RC3 – Intuitive XR – Physiological user-interfaces for interactive virtual worlds

Lecturer: Marius Klug
Fields: Neuroscience


Virtual Reality (VR) and Extended Reality (XR) offer unprecedented opportunities for creating immersive experiences. However, the challenge remains: How can we make interactions within these virtual worlds as intuitive and natural as possible? This talk aims to shed light on the utilization of physiological data—captured via techniques like EEG, EMG, EKG, and eye-tracking—as a groundbreaking approach to enhancing XR interfaces. We will explore the foundational principles of Mobile Brain/Body Imaging (MoBI) and passive Brain-Computer Interfaces (BCIs) that enable real-time, dynamic user interaction. Delving into ongoing experiments, we will discuss how physiological signals can be integrated into XR experiences, effectively \”humanizing\” our engagement with virtual worlds. Finally, the talk will address potential pitfalls and ethical considerations, as well as the transformative possibilities this research holds for the fields of Human-Computer Interaction (HCI), neuroscience, and beyond.


Marius Klug studied cognitive science in Tübingen and was already in contact with EEG as a measurement method and brain-computer interface during that time. He subsequently earned his doctorate in the field of mobile brain research under Prof. Klaus Gramann at TU Berlin. There, he extensively dealt with EEG analysis methods and virtual reality as an experimental method. Specifically, the application of EEG in a mobile context, the cleaning of data, and their interpretation in conjunction with other measurements, such as body and eye movements, were the focus of the research. The continuation of this research can now be found at BTU in the form of the practical use of psychophysiological measurement methods as an interface for real-time applications.

Affiliation: BTU Cottbus-Senftenberg
Homepage: https://www.b-tu.de/researchschool/gefoerderte-forschungsaktivitaeten/young-investigator-group-intuitive-xr

SC9 – Looking for Function in Social Systems and Sometimes Finding It: Ants, Humans, and Beyond

Lecturer: Theodore Pavlic
Fields: Behavioral Ecology, Game Theory, Modeling, Natural History, Collective Behavior, Ants, Bees, Wasps, Social Insects, Social Behavior


In this 4-part course, we delve into the fascinating world of social systems, drawing parallels between the intricacies of social-insect colonies and the complexities of human societies. Through the lens of resilience, robustness, and responsibility, we explore the dual nature of collective behavior, examining how it can both empower and challenge the adaptability of societies.

We begin by unraveling the mechanisms that enable social-insect colonies (with a particular focus on ants and social bees) to flexibly respond to environmental changes, while also scrutinizing the vulnerabilities that arise from individuals valuing public information too much over private information. We will also show motivational examples of natural collective decision-making systems in ant colonies that have qualitatively different cognitive capabilities at the level of the collective than the individual, effectively reducing the burden of individual responsibility in a functioning society.

We then continue to highlight strengths, weaknesses, opportunities, and threats in social systems by reviewing fundamental results from game theory. We start with a review of the Prisoner\’s Dilemma and ask whether it has much practical value. We then transition to a wider range of social games, such as the Hawk–Dove and the Stag Hunt, that highlight how living in societies is challenged not only by alignment of agendas but also coordination of collective action when there is limited information. This sets us up to talk about N-person games and equilibrium concepts that apply to larger social groups, with examples from social foraging to make this more concrete. Empowered with these game-theoretic fundamentals, we can then discuss the problem of altruism in societies and pivot to discussing alternative explanations for altruism based less on social relatedness (responsibility) and more on risk management (resilience and robustness).

In an attempt to close on an optimistic note, we conclude this course with examples of the various benefits of highly integrated social life viewed through the lenses of resilience, robustness, and responsibility. We uncover profound benefits of colony life in many social insects and discuss the mechanisms that underly these adaptations.

Ultimately, this course provides an opportunity to navigate through the complexities of social organization, shedding light on the fundamental principles that underpin the resilience, robustness, and responsibility of societies across the biological spectrum to highlight both how societies can protect from disturbances from the outside while also introducing new risks to manage that come from within.


  • Goss, S., Beckers, R., Deneubourg, J. L., Aron, S., & Pasteels, J. M. (1990). How trail laying and trail following can solve foraging problems for ant colonies. In Behavioural mechanisms of food selection (pp. 661-678). Springer Berlin Heidelberg.
  • Dussutour, A., Beekman, M., Nicolis, S. C., & Meyer, B. (2009). Noise improves collective decision-making by ants in dynamic environments. Proceedings of the Royal Society B: Biological Sciences, 276(1677), 4353-4361.
  • Weinstein, S., Pavlic, T. P., Walker, S. I., Davies, P. C. W., & Ellis, G. F. R. (2017). Noise and function. In From Matter to Life: Information and Causality (pp. 174-198). Cambridge University Press.
  • Pavlic, T. P., & Pratt, S. C. (2013). Superorganismic behavior via human computation. Handbook of human computation, 911-960.
  • Wilson, E. O. (1962). Chemical communication among workers of the fire ant Solenopsis saevissima (Fr. Smith) 2. An information analysis of the odour trail. Animal Behaviour, 10(1-2), 148-158.
  • Guo, X., Lin, M. R., Azizi, A., Saldyt, L. P., Kang, Y., Pavlic, T. P., & Fewell, J. H. (2022). Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning. Proceedings of the Royal Society B, 289(1967), 20212176.
  • Sasaki, T., & Pratt, S. C. (2011). Emergence of group rationality from irrational individuals. Behavioral Ecology, 22(2), 276-281.
  • Sasaki, T., & Pratt, S. C. (2012). Groups have a larger cognitive capacity than individuals. Current Biology, 22(19), R827-R829.
  • Burns, D. D., Franks, D. W., Parr, C., & Robinson, E. J. (2021). Ant colony nest networks adapt to resource disruption. Journal of Animal Ecology, 90(1), 143-152.
  • Wenzel, J. W., & Pickering, J. (1991). Cooperative foraging, productivity, and the central limit theorem. Proceedings of the National Academy of Sciences, 88(1), 36-38.
  • Sendova-Franks, A. B., & Franks, N. R. (1994). Social resilience in individual worker ants and its role in division of labour. Proceedings of the Royal Society of London. Series B: Biological Sciences, 256(1347), 305-309.
  • Middleton, E. J., & Latty, T. (2016). Resilience in social insect infrastructure systems. Journal of The Royal Society Interface, 13(116), 20151022.
  • Linksvayer, T. A., & Janssen, M. A. (2009). Traits underlying the capacity of ant colonies to adapt to disturbance and stress regimes. Systems Research and Behavioral Science: The Official Journal of the International Federation for Systems Research, 26(3), 315-329.
  • Naug, D. (2009). Structure and resilience of the social network in an insect colony as a function of colony size. Behavioral Ecology and Sociobiology, 63, 1023-1028.
  • Naug, D. (2008). Structure of the social network and its influence on transmission dynamics in a honeybee colony. Behavioral Ecology and Sociobiology, 62, 1719-1725.
  • Stroeymeyt N, Casillas-Pérez B, Cremer S. Organisational immunity in social insects. Current Opinion in Insect Science. 2014 Nov 1;5:1-5.


Dr. Theodore (Ted) Pavlic [@TedPavlic] is an Associate Professor at Arizona State University (ASU) jointly appointed in the School of Computing and Augmented Intelligence and the School of Life Sciences. His industry experience (before graduate school) includes work in analog electronics for instrumentation as well as a decade working in software engineering. In 2010, he received his PhD from The Ohio State University in electrical and computer engineering where he studied the design of nonlinear and optimal control systems for autonomy that were inspired by models of animal decision making from behavioral ecology. He followed his PhD with postdoctoral training in both computer science (software verification techniques applied to cyber-physical systems) and animal behavior (collective behavior of ants) and then started his faculty position at ASU in 2015. His research focuses on understanding adaptive decision-making strategies in autonomous systems. To this end, his laboratory does empirical work with natural systems, such as understanding resource allocation and decision-making in social-insect colonies, and does engineering work building decision-making algorithms for artificial systems, such as decentralized energy management systems for the built environment and novel neural network architectures inspired by the insect brain. Just as the biological models provide inspiration for novel engineering solutions, the engineering problems inspire new lines of scientific inquiry about those biological systems. Students and postdoctoral researchers in Pavlic\’s lab come from a wide range of disciplines, from Computer Science/Engineering to Industrial Engineering to Applied Mathematics to Biology and Animal Behavior, and participate in a similarly diverse range of academic communities. Professor Pavlic was the founding associate director of research for The Biomimicry Center at Arizona State University and continues to be closely associated with the center. He is also active in several of the efforts across ASU\’s campus focused on complex adaptive systems science. He is a member of several professional societies across engineering and the sciences, including IEEE, ACM, ABS, and IUSSI.

Affiliation: Arizona State University
Homepage: https://search.asu.edu/profile/1995237

RC1 – Higher-order phenomena in complex systems with a special focus on the brain: an hands-on experience

Lecturer: Matteo Neri & Mar Estarellas
Fields: Complexity, Information theory, neuroscience


Within complexity sciences, various approaches have been developed to investigate the intricate web of interactions underpinning the behavior of complex systems, such as society or the brain. In the last two decades, a great deal of success was reached by theoretical and computational frameworks based on the study of pairwise interactions [1]. Recently, a growing body of literature focused on the extension of these approaches to the study of higher-order interactions, i.e. between more than 2 units of a system [2, 3]. In this course, we will introduce some of the main concepts in the field (e.g. synergy and redundancy in terms of information content [4]), discuss results obtained by previous work, and show how concretely they can be employed in the study of real-world data, with a special focus on cognitive neuroscience and consciousness [5].

We will present our work, focusing on the relationship between the human brain and its internal processes as well as its interaction with the world around us, adopting information-theoretic approaches to analyze neuroimaging data collected with different techniques, in particular MEG, EEG, and fMRI.

Additionally, we aim to offer a hands-on experience by providing access to a cutting-edge toolbox [6], making the session interactive and applicable to real-world scenarios.

The proposed syllabus will be the following:

– Basic introduction to graph and information theoretical approaches, focusing on higher-order interactions.
– State of the art in using these measures to investigate complexity from brain to economy, with a special focus on cognition and consciousness science.
– Hands-on experience (bring your computer if you can!)



Matteo Neri is a PhD student in Cognitive Neuroscience, at Aix-Marseille Université with a background in Mathematics and Complex Systems Physics. His main interests are the study of complexity and emergence in the investigation of cognitive neuroscience and psychology, addressing questions as: how cognitive functions and consciousness relates to brain activity? What can we understand studying the activity patterns of different units of a complex system?

Affiliation: Institut de Neuroscience de la Timone
Homepage: https://www.int.univ-amu.fr/

Mar Estarellas is a postdoctoral researcher at the Consciousness and Cognition Lab, Cambridge University. The objective of her current post-doctoral research project is twofold. First, to employ a diverse range of analytical and computational methods, with an emphasis on information theory, to discern the neural informational features that correlate with consciousness and cognition across a spectrum of conscious states and populations, including those affected by dementia. Second, beyond the pursuit of fundamental scientific knowledge, a significant portion of this research is dedicated to using these biomarkers for early detection, stratification, and prognosis of dementia.She is deeply passionate about the symbiotic relationship between art, nature, and contemplative practices, and their collective potential to foster mental and ecological well-being.

Affiliation: Consciousness and Cognition Lab, Queen Mary University of London & University of Cambridge, UK

Homepage: https://www.ccc-lab.org/mar-estarellas.html

RC2 – Adaptive Ambulatory Assessment in Digital Health and MORE: Presenting and Experimenting with the Modular Open Research Platform for Situated and Longitudinal Human-Subject Research

Lecturer: David Haag
Fields: Psychology / Cognitive Science / Data Science


Ambulatory assessment has been an emerging paradigm in psychological and health research, allowing for the intensive longitudinal study of individuals\’ behavior, physiology, and experiences in their natural environments. By capturing data in the flow of daily life, ambulatory assessment allows us to gain insights into the dynamic processes that shape an individual’s behavior and to consecutively adapt interventions to the individual and their current context. The integration of digital technologies has revolutionized ambulatory assessment, offering robust methodologies for investigating complex systems ranging from individual behavior to broader socio-ecological interactions.

The course will provide an introduction into ambulatory assessment, its significance in current research, particularly with modern approaches such as Ecological Momentary Assessment (EMA) and its potential for future applications within psychology, digital health and beyond. By examining various studies, we will explore how this methodology contributes to our understanding of human behavior and system interactions in situ, furthering the discourse on adaptive capacities within complex environments.

Additionally, we will introduce the open-source platform MORE (Modular Open Research Platform; https://dhp.lbg.ac.at/more/?lang=en), an innovative tool developed at the Ludwig Boltzmann Institute for Digital Health and Prevention in Salzburg in a research and development effort led by Dr. Jan David Smeddinck. The system is aimed at establishing a digital infrastructure capable of handling the complexities of modern health studies enabling researchers to carry out ambitious ambulatory assessment studies. It addresses the integration of external devices, real-time data collection, participant administration, and data security standards. Notably, the platform contains near real-time data capture and interpretation features, thus allowing for the ecologically valid investigation of momentary antecedents of health behaviors or even testing interventions that adapt to an individual and their current context. This is crucial e.g. for research into personalization in digital health and further investigations along the concept of “precision health”. MORE comprises a web application for study management and a smartphone app for participant engagement. The smartphone app is configured to guide participants through study procedures, enabling the collection of questionnaire data and sensor information over extended periods in a minimally intrusive and minimally burdensome manner. The app\’s design reflects a commitment to sustainability, user-friendliness, and technological relevance.

In summary, this course will offer participants a blend of a theoretical introduction to ambulatory assessment and a comprehensive overview of the MORE platform, including its conception, key features, and examples of its capabilities to carry out ambulatory assessment studies. Additionally, attendees will have the opportunity for hands-on testing of the free and open-source system, providing a practical understanding of the platform.



David Haag
David Haag

David Haag is a PhD student in psychology working at the Ludwig Boltzmann Institute for Digital Health and Prevention in Salzburg (Austria). He received his master’s degree in psychology from the University of Graz in 2021. The same year, he started his PhD aiming to develop adaptive digital interventions to support individuals in achieving their physical activity goals. Following his initial investigation on momentary antecedents of physical activity using ambulatory assessment, he narrowed his research focus to action planning based behavior change interventions such as implementation intentions. His current work mostly deals with planning based Just-in-Time Adaptive Interventions (JITAIs) and how the latest developments in Large Language Models could tie in with JITAI implementation.

Affiliation: Ludwig Boltzmann Institute for Digital Health and Prevention
Homepage: https://dhp.lbg.ac.at/team/david-haag-msc/?lang=en

BC2 – Introduction to Neurobiology

Lecturer: Till Bockemühl
Fields: Neuroscience

Lecture 1: Basics

What does a neurobiologist do? What do nervous systems look like and how are they structured? Why do animals have nervous systems? What are neurons? How do neurons work and how do they communicate with each other?

Lecture 2: Sensory and motor systems

How do nervous systems sense the world, what is their input? How do vision, hearing, and the vestibular system work? How can nervous systems affect the world and what is their output? How do muscles work?

Lecture 3: Tools, methods, and model organisms

Which animals are studied in neuroscience? How can neuroscientists measure the structure and processes of nervous system? How do electrophysiology and calcium imaging work? Can we use genetics to explore and change nervous systems? What is optogenetics and how can we use it to control nervous systems?

Lecture 4: Important concepts

What are reflexes and how do they function? What are efference copies and corollary discharge and how are they used in nervous systems? How do animals navigate, what is path integration, and which structures in nervous systems contribute to this behavior?

Objectives of this course

This course is intended for listeners without a background in neurobiology or neuroscience. My goal is to give an overview of what it means to do neurobiology or be a neurobiologist, in general. I will broadly cover several levels of description, i.e. the cellular level, the systems level, methods, and concepts, so that the audience will get a good idea of what the current state of neurobiology and neuroscience is, how we address open questions in the field, and which developments might be coming in the future. The course will equip you with useful information applicable for other neuro-related courses during IK.


There are many excellent textbooks on general neurobiology and neuroscience; any of those will be helpful if you want to prepare for the course in advance. If I had to pick two, these books are more than comprehensive:

– Principles of Neural Science. Eds. Eric R. Kandel, James H. Schwartz, and Thomas M. Jessell. Vol. 5. New York: McGraw-Hill, 2012.

– From Neuron to Brain. Eds. John G. Nicholls, A. Robert Martin, Paul A. Fuchs, David A. Brown, Mathew E. Diamond, and David A. Weisblat. Vol. 5. Sunderland: Sinauer Associates, 2012.


Till Bockemühl studied biology and philosophy at Bielefeld University. He did his diploma thesis as well as his doctoral thesis with Volker Dürr in the lab of Holk Cruse at Bielefeld University. Currently, he is a postdoctoral researcher in the lab of Ansgar Büschges at the University of Cologne. His main research interests comprise the motor control of locomotion, neuroethology, and computational neurobiology. To investigate these topics, he uses the fruit fly Drosophila and the ever-expanding toolkit of methodological opportunities this model organism has to offer.

Affiliation: University of Cologne
Homepage: http://www.zoologie.uni-koeln.de/bueschges-staff-tillbockemuehl.html

Ronald Sladky. My research focuses on the amygdala and emotion processing in the human brain. In addition, I am always working on new neuroimaging, data processing, and modeling methods. One of these new methods is real-time functional MRI, where people can learn to regulate their own brain states while they are inside the MRI scanner. This method is not only a promising therapeutic tool, it will also allow for completely new ways of discovering how our brains work.

Affiliation: University of Vienna
Homepage: http://sweetneuron.at

ET2 – The End of Capitalism

Lecturer: Ulrike Herrmann
Fields: Economics/Politics


Humankind is ruining the planet, and the climate crisis is especially threatening. Hence, politics and economics hope for “green growth”. But this is an illusion. Green energy from solar panels and wind turbines will be insufficient to support permanent growth. The economy must shrink. Yet, shrinking would be the end of capitalism, because it is only stable as long as there is growth. Economic journalist Ulrike Herrmann describes what the future looks like – without growth, profit, cars, airplanes, banks, insurance companies and almost without meat.


Ulrike Herrmann

Ulrike Herrmann is financial editor at the German newspaper „tageszeitung“ (taz). She is a trained bank clerk and completed an internship (Volontariat) at the Henri-Nannen-School of Journalism. Subsequently, she studied history and philosophy at Freie Universität Berlin. She is regular guest on radio and television and authored several bestsellers. Her latest book is titled “The End of Capitalism: Why Growth and Climate Protection Are Incompatible – and How We Will Live in the Future” (KiWi 2022).

BC5 – Introduction to Computational Creativity

Lecturer: Philipp Wicke
Fields: Artificial Intelligence / Creativity


This course provides students with an overview on the research field of computational creativity. A focus of the systems covered is on language creativity, visual systems (diffusion models), and generative language models. The students learn various fundamental concepts of creativity research and generative computer models. In this context, corpora and systems are presented, their use is taught, and their application is practiced.


Philipp Wicke completed his B.Sc. at the University of Osnabrück and his PhD at University College Dublin.

SC12 – The Magic of Human Touch under Fighting Conditions. How to Reconcile in Stressful and Dangerous Situations. Experiences with the Japanese Martial Art Aikido.

Lecturer: Thomas Christaller


Since a few decades human touch became a hot topic in behaviour cognition, brain science, and psychology. Since the antiquity in Greece touch was regarded as the most primitive, animalistic sense in humans far away from the visual sense and its importance for human intelligence. Only Aristotle in one of his texts wrote that touch is the sense which makes us human. Today there is a huge amount of publications about the different roles touch plays in our capabilities to build up reliable social relationships with other humans. In general the appropriate touch helps you to trust others, becoming more self-confident, learning and performing better at school or university as well as performing a task.

In a way the forms of touch used in a fight have the opposite effect. They should hurt and dominate another person up to the point where this person gives in and subdue to the stronger, mightier, more brutal one. The major part of the training in the martial arts concentrate on techniques to control or hurt another person. The smaller part of the training is how to endure pain. The better you are in this the higher your chances are that you can take in punches.

Aikido is a martial art and has many very effective powerful techniques. But important is that you start to harmonize the forces of the other person with yours in a way that the person looses balance and you can finish a proposed attack in a way where nobody is threaten, hurt, dominated. In a way it is stopping the fight before it is overwhelming you.

In this course you will get physical, bodily experiences how to redirect a (simulated) attack into a harmless movement. And which important role touch plays there and your inner posture and emotions to those who are attacking you. We will reflect your experiences on a more scientific level to get step by step a more complete and complex picture of human nature. There is no previous knowledge necessary, neither in the cognitive framework of embodiment nor in martial arts. All experiments are carefully chosen so that physical power or strength doesn’t play a role and therefore you can perform these experiments and expect a lot of fun and no pain 😎


  • Amdur, Ellis. Hidden in Plain Sight. Esoteric Power Training within the Japanese Martial Traditions. Freelance Academy Press, Wheaton (IL), 2018.
  • Böhme, Rebecca. Human Touch. Warum körperliche Nähe wichtig ist. C.H. Beck, 2019.
  • Linden, David J. Touch: The Science of the Sense that Makes Us Human. Penguin, 2016.


Thomas Christaller

Thomas Christaller was a professor in Artificial Intelligence at the University of Bielefeld and director of the Fraunhofer Institute IAIS until his retirement in 2010. Since then he is a professional teacher for the Japanese martial art Aikido and running a dojo in Bonn (Zentrum für Bewegung & Lebenskunst). During his time as researcher he focused on building artificial systems to figure out what human intelligence is. He started with Natural Language Processing, turning to Knowledge-based Systems, and finally taking mobile robots as means to study embodied cognition.

He started to practice Aikido in 1972, making his first teaching experiences in 1976 at the University of Bielefeld. Over the years he blended his insights in AI about cognition with those he gained in Aikido. He gave Aikido seminars in Russia, USA, England, and Germany invited by diverse groups and organizations. While running a small robotics research lab as a side job in Kitakyushu, Japan, he gave Aikido classes at the campus of Wakamatsu. He has the 6th Dan (“Black Belt”) awarded by the so-called Hombu Dojo of Aikido in Tokyo. Here you can look at some videos to get an idea about his interpretation of Aikido. 

RC4 – Causality in Complex Systems – Implications of Dynamical Systems Theory for Theories of Cognition

Lecturer: Alexander Hölken
Fields: Dynamical Systems Theory, Complexity Theory, Causality, Cognitive Science


One of the central questions of cognitive science and neuroscience is that of how an organism’s intracranial states cause, and are in turn affected by, their behavior. Within cognitive science in particular, there exists a long-standing tradition of analyzing processes of cognition and behavior in terms of linear chains of cause and effect. For instance, computational theories of mind generally assume that the brain (or parts of it) operate similar to, or literally are, discrete-state machines (Chalmers, 2011). Thus, computational explanations of how cognitive processes cause behavior often refer to brain states as discrete entities, whose instantiation at some timestep t causes the instantiation of another discrete state at t+1, and so on, eventually realizing the behavior in question. While these assumptions are well-suited for explaining the development of abstract computational systems, they often fail when applied to real-life biological systems. This is because these systems don’t operate analogously to discrete-state machines:

1. Discrete-state systems are neatly decomposable into spatially separate entities that cause each other to change from one discrete timestep to the next. This need not be the case for complex natural systems (Kelso & Engstrom, 2008; Eigen, 2013; Lamb, 2015).

2. In discrete-state systems, temporality matters only in regards to the order in which system states happen in. In complex natural systems, both order and timing are causally relevant.

3. In discrete-state systems, the part/whole relationship is a mechanistically reductive one: The development of their larger-scale parts is explicable purely by reference to states of smaller-scale parts, along with their position on a causal chain. In complex natural systems, processes on the level of the whole system can causally influence processes on lower levels. This makes mechanistic reduction impossible: Even complete descriptions of lower-level interactions are not sufficient to explain the development of the whole (Bishop, 2008).

In this course, we will learn about the unique causal features of complex natural systems and how they explain their development. In particular, we will look at the relationship between wholes and their parts, and how system states at different levels constrain each other over time. In order to do so, we will become familiar with the concepts of dynamical regimes, attractor landscapes, and order/control parameters (Kelso, 1995) by looking at simple examples which do not presuppose a mathematical background. Many of these examples will come from experiments that explicitly aim to manipulate e.g. the control parameters of a dynamical regime, for instance in psychological studies about behavioral coordination (Schöner, Zanone & Kelso, 1992; Buchanan, 2004; Nalepka et al., 2021). Finally, we will wrap up by discussing possible solutions for contemporary problems that this methodology may offer, such as the creation of artificial general intelligence.


  • Bishop, R. C. (2008). Downward causation in fluid convection. Synthese 160, 229 – 248. DOI: 10.1007/s11229-006-9112-2
  • Buchanan, J. J. (2004). Learning a single limb multijoint coordination pattern: the impact of a mechanical constraint on the coordination dynamics of learning and transfer. Experimental Brain Research 156, 39 – 54. DOI: 10.1007/s00221-003-1763-3
  • Chalmers, D. J. (2011). A Computational Foundation for the Study of Cognition. Journal of Cognitive Science 12, 323 – 357.
  • Eigen, M. (2013). From Strange Simplicity to Complex Familiarity. Oxford University Press. • Kelso, J. A. S. (1995). Dynamic Patterns: The self-organization of brain and behavior. The MIT Press.
  • Kelso, J. A. S. & Engstrøm, D. A. (2008). The Complementary Nature. The MIT Press.
  • Lamb, M. (2015). Characteristics of Non-reductive Explanations in Complex Dynamical Systems Research. [Doctoral Thesis, University of Cincinnati]
  • Nalepka, P., Silva, P. L., Kallen, R. W., Shockley, K., Chemero, A., Saltzman, E. & Richardson, M. J. (2021). Task dynamics define the contextual emergence of human corralling behaviors. PloS ONE 16, e0260046. DOI: 10.1371/journal.pone.0260046
  • Schöner, G., Zanone, P. G., Kelso, J. A. S. (1992). Learning as Change of Coordination Dynamics: Theory and Experiment. Journal of Motor Behavior 24, 29 – 48


Alexander Hölken received his M.Sc. in Cognitive Science in March 2022 and is currently working on his PhD at the Ruhr-University Bochum. His research focuses on Dynamical Systems approaches to explaining cognitive behavior as a result of the coordination of intracranial states and processes with states and processes of the body and the environment. His doctoral thesis aims to develop a non-reductive physicalist theory of how mental states/processes can be genuinely causally efficacious.

Affiliation: Ruhr-Universität Bochum

RC3 – Infomation Theory and the Mind

Lecturer: Moritz Kriegleder
Fields: Cognitive Science, Information Theory, Philosophy of Science


Today, most discussions of the mind and brain include ambigious terms such as information, memory, and models. While we often do not question their origin and usage, we need to be aware of the philosophical problems of the direct analogy of computation and mental dynamics.
In my talk I will present the historical and mathematical basics of information theory and discuss its influence on cybernetics and cognitive science. While it has proven especially useful in cognitive neuroscience, our use of information to describe cognition stands on shaky philosophical ground. I will discuss these problems of interpretation in detail by analysis of the free energy principle, a current hotly debated mathematical model of the mind that builds on information theory and cybernetics.


  • Dupuy, Jean-Pierre (2000). The Mechanization of the Mind: On the Origins of Cognitive Science. Princeton University Press. Bruineberg, J., Dołęga, K., Dewhurst, J., & Baltieri, M. (2022). The Emperor\’s New Markov Blankets. Behavioral and Brain Sciences, 45, E183. doi:10.1017/S0140525X21002351 Di Paolo, E., Thompson, E., & Beer, R. (2022). Laying down a forking path: Tensions between enaction and the free energy principle. Philosophy and the Mind Sciences, 3. https://doi.org/10.33735/phimisci.2022.9187


Moritz Kriegleder

Moritz Kriegleder, MSc is a Phd Student in the ERC Project Possible Life by Professor Tarja Knuuttila. With a background in both Cognitive Science and Physics he is interested in computational modelling of the mind from a mathematical and philosophical perspective.

Affiliation: University of Vienna
Homepage: twitter: @mokriegleder