Author: Philipp Wicke

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SC5 – Conspiratorial Beliefs & Conspiracy Theories

Lecturer: Krzysztof Dolega
Fields: Epistemology, Social Psychology, Sociology

Content

Do you believe that the moon landing was fabricated by NASA to win the space race? Did the assassination of President John F. Kennedy involve people beyond the lone gunman, Lee Harvey Oswald? After all, the CIA’s MK-Ultra program, which conducted non-consensual mind-control experiments with psychedelic drugs on unsuspecting subjects, stands as a verified instance of a government conspiracy. What makes the other cases different? Turning to more contemporary issues, are you sure that the U.S. government has not purposefully ignored prior knowledge of the events leading up to September 11th to justify subsequent military actions? How much truth is there to the allegations against pharmaceutical companies suggesting intentional suppression of cures for diseases to maintain profit from ongoing treatments? And finally, did you know that the US army conducted a disinformation campaign in the Philippines in order to undermine the public trust in the Chinese-made COVID-19 vaccine?

As these examples illustrate, conspiracy theories and conspiratorial beliefs span a broad spectrum of contents, and one may wonder what combines them as a single object of research. In this course we will investigate the research on and prominent debates around such narratives.

Lecture 1 will focus on the history of the concept and prominent philosophical debates surrounding it.
Lecture 2 will look at the empirical research done on conspiracy theories/beliefs and whether they constitute a separate phenomenon.
Lecture 3 will turn to proposals about how we can respond to and mitigate the spread of such narratives in our society.

Literature

Lecturer

Krzysztof (Krys) Dolega is a philosopher of mind and cognitive science working at the Ruhr-Universität Bochum, where he also did his doctoral research under the supervision of Tobias Schlicht and Daniel Dennett. As a postdoc in Bochum’s Situated Cognition Research Group, he worked on the psychology and epistemology of conspiracy theories on a Volkswagen Foundation grant titled “Why do people believe weird things: Bayesian brain, conspiracy theories, and epistemic vices.” He then moved to the Université Libre de Bruxelles as a postdoc on Axel Cleeremans’ ERC-funded EXPERIENCE project, studying the nature and role of valence, value, and reward in conscious experience.

Affiliation: Ruhr-University Bochum
Homepage: krysdolega.xyz

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ET 2 – To be announced

Lecturer: Arno Villringer
Fields: Brain-Body Interactions, Neurological Disorders, Neurophysiology and Functional Neuroimaging Methods

Content

To be announced

Literature

  • To be announced

Lecturer

Arno Villringer studied medicine at University of Freiburg where he also received a Doctor of Medicine with work in molecular biology. After a fellowship at the Magnetic Resonance Imaging Unit at Massachusetts General Hospital at Harvard Medical School, he was neurology resident at Ludwig-Maximilians-University Munich, became a board-certified neurologist, and gained his habilitation. In the following 14 years, at Charité, Berlin, he headed a DFG-funded clinical research group, received a professorship and worked at the Department of Neurology, first as a consultant, and later as clinical director at the Benjamin Franklin Campus. Since 2007 he is a Director at the Max Planck Institute for Human Cognitive and Brain Sciences and at the Clinic for Cognitive Neurology at the University Hospital in Leipzig. His research focusses on brain-body interactions as well as neural and behavioral processes underlying the development of risk factors for neurological disorders, particularly stroke and dementia. Expertise lies in neurophysiology and functional neuroimaging methods some of which he pioneered (MR-based brain perfusion imaging, functional optical imaging). Clinically, he attempts to individualize preventive and therapeutic approaches for neurological disorders, particularly stroke. He founded the BMBF-funded national Competence Network Stroke, the Bernstein Center for Computational Neuroscience, the Berlin Neuroimaging Center, and was/is PI of two Clusters of Excellence (Neurocure @Charité, LEICEM @University Leipzig), as well as the founder and spokesperson of two graduate schools (Berlin School of Mind and Brain, Germany-wide Max Planck School of Cognition). He was/is also a PI in several DFG-funded integrated research centers on stroke and obesity (IFB), SFB, research groups, and graduate research colleges.

Affiliation: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Homepage: https://www.cbs.mpg.de/employees/villringer.html

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SC8 – Architectures for Trusting, Hopeful, and Responsible Brains

Lecturer: Wulf Haubensak
Fields: Circuit neuroscience, Ethology

Content

Lecture 1: The AIA-BS Circuit for Feelings of Self
To explore the neuronal basis of trust, hope, and responsible action, we first set out to identify a reductionist showcase: the ACC/insula–amygdala–brainstem (AIA-BS) circuit as an evolutionarily conserved substrate for affective feeling. This network demonstrates how the brain may generate subjective experience across species, from mice to humans.
We will explore the operational principles that server as basic building blocks for trust, hope, and responsible action.
Operating in “Read Mode,” the AIA-BS integrates visceral feedback tags stimuli as self-relevant through the amygdala, and generates autonomic signatures via the periaqueductal gray (PAG), producing the embodied sensation of “self and others.”
Lecture 2: Hope as Predict Mode—Inference and Expectation in the AIA-BS
Hope emerges from the AIA-BS circuit operating in “Predict Mode,” in which the ACC/insula simulates future bodily states, the amygdala biases processing toward valued outcomes, and ventral tegmental area (VTA) dopamine neurons fire phasically in response to reward-predictive cues, generating the neural signature of motivated anticipation—that is, hope.
We explore how contingent events build trust and how shifts in violated expectations produce disappointment and broken trust, through prediction-error signaling when anticipated rewards fail to materialize or contracts are breached. The lecture bridges computational frameworks, including predictive coding and active inference, with neurobiological data, showing how dopaminergic signals in the ventral striatum correlate with reward anticipation in mice and humans in translational paradigms.
This mechanistic account reframes hope not as abstract optimism but as a computable brain state grounded in interoceptive prediction and the attribution of agency.
Lecture 3: Empathy as Act Mode—From Shared Feeling to Responsible Action
Empathy emerges when the AIA-BS switches to “Act Mode,” detecting mismatches between another’s suffering and one’s capacity to help, converting this signal into urgency through amygdala activation, and executing prosocial motor programs via outputs from the PAG and nucleus accumbens.
ACC→amygdala→PAG pathways drive observational fear and the social transfer of analgesia in mice, while human fMRI studies confirm homologous ACC–insula–amygdala engagement during empathy for pain and moral decision-making. The lecture emphasizes that responsible action is not a cortical override of instinct but the recruitment of the same ancient care circuitry that generates maternal behavior, now extended to non-kin through cortical expansion and abstract representation.
This framework positions empathy as embodied, action-oriented, and mechanistically tractable rather than purely cognitive or emotional.
Lecture 4: Scaling the AIA-BS—From Individual Circuits to Social Structures
Here, we examine how individual AIA-BS circuits synchronize across brains to support cooperative behavior, while predictive alignment between individuals’ AIA-BS outputs creates the phenomenological experience of trust when others’ actions match our interoceptive expectations.
We contrast rigid, chemically driven hierarchies in mice—where olfactory and vomeronasal inputs rapidly map dominance relationships—with fluid, abstract human social networks enabled by an expanded medial prefrontal cortex, which modulates AIA-BS processing through reputation, norms, institutional cues, and misinformation.
We speculate about comparative genetic and evolutionary mechanisms that may tune AIA-BS–mediated social interactions, like oxytocin’s modulation of amygdala reactivity and insular coupling as a potential molecular basis of interpersonal trust across evolution.

Literature

  • Adolphs, Ralph. “The Social Brain: Neural Basis of Social Knowledge.” Annual Review of Psychology 60, no. 1 (2009): 693–716. https://doi.org/10.1146/annurev.psych.60.110707.163514.
  • Baumgartner, Thomas, Markus Heinrichs, Aline Vonlanthen, Urs Fischbacher, and Ernst Fehr. “Oxytocin Shapes the Neural Circuitry of Trust and Trust Adaptation in Humans.” Neuron 58, no. 4 (2008): 639–50. https://doi.org/10.1016/j.neuron.2008.04.009.
  • Bekoff, Marc. Social Play Behaviour: Cooperation, Fairness, Trust, and the Evolution of Morality. n.d.
  • Canessa, Nicola, Matteo Motterlini, Cinzia Di Dio, et al. “Understanding Others’ Regret: A fMRI Study.” PLoS ONE 4, no. 10 (2009): e7402. https://doi.org/10.1371/journal.pone.0007402.
  • Cohen, Michael S., and Jean Decety. “Social Feedback Mechanisms & Misinformation: A Neuroscience-Based Argument for Algorithm Regulation.” Behavioral Science & Policy 12, no. 1 (2026): 21–29. https://doi.org/10.1177/23794607251403323.
  • Dunbar, R. I. M., and Susanne Shultz. “Evolution in the Social Brain.” Science 317, no. 5843 (2007): 1344–47. https://doi.org/10.1126/science.1145463.
  • European Commission. Joint Research Centre. Trustworthy Public Communication: How Public Communicators Can Strengthen Our Democracies. Publications Office, 2024. https://doi.org/10.2760/695605.
  • Ferretti, Valentina, Federica Maltese, Gabriella Contarini, et al. “Oxytocin Signaling in the Central Amygdala Modulates Emotion Discrimination in Mice.” Current Biology 29, no. 12 (2019): 1938-1953.e6. https://doi.org/10.1016/j.cub.2019.04.070.
  • Jiang, Mengping, Linfan Gu, Mingyi Ma, Qin Li, Jonathan C. Kao, and Weizhe Hong. “Neural Basis of Cooperative Behavior in Biological and Artificial Intelligence Systems.” Science 391, no. 6780 (2026): eadw8151. https://doi.org/10.1126/science.adw8151.
  • Kargl, Dominic, Joanna Kaczanowska, Sophia Ulonska, and Florian Groessl. “The Amygdala Instructs Insular Feedback for Affective Learning.” eLife 2020;9:E60336 9 (2020): 1–36. https://doi.org/10.7554/eLife.60336.
  • Krueger, Frank, Kevin McCabe, Jorge Moll, et al. “Neural Correlates of Trust.” Proceedings of the National Academy of Sciences 104, no. 50 (2007): 20084–89. https://doi.org/10.1073/pnas.0710103104.
  • Nicolle, Antoinette, Dominik R. Bach, Chris Frith, and Raymond J. Dolan. “Amygdala Involvement in Self-Blame Regret.” Social Neuroscience 6, no. 2 (2011): 178–89. https://doi.org/10.1080/17470919.2010.506128.
  • O’Connell, L. a., H. A. a. Hofmann, L. A. O’Connnell, H. A. a. Hofmann, L. a. O’Connell, and H. A. a. Hofmann. “Evolution of a Vertebrate Social Decision-Making Network.” Science 336, no. 6085 (2012): 1154–57. https://doi.org/10.1126/science.1218889.
  • Papaleo, Francesco, Federica Antonelli, Anna Monai, et al. “Emotion Reconfigures Neuronal Coordination across Brains to Control Social Choice.” Preprint, In Review, August 28, 2026. https://doi.org/10.21203/rs.3.rs-9054745/v1.
  • Sladky, Ronald, Federica Riva, Lisa Anna Rosenberger, Jack Van Honk, and Claus Lamm. “Basolateral and Central Amygdala Orchestrate How We Learn Whom to Trust.” Communications Biology 4, no. 1 (2021): 1329. https://doi.org/10.1038/s42003-021-02815-6.
  • Van Der Linden, Sander, and Michael S. Cohen. “The Neuroscience of Misinformation: A Research Agenda.” Neuron 113, no. 14 (2025): 2225–29. https://doi.org/10.1016/j.neuron.2025.05.010.
  • Wu, Ye Emily, and Weizhe Hong. “Neural Basis of Prosocial Behavior.” Trends in Neurosciences 45, no. 10 (2022): 749–62. https://doi.org/10.1016/j.tins.2022.06.008.
  • Yiannakas, Adonis, and Kobi Rosenblum. “The Insula and Taste Learning.” Frontiers in Molecular Neuroscience 10 (November 2017): 335. https://doi.org/10.3389/fnmol.2017.00335.

Lecturer

Wulf Haubensak is Professor and Head of the Department of Neuronal Cell Biology at the Center for Brain Research, Medical University of Vienna, where he leads research in affective neuroscience. He also serves as an Adjunct Investigator at the Institute of Molecular Pathology and is a member of the steering committee of the FWF Cluster for Neuronal Circuit in Health and Disease. His research combines viral genetics, optogenetics, electrophysiology, advanced imaging, fMRI, behavioral neuroscience, and brain data science to investigate how cortico-limbic circuits generate emotional memories and affective responses. His group also studies how genetic and environmental factors shape affective traits, psychiatric conditions, and evolution. He received his diploma in biochemistry from the University of Bochum and his PhD in neurobiology from the University of Heidelberg. Before establishing his research group in Vienna, he worked at the Max Planck Institute for Cell Biology and Genetics and the California Institute of Technology. He received support from HFSP, the Max Planck Society, the Young Academy of the ÖAW, ERC, FWF, and Boehringer Ingelheim.

Affiliation: Medical University of Vienna
Homepage: https://hirnforschung.meduniwien.ac.at/unsere-abteilungen/abteilung-fuerneuronale-zellbiologie

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SC1 – Dark Patterns, Ethical Practice, and Technology Governance

Lecturer: Colin M. Gray
Fields: Human-Computer Interaction; Design Ethics; Technology Law and Policy; AI Governance

Content

Digital systems increasingly shape what people notice, disclose, buy, believe, and do. In this course, we ask when influence through design becomes deceptive or manipulative (often described as “dark patterns”), how these practices operate across interfaces and organizations, and how designers, researchers, organizations, and regulators can respond. Using examples from commerce, privacy, social media, and AI-enabled systems, participants will connect HCI and design theory with empirical evidence, professional ethics standards, and legal and regulatory approaches. Across four sessions, they will develop a shared conceptual vocabulary, examine how harms emerge and can be evidenced, explore how legal knowledge can become material for design, and consider practical routes toward more responsible and trustworthy technologies. No prior background in HCI or law is required. The course combines short lectures, discussion, interface analysis, and collaborative exercises.

Session 1 introduces dark patterns through HCI and design theory, asking what makes a design outcome normatively troubling or unlawful.
Session 2 examines how dark patterns operate across interfaces and organizations, how their harms can be evidenced, and who bears responsibility.
Session 3 explores how legal and regulatory knowledge can become practical material for design.
Session 4 considers how critique can be translated into more responsible design and organizational action.

Literature

  • Bringing Dark Patterns to Light Staff Report. (2022). Federal Trade Commission. https://www.ftc.gov/reports/bringing-dark-patterns-light
  • Gray, C. M. (2026, April). The Dark Patterns Knowledge Stack: Exploring New Ways to Negotiate Context, Law, and Design. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). https://doi.org/10.1145/3772318.3791264
  • Gray, C. M., & Chivukula, S. S. (2019). Ethical Mediation in UX Practice. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–11. https://doi.org/10.1145/3290605.3300408
  • Gray, C. M., Mildner, T., & Gairola, R. (2025, April). Getting Trapped in Amazon’s “Iliad Flow”: A Foundation for the Temporal Analysis of Dark Patterns. CHI ’25: CHI Conference on Human Factors in Computing Systems Proceedings. https://doi.org/10.1145/3706598.3713828
  • Gray, C. M., Santos, C. T., Bielova, N., & Mildner, T. (2024). An Ontology of Dark Patterns Knowledge: Foundations, Definitions, and a Pathway for Shared Knowledge-Building. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–22. https://doi.org/10.1145/3613904.3642436
  • Gunawan, J., Gray, C. M., Santos, C., & Bielova, N. (2025). Leveraging interdisciplinary methods for evidence collection in enforcement: Dark patterns as a case study. Internet Policy Review, 14(4). https://doi.org/10.14763/2025.4.2047
  • Mathur, A., Kshirsagar, M., & Mayer, J. (2021). What Makes a Dark Pattern… Dark? Design Attributes, Normative Considerations, and Measurement Methods. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–18. https://doi.org/10.1145/3411764.3445610
  • Monge Roffarello, A., Lukoff, K., & De Russis, L. (2023, April). Defining and identifying attention capture deceptive designs in digital interfaces. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. CHI ’23: CHI Conference on Human Factors in Computing Systems, Hamburg Germany. https://doi.org/10.1145/3544548.3580729
  • Wong, R. Y. (2021). Tactics of Soft Resistance in User Experience Professionals’ Values Work. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), Article 355. https://doi.org/10.1145/3479499

Lecturer

Colin M. Gray is a Professor of Informatics in the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington, where they are Director of the Human-Computer Interaction design (HCI/d) program. They hold an appointment as Visiting Researcher at Northumbria University and have previously held appointments as Guest Professor at Beijing Normal University and Visiting Researcher at Newcastle University. Colin holds a PhD in Instructional Systems Technology from Indiana University Bloomington, a MEd in Educational Technology from University of South Carolina, and a MA in Graphic Design from Savannah College of Art & Design. They have worked as an art director, contract designer, and trainer, and their involvement in design work informs their research on design activity and how design capability is learned. Colin’s research focuses on the ways in which the pedagogy and practice of designers informs the development of design ability, particularly in relation to ethics, design knowledge, and dark patterns. They have consulted on multiple legal cases relating to dark patterns and data protection and work with regulatory bodies and non-profit organizations to increase awareness and action relating to deceptive and manipulative design practices. Colin’s research and engagement activities cross multiple disciplines, including human-computer interaction, instructional design and technology, law and policy, design theory and education, and engineering and technology education. Colin is proudly gay/queer and non-binary and uses they/them pronouns.

Affiliation: Indiana University Bloomington
Homepage: https://colingray.me

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PC4 – The Grandmaster’s Mind Reason, Intuition and Responsible Decision-Making in the Age of AI

Lecturer: Stefan Kindermann
Fields: Decision-Making, Cognitive Science, Artificial Intelligence, Chess, Strategy

Content

How do we make sound decisions when we cannot calculate every possibility? Chess grandmasters constantly face this challenge: they must combine rational analysis with intuition, recognize meaningful patterns in highly complex situations, distinguish essential information from distracting detail, and act under uncertainty and time pressure.
Drawing on Stefan Kindermann’s experience as a chess grandmaster, author and strategic adviser, this course explores what the thinking of chess experts can teach us about planning and decision-making in professional, academic and personal life. Particular attention will be given to the true role of intuition: how it develops, when it can be trusted, where it is prone to systematic error, and how intuition and conscious reasoning can be combined.
The course will introduce the “Königsplan” (“King’s Plan”), a practical model for holistic strategic thinking derived from the decision-making processes of chess grandmasters. Participants will apply these ideas in an interactive chess session. No previous chess knowledge is required: carefully selected positions and short games will make cognitive processes such as pattern recognition, perspective-taking, evaluation, prioritization and decision-making under uncertainty directly observable.
Finally, chess will serve as a model and testing ground for artificial intelligence. Based on more than three decades of experience with increasingly superhuman chess programs, the course will examine how the relationship between humans and intelligent machines has evolved—from competition and defeat to cooperation and new forms of human–AI decision-making. This provides a concrete foundation for discussing trust in AI, the limits of machine judgement and the continuing importance of human responsibility.


Course Sessions
Session 1 The True Role of Intuition in Decision-Making
What do we mean by intuition, and how does it arise? This session explores how chess grandmasters use pattern recognition and unconscious evaluation to navigate complex situations. We will examine when intuition deserves our trust, how it can be developed, and how experts communicate insights that they may initially be unable to explain.
Session 2 Combining Intuition and Reason: The “Königsplan”
Intuition alone is fallible, while rational analysis alone often becomes overwhelmed by complexity. This session examines typical intuitive errors and cognitive biases and introduces the “Königsplan,” a practical model for integrating analysis, intuition, changes of perspective and strategic orientation. Participants will apply the model to decisions beyond chess.
Session 3 Chess Thinking in Action: An Interactive Workshop
Participants will play and analyse selected chess positions together with a grandmaster. The emphasis is not on chess strength or theoretical knowledge, but on observing one’s own thinking in action: identifying priorities, generating alternatives, questioning first impressions, evaluating risks and making decisions under limited time. The experiences at the chessboard will then be transferred to real-life planning and decision-making.
Session 4 Chess as a Model and Testing Ground for Artificial Intelligence
For decades, chess has been one of the most important laboratories for artificial intelligence. Drawing on personal experience with several generations of chess computers, this session traces the development from human–machine competition to cooperation with superhuman systems. It asks what chess can teach us about trusting AI, retaining meaningful human agency and making responsible decisions in a world increasingly shaped by intelligent machines.

Literature

  • Der Königsplan – Strategien für Ihren Erfolg Kindermann/von Weizsäcker Rowohlt

Lecturer

Intuition trainer and keynote speaker Stefan Kindermann is an international chess grandmaster and co-founder of the Munich Chess Academy and the Munich Chess Foundation . He has been a member of Rotary International Munich since 2012. He is the author of several specialist books, a columnist for the Süddeutsche Zeitung, an NLP Master Practitioner, a keynote speaker, a trainer, and a coach. Together with Prof. Robert von Weizsäcker and Dijana Dengler, he developed the Königsplan strategy model and teaches this concept through lectures, coaching sessions, and seminars. During his time as a professional chess player, he participated in eight Chess Olympiads and one World Championship, and with Bayern Munich he won the German Team Championship nine times as well as the European Cup. Stefan Kindermann currently plays in the first Bundesliga for MSA Zugzwang, the team of the Munich Chess Academy.

Affiliation: Münchener Schachakademie GmbH und Münchener Schachstiftung
Homepage: www.koenigsplan.com

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SC7 – Counting on Words

Lecturer: Robert Porzel, Laura Spillner
Fields: Computational Linguistics

Content

1. Session 1 will introduce the branches of natural language processing pertinent to the rise of LLMS
2. Session 2 will introduce early language models and what they were used for
3. Session 3 will introduce common tasks, such as classification, that are handled by ML approaches
4. Session 4 will introduce embeddings and the types of neural networks that paved the way for LLMS

Literature

  • Daniel Jurafsky and James H. Martin. 2026. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3rd edition. Online manuscript released August 19, 2026.
  • https://web.stanford.edu/~jurafsky/slp3.

Lecturer

Throughout his studies Robert Porzel earned degrees in general linguistics, computational linguistics and computer science. Previous to his current work he worked in the interdisciplinary collaborative research center “Language and Situation” (SFB 245) and the special research program “Language Production” (SPP 1022) funded by the German Research Foundation (DFG). Thereafter, he worked at the European Media Laboratory (EML) on several natural language processing projects, i.e. DeepMap, Embassi, SmartKom and SmartWeb, which were funded by the Klaus Tschira Foundation (KTS) and the German Ministry for Education and Research (BmBF). In between he was a guest researcher at the University of California, Berkeley working on a formalization of embodied construction grammar in the EDU project. After moving to the University of Bremen, Robert Porzel started work on contextual computing for autonomous collaborative systems in the DFG-funded special research program “Autonomous Logistic Processes” (SFB 637). He initiated and chaired the biannual ScaNaLU workshop series on scalable natural language understanding systems (2002, 2004 and 2006) and was a member of the steering board of the Interdisciplinary College (IK) – an international spring school on cognitive science, neurosciences and AI – which he chaired in 2010 together with Natalie Sebanz and Manfred Spitzer. His current position is that of “Reader in Digital Media” at the University of Bremen and he researches in the collaborative research center “Everyday Activity Science and Engineering” (SFB 1320) on knowledge models for cognitive robotics.

Affiliation: University of Bremen
Homepage: https://www.uni-bremen.de/dmlab/team/dr-ing-robert-porzel

Laura Spillner is a doctoral researcher at the Digital Media Lab. She studied media science and digital media and then completed a master’s degree in computer science at the University of Bremen, with a focus on artificial intelligence, cognition and robotics. Her master’s thesis focused on natural language processing and conversational agents. Laura is working in the European Project “MUHAI: Meaning and understanding in Human-centered artificial intelligence”. In this project and in her PhD thesis she is researching natural language understanding, and the development of explainable algorithms AI and hybrid AI in the field of language processing.

Affiliation: Universität Bremen
Homepage: https://www.uni-bremen.de/en/dmlab/team/laura-spillner

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PC2 – Embodied Resilience: Finding Ground for Action in Uncertain Times

Lecturer: Asena Boyadzhieva
Fields: Cognitive Science, Embodied Cognition, Neuroscience, Psychology, Systems Thinking, Social Cognition

Content

How do we remain responsive and capable of acting when the future is uncertain?

This practical course explores resilience as an embodied capacity: the ability to stay present, regulate, connect and act in conditions of complexity and uncertainty. Drawing on embodied cognition, interoception, relational practices and systems thinking, the course investigates how processes that are often studied separately — individual regulation, interpersonal trust, collective dynamics and responsible action — are connected through the living body.

Rather than treating embodiment as an alternative to cognitive or analytical approaches, we use embodied practice as a form of experimental inquiry. Through movement, attention, sensory awareness, relational exercises and reflection, participants will investigate how bodily states shape perception, interaction and the capacity to respond.

The four sessions move from the individual to the collective and from present experience towards future action:
1. Trusting the Self
We begin with interoception, sensory awareness and grounding. Participants explore how attention to bodily states can support orientation and flexibility when certainty is unavailable, and investigate the relationship between regulation, perception and agency.
2. Trusting Others
Trust is explored as an emergent relational process rather than only a cognitive judgement. Through paired and small-group experiments involving movement, boundaries, synchronisation and responsiveness, participants investigate how trust and co-regulation develop between people.
3. Trusting the System – Interbeing
We expand from interpersonal interaction towards collective dynamics. Group-based practices provide an experiential exploration of feedback, synchronisation, differentiation and interdependence, connecting processes within nervous systems to patterns that emerge between people and within larger systems.
4. Trusting the Future
The final session turns towards the future. Building on the previous sessions, participants explore hope not as optimism or certainty, but as a capacity to remain engaged and act under conditions of uncertainty. The course concludes by connecting embodied awareness with agency, responsibility and possible forms of action.

No previous experience with movement, meditation or somatic practices is required. The course is designed as an experiential laboratory in which participants can investigate their own embodied experience while connecting it to questions from cognitive science, social cognition and systems thinking.

Literature

  • Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.
  • Macy, J., & Johnstone, C. (2022). Active Hope: How to Face the Mess We’re in with Unexpected Resilience and Creative Power. New World Library.
  • Scharmer, C. O., & Kaufer, K. (2025). Presencing: 7 Practices for Transforming Self, Society, and Business. Berrett-Koehler Publishers.
  • Stroh, D. P. (2015). Systems Thinking for Social Change: A Practical Guide to Solving Complex Problems, Avoiding Unintended Consequences, and Achieving Lasting Results. Chelsea Green Publishing.

Lecturer

Asena Boyadzhieva holds a Master’s degree in Cognitive Science from the University of Vienna and a Bachelor’s degree in Biotechnology from IMC Krems. Her path has since moved from studying living systems in the laboratory to exploring them through the lived body. She is a certified social pedagogue, holistic dance facilitator and yoga teacher, and works at the intersection of embodiment, education, and art. Through The HeArt Center and coMetta, she creates experiential spaces where scientific and embodied ways of knowing can meet. Her work is rooted in the curiosity of what happens when we move beyond understanding the body as an object of study and begin to experience it as an active participant in perception, relationship and action. She is particularly interested in embodied resilience, collective processes and how we can cultivate the capacity to remain connected and responsive in times of complexity and uncertainty.

Affiliation: coMetta – Verein für ganzheitliches Lernen und nachhaltige innere Entwicklung
Homepage: theheartcenter.at

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SC10 – Fasten your brain belts! – an introduction to neuromorphic computing

Lecturer: Herbert Jaeger
Fields: Physics, neuroscience, AI, computer science, cognitive science, robotics, systems and control, dynamical systems, philosophy, microchip engineering, and more

Content

The current AI revolution is based on gigantic artificial neural networks that suck up gigantic supplies of data and electrical energy. Biological brains are also gigantic neural networks, but they digest only the little information that a single little life delivers, at 20 Watts peak power. Still, with your brain you can do all the things that you can do, – and that no AI robot yet can do – bake cakes and dance and have fun with your friends and ALL THE REST – think about ALL the things you have done in your life. This setting may explain why currently we see a surge of interest (and funding) in unconventional ‘brain-like’ computing technologies. The current buzzword is ‘neuromorphic computing’ (NC). This course introduces you to this field and its fundamental riddles.

Connection to the IK 2027 focus theme: if (big IF) a really, really brain-like computer would one day be built: would it be responsible for you to switch it on? and after you had switched it on, could you trust that this ‘computer’ acts responsibly?

Session 1: When you are thinking of ‘computing’, does your brain ‘compute’? There are two answers to this question, one of which we do understand. The other is the heartbeat of NC research.

Session 2: A bold but helpless attempt to survey the zillion facets and historical sources of NC, plus flashlights on the state of the art.

Sessions 3: All these challenges! technical, physical, mathematical, practical, educational, ethical, philosophical – almost everything-al. Some might be unsolvable.

Session 4: Speciality of the house: formal semantics for NC. What do we mean when we say a brain computes, and what does the brain mean when it does its thing?

(coverage subject to spontaneous change)

Literature

  • There are many introductions and surveys, but they are all written from some specific angle. Authors are human (or used to be), and a human being in his/her lifetime can only first see, then survey what fits in their lifespan. But the wider NC fields are so outrageously multi-multi-disciplinary that single authors and, in fact, entire author collectives cannot come anywhere close to a complete overview, even when they try. Two such tries are
  • Finocchio, G., Incorvia, J. A. C., … & Bandyopadhyay, S. [altogether > 50 authors] (2024). Roadmap for unconventional computing with nanotechnology. Nano Futures, 8(1), 012001. Open access at https://iopscience.iop.org/article/10.1088/2399-1984/ad299a (written from a non-digital hardware angle)
  • Jaeger, H. (2021). Towards a generalized theory comprising digital, neuromorphic and unconventional computing. Neuromorphic Computing and Engineering, 1(1), 012002. Open access at https://iopscience.iop.org/article/10.1088/2634-4386/abf151 (written from a formal theory-building angle)

Lecturer

Herbert Jaeger studied mathematics and psychology in Freiburg (Germany), got his PhD Computer Science / AI in Bielefeld (Germany) and then did a postdoc at the (then) German National Research Institute for Mathematics and Computer Science (GMD) in Sankt Augustin (Germany), where he subsequently founded the research unit ‘Modeling Intelligent Dynamical Systems’ (MINDS); then from 2001 to 2019 he served as professor in the CS department of Jacobs University Bremen (Germany). Since 2019 he has been Professor for Computing in Cognitive Materials at the University of Groningen. Current research focus: mathematical foundations for a theory of computing on the basis of non-digital physical substrates. Jaeger retired in June 2025 and now has almost enough time for tackling the math-of-complex-systems riddles that got him hooked since student times.

Affiliation: University of Groningen
Homepage: https://www.ai.rug.nl/minds/

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BC3 – Introduction to Machine Learning

Lecturer: Benjamin Paaßen
Fields: Machine Learning

Content

Machine learning is concerned with automatically learning models (patterns, regularities, correlations) from training data such that these models generalize to new data. To do so, machine learning combines concepts from mathematics (esp. statistics, probability theory, linear algebra, and optimization), artificial intelligence, and computer science. This course will provide an introduction to machine learning for the un-initiated. Students will need to suffer through some math, but hopefully my enthusiasm will convey the beauty behind it 🙂 And I will employ ample examples and pictures.

In more detail, the course will have four sessions with the following topics:

1. Basic Concepts: Functions, learning algorithms, optimization, linear regression (as an example of a learning algorithm), regularization, probability theory, machine learning theory, how to design a ML experiment, how to read an ML paper
2. Classic machine learning tasks and methods to solve them: The distance perspective on ML, Regression, Classification, Dimensionality Reduction, Clustering
3. Artificial neural networks and deep learning: Neural network modules, recipes for neural networks, including current LLMs, adversarial attacks
4. Reinforcement learning and fairness

Each session is accompanied by a (voluntary) programming exercise in Python. Exercise sheets (and slides) can be found here: https://bpaassen.gitlab.io/Teaching.html

Literature

Lecturer

Benjamin Paaßen is Junior Professor for Knowledge Representation and Machine Learning at Bielefeld University, Germany. Their focus is on domain-informed, interpretable, and explainable machine learning, with a particular focus on machine learning for education. They are also affiliate researcher at the Educational Technology Lab of the German Research Center for Artificial Intelligence (DFKI), member of the Young College of the Northrhine-Westphalian Academy of Sciences and Arts, and Junior Fellow of the German Computer Science Society.

Affiliation: Bielefeld University
Homepage: https://bpaassen.gitlab.io/

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MC4 – Defence Against the Dark Arts: Responsible Design as a Counterspell to Deceptive Design

Lecturer: Thomas Eßmeyer
Fields: Human Computer Interaction, Psychology

Content

The design of online content is increasingly governing and disrupting our choices, while the truthfulness of content becomes harder to assess. A root lies in the design of user interfaces, which are often driven by commercial incentives conflicting with users’ agency and their best interests. In a time of generative AI and LLMs, where the design process is frequently offloaded to automated systems, it is all the more important to understand the ethical caveats of modern technologies before they leave our labs. This course will discuss how deceptive design and dark patterns shape our behaviour and expectations, leading to consequences from frustrations to actual harm. We will catch up with the current state of the art behind this research domain, take an excursion to regulatory protective measures, and discuss paths forward to develop human-centred technologies. Below is a preliminary structure for this course, which will be accompanied with interactive elements. This structure might be subject to slight changes:

Session 1 introduces the concept of dark patterns in the context of human-centred design.
Session 2 discusses the cognitive mechanisms and biases at play and risks through generative AI and LLMs.
Session 3 addresses both legal and organisational responsibilities when people are harmed.
Session 4 covers ethical caveats for the development of user interfaces and what we can do better.

Literature

  • Colin M. Gray, Cristiana Teixeira Santos, Nataliia Bielova, and Thomas Mildner. 2024. An Ontology of Dark Patterns Knowledge: Foundations, Definitions, and a Pathway for Shared Knowledge-Building. In Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–22. https://doi.org/10.1145/3613904.3642436
  • Ana Caraban, Evangelos Karapanos, Daniel Gonçalves, and Pedro Campos. 2019. 23 Ways to Nudge: A Review of Technology-Mediated Nudging in Human-Computer Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–15. https://doi.org/10.1145/3290605.3300733
  • Richard H. Thaler and Cass R. Sunstein. 2008. Nudge: improving decisions about health, wealth, and happiness. Yale University Press, New Haven.
  • Arunesh Mathur, Jonathan Mayer, and Mihir Kshirsagar. 2021. What Makes a Dark Pattern … Dark ? Design Attributes, Normative Considerations, and Measurement Methods. In CHI’21, 18. https://doi.org/10.1145/3411764.3445610

Lecturer

After successfully completing a BA in Digital Media and an MSc in Computer Science, Dr Thomas Eßmeyer (né Mildner) received a PhD at the University of Bremen. In his work, Thomas focuses on user wellbeing and countermeasures to unfair and deceptive design practices, often referred to as Dark Patterns.

Affiliation: University of Bremen
Homepage: https://thomasessmeyer.com/