Author: Benjamin

Lecturer: Michael Bressler
Fields: Medicine/Neuroscience/Software Engineering

Content

Following an amputation, up to 90% of those affected experience sensations of their absent limb. These sensations can range from a simple sense of the limb\’s continued presence to the perception of a distorted or twisted phantom limb, which can result in significant pain and discomfort. The duration of phantom limb pain can persist for several years and negatively affects the individual\’s overall quality of life.

While the underlying mechanism of phantom limb pain is not fully understood yet, there are several theories that attempt to explain the phenomenon. To date, there is also no standard therapy for alleviating the pain. Treatment of phantom limb pain with conventional pain medications is often not effective. A frequent treatment method, but also not effective in all patients, is mirror therapy, in which the presence of the missing limb is visually simulated to the brain by placing a mirror in the sagital plane of the patient. To improve the effectiveness of this approach, researchers are exploring the use of immersive digital media, such as augmented reality (AR) or virtual reality (VR), as well as sensory feedback techniques.

This talk gives an overview of the phenomenon of phantom sensations and phantom limb pain, and addresses the proposed theories. The talk will present the development of a software tool called C.A.L.A., which is designed to facilitate the visualization and documentation of phantom limbs. Additionally, an augmented reality (AR) game, developed as a digital extension of mirror therapy, will be introduced. Finally, the course will discuss strategies for the long-term reduction of phantom limb pain by means of computer-aided technologies.

Literature

• Bressler M, Merk J, Heinzel J, Butz MV, Daigeler A, Kolbenschlag J, Prahm C. Visualizing the Unseen: Illustrating and Documenting Phantom Limb Sensations and Phantom Limb Pain With C.A.L.A. Front Rehabil Sci. 2022 Feb 9;3:806114. doi: 10.3389/fresc.2022.806114. PMID: 36189032; PMCID: PMC9397903.
• Prahm C, Bressler M, Eckstein K, Kuzuoka H, Daigeler A, Kolbenschlag J. Developing a wearable Augmented Reality for treating phantom limb pain using the Microsoft Hololens 2. ACM Int Conf Proceeding Ser. 2022;1(1):309–12. doi:10.1145/3519391.3524031.
• Flor H. Phantom-limb pain: characteristics, causes, and treatment. Lancet Neurol. 2002 Jul;1(3):182-9. doi:10.1016/s1474-4422(02)00074-1. PMID: 12849487.
• Melzack R. Pain and the neuromatrix in the brain. J Dent Educ. 2001 Dec;65(12):1378-82. PMID: 11780656.
• Ortiz-Catalan M. The Stochastic Entanglement and Phantom Motor Execution Hypotheses: A Theoretical Framework for the Origin and Treatment of Phantom Limb Pain. Front Neurol. 2018 Sep 6;9:748. doi: 10.3389/fneur.2018.00748. PMID: 30237784; PMCID: PMC6135916

Lecturer

Michael Bressler finished his Master’s degree in Information Technology at the Technical University of Vienna with a focus on human-computer interfaces and user interface design. After working as a software engineer for several years in the private sector, he returned to research at the department for Hand, Plastic, Reconstructive and Burn Surgery at the University Clinic of Tuebingen/BG Hospital, Germany. His research mainly focuses on computer assisted rehabilitation, virtual and augmented reality, and serious games for health.

Affiliation: BG Hospital, University Clinic of Tuebingen, Department for Hand, Plastic, Reconstructive and Burn Surgery
Homepage: https://www.bg-kliniken.de/klinik-tuebingen/fachbereiche/detail/rekonstruktive-chirurgie/

Lecturer: Marius Klug
Fields: Cognitive Neuroscience

Content

Recent technological advancements in instrumentation and analysis methods of human brain imaging data such as electroencephalography (EEG) increasingly allow the measurement of mobile participants interacting with their environment. The new field of Mobile Brain/Body Imaging (MoBI) (Gramann et al., 2011; Jungnickel et al., 2019) combines these measurements with imaging methods regarding the body, such as motion or eye tracking, and analyzes the multimodal data in order to investigate natural cognition in action. These analyses require the synchronized import of all data streams, options to process body data modalities, reliable preprocessing of EEG data in light of the elevated amount of non-cortical contributions in mobile settings, and the combined functional analysis of all modalities.

To facilitate this process, the BeMoBIL Pipeline was created (Klug et al., 2022). This is an open-source MATLAB toolbox for fully synchronized, automatic, transparent, and replicable import, processing, and visualization of MoBI and other EEG data. It includes wrappers for EEGLAB functions, uses various existing EEGLAB plugins, and comes with additional new functionalities such as the extraction of events from the data. All parameters are configurable in central scripts and everything is additionally stored in the data itself, facilitating the report and replication of MoBI studies. Throughout the process, plots are generated to keep the researchers informed.

This course will introduce the concept of MoBI, explain EEG analysis in the BeMoBIL Pipeline with details and parameter choices, and give an outlook on example applications and future prospects.

Literature

• Gramann, K., Gwin, J. T., Ferris, D. P., Oie, K., Jung, T. P., Lin, C. T., Liao, L. D., & Makeig, S. (2011). Cognition in action: Imaging brain/body dynamics in mobile humans. Reviews in the Neurosciences, 22(6), 593–608.
• Jungnickel, E., Gehrke, L., Klug, M., & Gramann, K. (2019). MoBI-Mobile Brain/Body Imaging. In H. Ayaz & F. Dehais (Eds.), Neuroergonomics: The Brain at Work and in Everyday Life (1st ed., pp. 59–63). Elsevier.
• Klug, M., Jeung, S., Wunderlich, A., Gehrke, L., Protzak, J., Djebbara, Z., Argubi-Wollesen, A., Wollesen, B., & Gramann, K. (2022). The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data. In bioRxiv.

Lecturer

Marius studied Cognitive Science in Tübingen, Germany, and received his PhD in Cognitive Neuroscience at the TU Berin, Germany. His research focused on methodological considerations and advancements of Mobile Brain/Body Imaging data analysis. In his new research group at BTU Cottbus, Germany, he will investigate the application of physiological data as user interfaces in virtual reality.

Affiliation: TU Berlin, BTU Cottbus, Zander Labs
Homepage: https://discord.gg/7MJjQ3f

Lecturer: Birgit Peterson
Fields: Scientific Writing

Content

The epistemological process of academic working and particularly the processes of scientific reading and writing involve a variety of materialities, mindsets and spaces. So, the ability to adapt these complex processes in a situated way, combining different materialities and different mindsets methodologically to create harmonic rhythms and step sequences of working is crucial for further development. This “choreography”, the arrangement of materialities over time and space, influences the results and the success of scientific writing and academic work.
In this practical course we are going to reflect and share our individual “choreographies” of academic working. Although all 4 parts built on each other, it is possible to join for only one topic as well.

In the 1st lecture, the focus will be, on how we create spaces and adjust it to the needs of our diverse academic working processes. What are these spaces constituted of? How do we arrange stuff and staff to design a prosperous atmosphere? And how do these different spaces and influences our cognitive processes, enabling different choreographies of thinking and working?

In The 2nd lecture we explore our reading behaviour. First, we discuss, how we are framed by different reading space and materialities, Then we focus on how we switch them and our mindsets to better interact with literature and data for different purposes. Finally, we look on promising strategies to thrive our successful reading processes.

In the 3rd lecture we will extend these explorations to our writing behaviour, focussing on the one hand on the diverse materialities that are involved in embodying thoughts in verbal and non-verbal products, and how we change our choreographies when shifting between drafting, rewriting and revision processes.

The 4th Lecture draws form the former ones but additionally stresses the roles of rhythms, patterns and the scrambling of elements for the whole composition: How does the harmonic compilation of spaces, materialities and mindsets, and the rhythm of switches within them overall, constitute a successful choreography for our personal academic working processes?

Lecturer

Birgit Peterson

Affiliation: University of Vienna
Homepage: https://www.germ.univie.ac.at/birgit-peterson/

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?

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 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

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 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/

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 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 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

Lecturer: Astrid Prinz
Fields: Computational Neuroscience

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/

Lecturer: Diedrich Wolter
Fields: Artificial Intelligence

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

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/

Lecturer: Laura Bringmann
Fields: Psychology/Statistics

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

• https://www.sciencedirect.com/science/article/pii/S2352250X21000300
• https://www.researchgate.net/publication/334557550_What_Do_Centrality_Measures_Measure_in_Psychological_Networks

Lecturer

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/