Author: Philipp Wicke

Lecturer: Franjo Weissing
Fields: Ecology & Evolution, Behavioural Biology

Content

Biological organisms have to cope with ever-changing environmental conditions. They have been ‘designed’ for this task in a long evolutionary history, but how evolution by natural selection has achieved this is far from clear. Two properties are crucial for long-term survival in a changing world: ‘robustness’ (the ability to build the same phenotype under very different conditions) and ‘evolvability’ (the ability to rapidly respond to changing conditions by adaptive evolution). The conundrum is that these properties seem to be contradictory: doesn’t a robust design impede evolvability, and doesn’t evolvability require a flexible design? A second problem is that ‘evolutionary design’ is fundamentally different from the ‘engineered design’. While an engineer has foresight, adaptive evolution resembles a ‘blind watchmaker’ (Dawkins 1986) in that it is driven by short-term selection pressures. We all know that following short-term incentives often has negative implications in the longer term. How, then, can long-term properties like robustness and evolvability be shaped by a myopic process like natural selection?
Questions like these will be addressed in four sessions. The first two sessions will illustrate the dynamic complexity of apparently simple ecological and evolutionary systems. We will see that such systems can be ‘fundamentally unpredictable’ and that adaptive evolution can, in principle, drive a population to extinction (‘evolutionary suicide’). In the last two sessions, we will sketch a new way of evolutionary thinking that may (partly) resolve issues like these. We will see that the evolution of ‘responsive strategies’ (strategies that respond to the local environmental conditions) is fundamentally different from the evolution of non-responsive strategies. The reciprocal causality inherent to these strategies speeds up evolution by orders of magnitude and leads to quite different evolutionary outcomes. In biological organisms, responsive strategies are often implemented via regulatory networks (e.g., gene regulation networks or neural networks). It turns out that the evolution of such networks shares various properties with learning (by machines or intelligent agents).

Literature

• Session 1: Out of equilibrium
  • Huisman, J. & Weissing, F.J. 1999. Biodiversity of plankton by species oscillations and chaos. Nature 402: 407-410, doi: 10.1038/46540.
  • Huisman, J. & Weissing, F.J. 2001. Fundamental unpredictability in multispecies competition. American Naturalist 157: 488-494, doi: 10.1086/319929.
• Session 2: Conflict and cooperation
  • Baldauf, S.A., Engqvist, L. & Weissing, F.J. 2014. Diversifying evolution of competitiveness. Nature Communications 5: 5233, doi: 10.1038/ncomms6233.
  • Long, X. & Weissing, F.J. 2023. Transient polymorphisms in parental care strategies drive divergence of sex roles. Nature Communications 14: 6805, doi: 10.1038/s41467-023-42607-6.
• Session 3: The reciprocal causality of responsive strategies
  • Quiñones, A.E., Van Doorn, G.S., Pen, I., Weissing, F.J. & Taborsky, M. 2016. Negotiation and appeasement can be more effective drivers of sociality than kin selection. Phil. Trans. R. Soc. B 371:20150089, doi:10.1098/rstb.2015.0089.
  • Netz, C., Hildenbrandt, H. & Weissing, F.J. 2022. Complex eco-evolutionary dynamics induced by the coevolution of predator-prey movement strategies. Evol. Ecol. 36: 1-17, doi: 10.1007/s10682-021-10140-x.
  • Gupte, P.R., Albery, G.F., Gismann, J., Sweeny, A.R. & Weissing, F.J. 2023. Novel pathogen introduction triggers rapid evolution in animal social movement strategies. eLife, 12: e81805, doi: 10.7554/eLife.81805.
• Session 4: Robustness and evolvability
  • Wagner, A. 2008. Robustness and evolvability: a paradox resolved. Proc. Royal Society B 275: 91-100, doi: 10.1098.rspb.2007.1137.
  • Watson, R.A. & Szathmáry, E. 2016. How can evolution learn? Trends in Ecology & Evolution 31: 147-157, doi: 10.1016/j.tree.2015.11.009
  • Van Gestel, J. & Weissing, F.J. 2016. Regulatory mechanisms link phenotypic plasticity to evolvability. Scientific Reports 6:24524, doi: 10.1038/srep24524.

Lecturer

After studying mathematics and biology at the University of Bielefeld, I did my PhD work at the Centre for Interdisciplinary Studies (ZiF Bielefeld), where I co-organised the research year ‘Game Theory in the Behavioural Sciences’. Together with the later Nobel laureate Elinor Ostrom, I pioneered the introduction of ‘evolutionary thinking’ into the political sciences. In 1989, I moved to the University of Groningen (Netherlands), where I tackled a wide variety of eco-evolutionary questions with a combined theoretical and empirical approach. My area of expertise lies in the development and analysis of mathematical and computational models. Our emphasis is on ‘mechanistic models of intermediate complexity’ that lead to insights and predictions that can be tested in close collaboration with empirical biologists. In my research, I strive to understand the emergence of diversity at all levels of biological organisation (e.g., differences between cells, individuals, the sexes, groups, species, and ecosystems) and the implications of diversity for the evolution and functioning of biological systems. In the last ten years, I have broadened my research again to other disciplines. As a Distinguished Lorentz Fellow, I spent a research year at the Institute of Advanced Study in the Humanities and Social Sciences in Amsterdam, where I critically investigated the foundations of cultural evolution theory. I became convinced that new approaches are needed that do justice to the differences between genetic and cultural evolution. To this end, we are currently working on a new framework for the evolution of individual and social learning, which is based on the evolution of neural networks.

Affiliation: University of Groningen
Homepage: https://research.rug.nl/en/organisations/weissing-group-theoretical-biology

Lecturer: Giulia Belgiovine
Fields: Artificial Intelligence, Human-Robot Interaction

Content

The study of human-robot interaction is a vast and intricate field that adopts a multidisciplinary approach and encompasses numerous challenges. On the one hand, the quest to comprehend and model the intricate mechanisms underlying human cognitive and social abilities; on the other hand, the problem of how to replicate a comparable level of intelligence in cognitive interactive agents. Achieving this necessitates the integration of sensory and motor capabilities, along with memory, reasoning, and learning mechanisms, to develop artificial agents endowed with adaptation and generalization skills.
In these lectures, we will explore the interdisciplinary nature of this field, discussing the challenges and opportunities that lie ahead.

Lecturer

Giulia Belgiovine is a postdoctoral researcher at the COgNiTive Architectures for Collaborative Technologies (CONTACT) unit of the Italian Institute of Technology, Genoa, Italy. Her research investigates how to develop cognitive architectures for social robots to promote better human-robot interactions and how to foster robots’ autonomous learning and adaptive behavior. Her research interests include multiparty interactions, assistive robotics, and lifelong learning.

Affiliation: Italian Institute of Technology

Lecturer: Martin Tik, Anna-Lisa Schuler
Fields: Cognitive Neuroscience, Clinical Neuroscience, Neuroimaging

Content

In this course we will discuss basics, applications and hot topics for two of the most popular magnets in brain research: TMS and MRI. While fMRI allows for the depiction of neural underpinning underlying task processing, TMS as a neuromodulation technique allows for the targeted manipulation of these processes. This course will be dedicated to give an overview about both of the techniques and the advantages of their combination.

Session 1: In session one we will give an overview about the history of magnets in cognitive neuroscience and the evolution of the methods of interest (TMS, fMRI). We will furthermore explain the technical basics and the composition of these devices.

Session 2: In session two we will discuss the physiological mechanisms of action underlying the techniques including blood oxygenation and neuronal action potentials. Then we will give an overview about different applications of TMS and fMRI including exemplary research.

Session 3: In this session we will discuss specific applications of combining TMS with fMRI in cognitive neuroscience and clinical medicine. In the second part of this session, participants will have the opportunity to plan their hypothetical own TMS and fMRI experiments in small groups.

Session 4: Participants will discuss their projects in a plenum. Finally, there will be a summary, question round and wrap up of the course.

Learning goals:

– Basic principles of TMS and fMRI
– Applications of TMS and fMRI
– Direct transfer of these contents to own research

Literature

• Chen, J. E., & Glover, G. H. (2015). Functional magnetic resonance imaging methods. Neuropsychology review, 25, 289-313.
• Pitcher, D., Parkin, B., & Walsh, V. (2021). Transcranial magnetic stimulation and the understanding of behavior. Annual Review of Psychology, 72, 97-121.
• Burke, M. J., Fried, P. J., & Pascual-Leone, A. (2019). Transcranial magnetic stimulation: Neurophysiological and clinical applications. Handbook of clinical neurology, 163, 73-92.

Lecturer

Martin Tik is a Group Leader at the Medical University of Vienna. His main research interests lie in advancing the method of interleaving TMS with fMRI and the improvement of depression treatment using the combination of these methods.

Affiliation: Medical University of Vienna
Homepage: https://www.martintik.at/

Anna-Lisa Schuler is a Post-Doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences. She is mainly interested in the combination of TMS with fMRI in cognitive and clinical neuroscience including language processing and plasticity in healthy and neuropsychiatric populations.

Affiliation: Max Planck Institute for Human Cognitive and Brain Sciences
Homepage: https://twitter.com/AnnaLisaSchule1

Lecturer: Erika Covi
Fields: Emerging memory devices, Neuromorphic computing

Content

In recent years, the cloud-based approach to data classification has been challenged by the edge computing paradigm, which has enabled real-time data processing at the network edge, ideally next to the sensor collecting data. This paradigm poses severe constraints on the systems in terms of power-efficiency, compactness, and latency [1, 2]. Therefore, we need to explore unconventional hardware solutions able to meet these stringent requirements.
Brain-inspired architectures, particularly Spiking Neural Networks (SNNs), are promising candidates to achieve low-latency computation, and stateful, energy-efficient operations [3]. However, their current implementations primarily rely on digital or mixed-signal Complementary Metal-Oxide-Semiconductor (CMOS) technologies, which pose challenges in meeting the demanding memory, area, and power constraints of computing on the edge [1].
In this context, the integration of embedded memristive technologies holds a significant promise to enhance the capabilities of CMOS technology [2, 4] for the development of neuromorphic hardware [5]. Memristive devices are nanoscale devices able to change their conductivity upon application of proper electrical stimuli. They offer fast and energy-efficient tuneable volatile and non-volatile storage, and are therefore well-suited for storing SNN parameters. The exploitation of their unique properties, such as operation voltages compatible with current CMOS technology as well as analogue, neural-/synaptic-like behaviour, offer an attractive opportunity for realizing energy-efficient and massively parallel computing architectures in conjunction with CMOS technology [3, 5]. Indeed, these features enable efficient computation, neural dynamics, and synaptic plasticity, which are essential traits for emulating the brain’s functionality in hardware [4, 6].
In this course, we explore the role of memristive devices in emulating the functionality of neural networks, enabling edge systems to classify and recognise patterns or process sensory inputs. We emphasize the need for co-developing memristive devices with CMOS circuits to enable seamless integration and to exploit the strengths of both technologies. Furthermore, we discuss how the co-development of memristor devices, CMOS circuits, and innovative learning algorithms can facilitate edge computing paradigms. Moreover, the intrinsic physical characteristics of memristive devices, if correctly exploited, enable analogue computing, thus offering a compelling alternative to traditional digital approaches. We also discuss the challenges and opportunities of developing memristive-CMOS hardware neuromorphic architectures. The co-design of devices, circuits, and algorithms indeed requires to identify and address issues related to device variability, scalability, and system integration.
In conclusion, the synergistic co-development of memristive devices, CMOS circuits, and innovative algorithms can pave the way for intelligent edge devices capable of performing complex cognitive tasks.

Literature

• [1] E. Covi et al. Front. in Neurosci., 15, 611300 (2021).
• [2] D. V. Christensen et al. Neuromorph. Comput. Eng., 2, 022501 (2022).
• [3] E. Chicca et al. Proc. of the IEEE, 102, pp. 1367-1388 (2014).
• [4] D. Ielmini and S. Ambrogio, Nanotech., 31, 092001 (2019).
• [5] A. Amirsoleimani et al. Adv. Intell. Sys., 2, 2000115 (2020).
• [6] E. Covi et al. Neuromorph. Comput. Eng., 2, 012002 (2022).

Lecturer

Dr. Erika Covi is Assistant Professor at the Zernike Institute for Advanced Materials & Groningen Cognitive Systems and Materials Center (Groningen, the Netherlands). She received her PhD in Microelectronics in 2014 from the University of Pavia (Italy), where she worked on designing integrated systems for the characterisation of memristive devices. She also worked at the National Research Council (CNR) of Italy, at Politecnico di Milano (Italy), and at NaMLab gGmbH (Dresden, Germany). Her research interests lie at the intersection of emerging devices, circuit design, and brain-inspired computing. More specifically, they focus on exploiting the intrinsic physical characteristics of memristive devices to reproduce computational primitives of the brain in mixed neuromorphic-memristive systems.

Affiliation: University of Groningen

Lecturer: Tarek R. Besold
Fields: Artificial Intelligence, Machine Learning, AI Governance

Content

In this course we will (try to) assess the current state of play as regards responsible AI development from both a theoretical/principled (i.e., what are – or can be – the foundations of responsible AI systems and development practices?) and an applied (i.e., which part(s) of the theory carry over into practice and what are the challenges/breaking points?) perspective.

We will have a look at (some of) the conceptual underpinnings of responsible tech development, at the particularities which AI as a domain adds to those, and at proposals for how to bring the chosen values and guidelines into application. We will also have a look at the pressures AI developers face in practice, what these mean for the implementation of responsible development approaches, and if there are (currently) hard-to-overcome breaking points between theory and application. Finally, we will map some of the governance and regulatory infrastructure and processes governments are now working to put into place in order to guarantee adherence to a minimal standard of good practices in the development of AI systems.

Literature

• High Level Expert Group on Artificial Intelligence. “Ethics Guidelines for Trustworthy AI”. 2019. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.
• Mitchell, Margaret, et al. “Model cards for model reporting”. Proceedings of the conference on fairness, accountability, and transparency. 2019. https://arxiv.org/abs/1810.03993.
• Thong, William, Przemyslaw Joniak, and Alice Xiang. “Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color”. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023. https://openaccess.thecvf.com/content/ICCV2023/papers/Thong_Beyond_Skin_Tone_A_Multidimensional_Measure_of_Apparent_Skin_Color_ICCV_2023_paper.pdf.
• Chatila, Raja, et al. “Trustworthy AI”. Reflections on Artificial Intelligence for Humanity (2021): 13-39. https://link.springer.com/chapter/10.1007/978-3-030-69128-8_2
• Mittelstadt, Brent, Sandra Wachter, and Chris Russell. “The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default”. arXiv preprint arXiv:2302.02404 (2023). https://arxiv.org/abs/2302.02404.
• Mittelstadt, Brent. “Principles alone cannot guarantee ethical AI”. Nature machine intelligence 1.11 (2019): 501-507. https://arxiv.org/abs/1906.06668.

Lecturer

Tarek R. Besold is a Senior Research Scientist at Sony AI in Barcelona, as well as an affiliated researcher with the Philosophy & Ethics group at Eindhoven University of Technology. His work covers topics at the intersection between AI, cognitive science, and real-world applications. He also serves as a start-up advisor and consulting AI policy expert, and was the founding chairman of the German DIN/DKE Standards Working Committee on AI NA 043-01-42 GA.

Affiliation: Sony AI Barcelona
Homepage: https://ai.sony/

Lecturer: Jürgen Gadau
Fields: Sociobiology, Genomics and Epigenetics of adpative traits

Content

I plan to give an overview on social evolution in animals and humans from an evolutionary and genomic/epigenetic point of view. I plan three sessions, the first will give a general overview on genotype/epigenotyp – phenotype mapping, the second will highlight and introduce epigenetic regulations and inheritance and finally I will discus the similarities and differences between social insects and humans, i.e. understand the key processes and mechanisms that sustain cooperation in these hypersocial organisms.
1. From Genotype to social Phenotypes
2. The role of epigenetics and phenotypic plasticity for individualisation and social evolution
3. Humans the other animals

Literature

• Frank Seebacher and Jens Krause: Epigenetics of Social Behaviour
• https://www.sciencedirect.com/science/article/pii/S0169534719301399
• Robert Boyd
• A different kind of animal

Lecturer

I received my PhD from the University of Würzburg in 1997, spent 2 years as a PostDoc at UC Davis in California and habilitated between 1999 and 2004 in Würzburg (Zoologie). From 2004-2016 I was professor at Arizona State University before I accepted a W3 Professorship for Molecular Evolution and Sociobiology at the University Münster. I work on the genetic and epigenetic architecture and evolution of adaptive traits in social and solitary Hymenoptera (ants, bees and wasps). Since 2021 I am also dean for the biology faculty.

Affiliation: University of Münster
Homepage: https://www.uni-muenster.de/Evolution/molevolsocbio/

Lecturer: Johannes Textor
Fields: Immunology, Artificial Intelligence, Machine Learning

Content

Our body harbours two complex learning systems: the central nervous system (CNS), and the adaptive immune system (IS). Artificial neural network models of the CNS have contributed substantial insight to neuroscience and CNS-inspired “deep learning” has revolutionized artificial intelligence. In contrast, how the IS processes information is still much less understood. This course will therefore ask: how can we understand information processing, learning and adaptation in the IS through the lens of computer science?

We will dive deep into several fascinating processes in the adaptive immune system such as negative selection, self-foreign discrimination, tolerance, and affinity maturation. I will cover the necessary immunological background to come to an understand of what we do and don’t know about these processes, and discuss how computational and mathematical models have been instrumental in our quest to understand the immune system from a computational perspective. I hope you will leave this course as fascinated and inspired by the marvelous architecture of our immune system as I am, and that this inspiration will transform and broaden your view of fundamental concepts like learning, adaptation, and generalization.

Literature

• Inge M N Wortel, Can Keşmir, Rob J De Boer, Judith N Mandl, Johannes Textor:
• Is T Cell Negative Selection a Learning Algorithm? Cells 9(3): 690, 2020. doi: 10.3390/cells9030690
• Marsland R 3rd, Howell O, Mayer A, Mehta P. Tregs self-organize into a computing ecosystem and implement a sophisticated optimization algorithm for mediating immune response. Proc Natl Acad Sci U S A. 2021 Jan 5;118(1):e2011709118. doi: 10.1073/pnas.2011709118.
• Jürgen Westermann, Tanja Lange, Johannes Textor, Jan Born:
• System Consolidation During Sleep – A Common Principle Underlying Psychological and Immunological Memory Formation.
• Trends in Neurosciences 38: 583-595, 2015.
• Stephanie Forrest, Steven A. Hofmeyr, Anil Somayaji. Computer Immunology. Communications of the ACM 40, 1997, pp 88–96. https://doi.org/10.1145/262793.262811

Lecturer

Johannes Textor is an associate professor in the Data Science and Medical BioScience departments at Radboud University and Radboud University Medical Center in Nijmegen, The Netherlands. His team studies the adaptive immune system through the lens of computation and learning. Building in silico models of the human immune system and combining these with various types of experimental data, they aim to understand the essence of what makes immune systems learn and adapt to changing environments. Johannes Textor holds a Vidi grant from the Dutch Research Council, a program grant from the Human Frontiers Science foundation, and was a visiting scholar at the Simons Institute, UC Berkeley, for the Spring 2022 semester.

Affiliation: Radboud University Nijmegen
Homepage: johannes-textor.name

Lecturer: Prof. Luc Steels
Fields: Artificial Intelligence, Consciousness

In 2021, Blake Lemoine, a software engineer at Google, and responsible for testing a chatbot LaMDA (Language Model for Dialog Applications) came to the conclusion that LaMDA was sentient, in the sense of being conscious. Since then many other users of generative AI systems, in particular ChatGPT, have reported similar experiences and the topic of machine consciousness suddenly became ‘salonfähig’, with neuroscientists and philosophers pitching in to define requirements for machine consciousness and whether or not this might ever be possible – although mostly concluding that current AI systems do not qualify as conscious agents. What are we to make of all this?

This talk intends to bring this discussion to IK, a forum exceptionally well adapted to have groundbreaking multi-disciplinary open discussions. I will argue for a vision of the mind as a dense network of complex adaptive networks, operating at different layers, from living embodiment to sentience, cognition, sapience and consciousness. The networks are autonomous and autopoietic. In other words, they develop on their own account and remain in a constant state of becoming, grounded in embodied action on the one hand and cooperation and competition with other agents on the other. We will then discuss consciousness from the point of view of this framework, reflecting on measures of awareness, the role of language, what consciousness might be for, non-ordinary states of consciousness, moral consciousness, a.o.  In this vision there is no single ‘I’, no physical conscious substance à la Penrose or Faggin, no intelligence without grounding or social interaction, no understanding without meaning. 

The goal of this talk is not to announce a new gospel but to stimulate the discussion and, in particular, become more disciplined and careful in attributing consciousness or other mental qualities to machines.

Lecturer

Luc Steels is a prominent figure in the field of artificial intelligence and cognitive science, known for his influential academic career. He has made significant contributions to various areas, including computational linguistics, robotics, and artificial life. His research has focused on the development of artificial systems capable of language evolution and cognitive processes, pioneering the use of robots and computer models to study language emergence. Throughout his career, he has held esteemed positions at institutions such as the Vrije Universiteit Brussel and the Sony Computer Science Laboratory. Luc Steels’ work has had a profound impact on our understanding of the origins of language and the evolution of intelligent systems.

Lecturer: Lukas Zenk
Fields: Improvisation; Creativity; Innovation

Content

In today’s complex and unpredictable world, the traditional homo economicus approach of careful planning and execution faces significant challenges. The demand for rapid decision making in complex situations requires a shift toward improvisation – an approach characterized by rapid problem identification, idea generation, and immediate implementation.

Drawing a parallel with classical theater, where actors meticulously follow predefined scripts, improvisational theater introduces a different paradigm. Actors take the stage without a predetermined story or characters, relying on their ability to improvise in the present moment. This skill, essential to artistic performance, finds a counterpart in the professional world, which faces time pressures and unpredictable challenges. Organizational Improvisation, an emerging field, explores and fosters this creative skill in organizations.

The workshop aims to immerse participants in improvisational mindsets and experience their applicability in a variety of scenarios, with a particular focus on co-creativity. By encouraging radical collaboration, dealing with uncertainty, and fostering co-creative learning environments, the workshop aims to inspire participants to rethink and reinvent their ways of behaving and thinking.

Literature

• Zenk, L.; Hynek, N.; Schreder, G. & Bottaro, G. (2022). Toward a system model of improvisation. Thinking Skills and Creativity, 100993. https://doi.org/10.1016/j.tsc.2021.100993
• Zenk, L.; Wetzel, R., & Peschl, M. (2023). Improvisation as a design for organizational emergence. In M.P. Cunha, V. Dusya, A. Abrantes & A. Miner (Eds.). The routledge companion to improvisation in organizations. Routledge.
• Zenk, L., Steiner, G., Pina e Cunha, M., Laubichler, M. D., Bertau, M., Kainz, M. J., Jäger, C., & Schernhammer, E. S. (2020). Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19. International Journal of Environmental Research and Public Health, 17(21), 7884. https://doi.org/10.3390/ijerph17217884.

Lecturer

Lukas Zenk (Lukas.Zenk@donau-uni.ac.at) is Associated Professor of Innovation and Network Research and Deputy Head of Research at the Department for Knowledge and Communication Management at the Danube University Krems, Austria. In his applied research projects, he investigates how people collaboratively solve complex problems and how creative and innovative processes can be supported. He led research projects on Organizational Improvisation and Meta-Competences and developed the university modules on “Business Improvisation” and “Cognition and Creativity”. Lukas co-founded the improvisational theater company Quintessenz in Vienna and was a member of the executive board of the worldwide Applied Improvisation Network. He lectures at various universities, consults companies and gives keynote presentations. His innovative lectures, talks, and research projects in Networks, Innovation and Improvisation have earned him several awards. (see www.lightbox.at)

Affiliation: University of continuing education Krems
Homepage: https://www.donau-uni.ac.at/en/university/organization/employees/person/4294993368

Lecturer: Andreas Kalckert
Fields: Cognitive Neuroscience, Experimental psychology

Content

Embodied approaches have inspired and yielded new perspectives in a variety of research disciplines. These approaches have emphasized the role of the body in cognitive functions, not only for interacting with the world, but also for perceptual experiences. Psychological experiments using bodily illusion have taken this notion a step further and have provided evidence that even temporary experiences of embodiment can directly alter perception, action, and cognition. Consequently, these tools create new ways to change individuals in unprecedented ways.

In this course, I will provide an introduction into the perceptual and neuronal processes underlying bodily illusions. I will then illustrate how such illusions have demonstrated changes in the experience and attitudes in both healthy and patient populations, and discuss the potential of using these paradigms for therapeutic interventions. We will also touch on some ethical dimensions within this research that raise questions over its use in the future.

Literature

• Ehrsson, H. H. (2019). Multisensory processes in body ownership. In Multisensory Perception: From Laboratory to Clinic. Elsevier.
• Pyasik, M., Ciorli, T., & Pia, L. (2022). Full body illusion and cognition: A systematic review of the literature. Neuroscience & Biobehavioral Reviews, 143, 104926. https://doi.org/10.1016/j.neubiorev.2022.104926
• Slater, M. & Sanchez-Vives, M. (2022). A plastic virtual self. In The Routledge Handbook of Bodily awareness. Routledge

Lecturer

Andreas Kalckert received his PhD at the Dep. of Neuroscience, Karolinska Institute (Sweden). He worked as a lecturer in psychology at the University of Reading Malaysia, and now is a senior lecturer in Cognitive Neuroscience at the University of Skövde (Sweden). In his research, he investigates the processes underlying the experience of the own body from both a psychological and neuroscientific perspective. Here he is particularly interested in the role of movements.

Affiliation: Dep. of Cognitive Neuroscience and Philosophy
Homepage: https://www.his.se/en/about-us/staff/andreas.kalckert/