 18(4), 691-730.
  • Ramstead, M. J. D., Kirchhoff, M. D. & Friston, K. J. (2020). A Tale of Two Densities: Active Inference Is Enactive Inference. Adaptive Behavior 28 (4): 225–39. Roy, J. M., Petitot, J., Pachoud, B. & Varela, F. J. (1999) Beyond the gap: An introduction to naturalizing phenomenology. In: Petitot J., Varela F. J., Pachoud B. & Roy J. M. (eds.) Naturalizing phenomenology: Issues in contemporary phenomenology and cognitive science. Stanford University Press, Stanford CA: 1–83.
  • Varela, F. J. (1996). Neurophenomenology: A methodological remedy for the hard problem. Journal of consciousness studies, 3(4), 330-349.

Lecturer

Jelena Rosic
Jelena Rosic

Jelena Rosic is a master’s student in cognitive science at the University of Vienna and a Ph.D. student at Aalto University, previously working on interdisciplinary projects that combine the methods of neuroscience with arts and humanities. She completed training in Micro-phenomenology (with Claire Petitmengin) and has been practicing the method aiming at neurophenomenological integration and modeling.

Moritz Kriegleder
Moritz Kriegleder

Moritz Kriegleder is a Ph.D. student at the University of Vienna in the ERC Project Possible Life of Professor Tarja Knuuttila and is a member of the Association for Mathematical Consciousness Science. He has a background in physics and cognitive science and investigates the philosophical and mathematical foundations of computational models of cognition, such as the free energy principle.

Affiliation: University of Vienna

Keynote Lecture 4 – Homeostatic regulation of neuronal networks

Lecturer: Astrid Prinz
Fields: Computational Neuroscience

Nervous system

Content

Neuronal networks produce reliable output throughout an animal’s life despite constant turnover of circuit components and developmental and environmental changes. I will discuss how circuits achieve this functional homeostasis, and describe the potential role of parameter variability and degeneracy in homeostasis.

Literature

  • Prinz AA, Bucher D, Marder E (2004). Similar network activity from disparate circuit parameters. Nature Neurosci 7:1345-1352.
  • Gunay C, Prinz AA (2010). Model calcium sensors for network homeostasis: Sensor and readout parameter analysis from a database of model neuronal networks. J Neurosci 30: 1686-1698.
  • Olypher AV, Prinz AA (2010). Geometry and Dynamics of Activity-Dependent Homeostatic Regulation in Neurons. J Comp Neurosci 28: 361-374.

Lecturer

Astrid Prinz received her PhD in Physics from Technische Universität Muenchen in 2000. After postdoctoral work with Eve Marder at Brandeis University, she joined the faculty in the Department of Biology at Emory University in Atlanta in 2005. She combines experimental and computational methods to investigate pattern generation, signal processing, variability, and homeostasis in small neuronal circuits.

Affiliation: Emory University
Homepage: http://www.biology.emory.edu/research/Prinz/

Practical Course 7 – AI for Angry Birds

Lecturer: Diedrich Wolter
Fields: Artificial Intelligence

AI & Robotics

Content

AI has defeated human players in several games so far, including board and video games. However, computers cannot compete with humans in physical simulation games, let alone in in real-world every day problem solving in a physical environment. Why?
In this course we set out to explore why solving novel physical interaction problems is so hard and we discuss AI techniques such as reasoning, planning, or learning that may help to make progress towards mastering physical manipulation tasks. Participants in the course will make their own experiments by implementing rules or algorithms to make their own agent. We take a look at the Bambird agent we develop that has won the international scientific “AI Birds” competition twice and discuss the gap between AI and human performance we still need to bridge.

Literature

  • Kenneth D. Forbus (2019). Qualitative Representations: How People Reason and Learn about the Continuous World, MIT press
  • Malik Ghallab, Dana Nau, Paolo Traverso (2016). Automated Planning and Action, , Cambridge University Press
  • Peter Norvig, Stuart J. Russell (2020). Artificial Intelligence: A Modern Approach, 4th edition, Prentice Hall

Lecturer

Diedrich Wolter
Diedrich Wolter

Prof. Dr. Diedrich Wolter studied informatics at the University of Hamburg and obtained his doctoral degree from the University of Bremen in 2006 where he researched spatial representation and reasoning for mobile robots in context of the multi-disciplinary research center “spatial cognition”. He continued as principal investigator in the spatial cognition research center before joining Bamberg University in 2013. At Bamberg, he heads the group on Smart Environments which studies Knowledge Representations and their use in intelligent systems. Since 2016 he develops the “Bambirds“ agent together with his students. So far, they have won the international AI birds competition twice and they aim to advance AI’s performance in real-world problem solving. He first visited IK Günne in 1997 as student.

Affiliation: University of Bamberg
Homepage: https://www.uni-bamberg.de/en/sme/team/diedrich-wolter/

Lecture Series 5 – Network analysis in psychology

Lecturer: Laura Bringmann
Fields: Psychology/Statistics

Mind

Content

This course consists of two hours.

The first hour (Tuesday) will be a lecture on psychological networks. In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach thereby focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. Time-series are needed to map network theory to network models in clinical practice, and currently, vector autoregressive models are represented as psychological networks. In this talk, I will discuss the reliability and validity of these psychological networks and their usefulness (or not) for clinical practice.

In the second meeting (Thursday), we will discuss this assignment that you will have made beforehand. It will involve theoretical questions, and also technical questions in R for those who are interested in more technical aspects of the models.

Literature

Lecturer

Laura Bringmann

Dr. Laura Bringmann is an assistant professor at the Psychometrics and Statistics department of the University of Groningen. She studied at the University of Amsterdam, receiving a bachelor’s degree in clinical neuropsychology, a bachelor’s degree in philosophy, and a master’s degree in psychological methods. She also has a master’s degree in clinical neuroscience from Ruhr University Bochum. In October 2016, she defended her PhD thesis at KU Leuven, Belgium, supervised by Francis Tuerlinckx and Denny Borsboom. Her main interests are time series analyses, dynamical networks, ESM in clinical practice, and philosophy of psychology. She is a part of The Interdisciplinary Center Psychopathology and Emotion regulation (ICPE) and the Young Academy Groningen (from September 2018-2023).

Affiliation: University of Groningen
Homepage: https://www.laurabringmannlab.com/

Lecture Series 6 – Plasticity in neural networks

Lecturer: Fleur Zeldenrust
Fields: Computational Neuroscience

Nervous system

Content

The brain continuously processes information. The physical structure of the brain (its ‘hardware’) shapes this information processing and vice versa: the computations needed for information processing (the ‘software’) are adapted to the physical structure of the hardware. Moreover, both the hardware and the software are flexible: we change the way we sense the world by actively changing the physical properties of our brain to adapt to the task at hand. In this lecture, I will discuss what we already know about these mutual flexible interactions, and what the current open questions are.

Lecturer

Fleur Zeldenrust
Fleur Zeldenrust

Dr Fleur Zeldenrust studied physics and neuroscience at the University of Amsterdam, where she also did her PhD in computational neuroscience. She was a postdoctoral fellow at the École Normale Supérieure in Paris, after which she returned to the Netherlands to set up a track in computational neuroscience in the ‘Psychobiology’ BSc program at the University of Amsterdam. A Veni and a Mohrmann grant (2015) allowed her to set up her own research group called ‘Biophysics of Neural Computation’ at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, where she recently got tenured. She also recently became a member of ‘De Jonge Akademie’.

Affiliation: Donders Institute for Brain, Cognition and Behaviour, Radboud University
Homepage: https://fleurzeldenrust.nl/

Career Fishbowl

Many people are unsure of how to proceed with their career. No matter whether you are a student wondering what comes after your degree or an experienced professional considering new opportunities, this fishbowl discussion is supposed to provide inside views of different career options.

In the fishbowl, all panelists and the moderator are together on stage, with a couple of additional chairs. Participants who want to ask a question or give their own perspectives are welcome to take a seat on stage for some time and leave it then to somebody else.

We have a great panel of people with roots in academic research, who have chosen different career paths inside and outside of academia:

Lecturers

Enrico Fucci
Enrico Fucci

Enrico Fucci holds a PhD in Neuroscience from UCBL Lyon 1, France. Implementing multidisciplinary approaches from neuroimaging, experimental psychology and neurophenomenology, his research aims to create bridges between Western science and contemplative traditions on topics such as social cognition, emotion regulation and perceptual learning. He is currently a researcher and board member of the Institute for Globally Distributed Open Research and Education (IGDORE), an independent research institute dedicated to improving the quality of science and quality of life for scientists and their families, with a strong emphasis on location-independent work and open and reproducible research. Check Enrico’s academic publications here.

Lydia Nemec
Lydia Nemec

Lydia Nemec is a theoretical physicist with research focus at the interface between theoretical physics, computer science and chemistry. After her postdoc at the TU Munich, her career started as a Data Scientist in the Rail Industry at Knorr-Bremse. Today, she is the head of the ZEISS Data Science team – the ZEISS AI Accelerator.

Janina Radny
Janina Radny

Janina Radny had a quite flexible career path. She started in forest ecology, made a detour into event management, and then took the step into science management at the Bernstein Network Computational Neuroscience. She’ll be happy to share how to upcycle experiences from former lifes to create new ideas.

Moritz Tenorth
Moritz Tenorth

Moritz Tenorth currently is CTO at Magazino. After spending several years in robotics research, he previously worked as robotics consultant for Siemens Novel Businesses. During his research at TU München, the CMU Robotics Institute in Pittsburgh, the ATR in Kyoto, and the University of Bremen, he investigated how autonomous manipulation robots can be equipped with Artificial Intelligence methods, in particular knowledge representation and reasoning capabilities. He published more than 50 articles and conference papers in robotics and AI. Moritz Tenorth obtained a diploma in Electrical Engineering from RWTH Aachen University and a PhD in Computer Science from TU Munich.

Bastian Epp
Bastian Epp

Bastian Epp: I studied a (at the time) novel study programme merging engineering and physics which lead to a doctoral degree in physics. I was so lucky that I was affiliated with an interdisciplinary graduate school, bringing together people from physics, biology, computer science and psychology. This formed the starting point to be able to follow my excitement of all aspects of nature and to share this excitement with other people. In science I explore the sense of hearing – in the overlap between physics, engineering and Ibiology. In my teaching activities, I enjoy to experience the moment where someone suddenly gains a deep understanding of some aspect of science.

Keynote lecture 6 – Flexible brains in social contexts

Lecturer: Suzanne Dikker
Fields: neuroscience, social psychology

Mind

Content

When we feel like we’re ‘on the same wavelength’ with another person, are our brainwaves literally ‘in sync’? Methodological innovations now make it possible to study the human brain during naturalistic social events. We will discuss examples from both within and outside the laboratory to explore how our brains and bodies adapt to others and to our environment during dynamic face-to-face social interactions, and how such flexibility may help facilitate successful communication and increase social connectedness.

Literature

  • Hoehl, S., Fairhurst, M., & Schirmer, A. (2021). Interactional synchrony: signals, mechanisms and benefits. Social Cognitive and Affective Neuroscience, 16(1-2), 5-18.
  • Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S., & Keysers, C. (2012). Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends in cognitive sciences, 16(2), 114-121.
  • Koole, S. L., & Tschacher, W. (2016). Synchrony in psychotherapy: A review and an integrative framework for the therapeutic alliance. Frontiers in psychology, 7, 862.
  • Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J.J., Ding, M. and Poeppel, D., 2017. Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Current biology, 27(9), 375-1380.

Lecturer

Dr. Suzanne Dikker

Suzanne Dikker’s work merges cognitive neuroscience, performance art and education. She uses a ‘crowdsourcing’ neuroscience approach to bring human brain and behavior research out of the lab, into real-world, everyday situations, with the goal to characterize the brain basis of dynamic human social communication. As a senior research scientist at the Max Planck — NYU Center for Language, Music and Emotion (CLaME), affiliate research scientist at the Department of Clinical Psychology at VU Amsterdam, and member of the art/science collective OOSTRIK + DIKKER, Suzanne leads various research projects, including MindHive, a citizen science platform that supports community-based initiatives and student-teacher-scientist partnerships for human brain and behavior research.

Affiliation: NYU-Max Planck Center for Language, Music and Emotion
Homepage: www.suzannedikker.net