Author: Philipp Wicke

Lecturer: Marianna Bolognesi
Fields: CogSci, NLP, Linguistics

Content

This course explores how language shapes our capacity to think and communicate about concepts that extend beyond the concrete and immediate, from everyday generalisations to complex scientific ideas. It examines how these abilities are mirrored, tested, and expanded by artificial systems such as large language models.

Participants will discuss how abstract meaning is created, shared, and adapted in human interaction, and how machines approach similar tasks in ways that can be both powerful and limited. Drawing on examples of data gathered from people and generated by language models, we will reflect on how human intuitions and artificial output can be compared and used together to reveal new insights about meaning-making.

Based on themes and research findings from the ERC project ABSTRACTION ( ERC-2021-STG-101039777), this course invites students and researchers to consider how language connects minds, data, and technologies within increasingly blended digital realities.

Literature

Basic introductory literature:

• Reilly, J., Shain, C., Borghesani, V. et al. What we mean when we say semantic: Toward a multidisciplinary semantic glossary. Psychonomic Bulletin & Review (2024). https://doi.org/10.3758/s13423-024-02556-7
• Barsalou Lawrence W. (2003). Abstraction in perceptual symbol systemsPhil. Trans. R. Soc. Lond. B 358 1177–1187
• Bolognesi, M., Burgers, C., & Caselli, T. (2020). On abstraction: decoupling conceptual concreteness and categorical specificity. Cognitive processing, 21(3), 365–381. https://doi.org/10.1007/s10339-020-00965-9
• Burgoon, E. M., Henderson, M. D., & Markman, A. B. (2013). There are many ways to see the forest for the trees: A tour guide for abstraction. Perspectives on Psychological Science, 8(5), 501–520. https://doi.org/10.1177/1745691613497964
• Ilievski, F., Hammer, B., van Harmelen, F., Paassen, B., Saralajew, S., Schmid, U., Biehl, M., Bolognesi, M., Dong, X. L., Gashteovski, K., Hitzler, P., Marra, G., Minervini, P., Mundt, M., Ngomo, A.-C. N., Oltramari, A., Pasi, G., Saribatur, Z. G., Serafini, L., … Villmann, T. (2024). Aligning Generalisation Between Humans and Machines. arXiv. https://doi.org/10.48550/arXiv.2411.15626
  
  Research findings by the ABSTRACTION research group, which will be discussed during the course:
  
• Ravelli, A. A., & Bolognesi, M. (2024). Yet another approximation of human semantic judgments using LLMs… but with quantized local models on novel data. Italian Journal of Computational Linguistics, 10(2), 146. https://doi.org/10.17454/IJCOL102.04
• Puccetti, G., Collacciani, C., Ravelli, A.A., Esuli, A., Bolognesi, M. (2025). ABRICOT – ABstRactness and Inclusiveness in COntexT: A CALAMITA Challenge. Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024). pages: 1161-1167. https://aclanthology.org/2024.clicit-1.128/
• Mazzuca, C., Villani, C., Lamarra, T., Bolognesi, M., Borghi, AM (2025). Abstractness impacts conversational dynamics. Cognition (258), 106084. https://doi.org/10.1016/j.cognition.2025.106084
• Ravelli, A.A., Bolognesi, M.M. & Caselli, T. Specificity ratings for English data. Cognitive Processing (2024). https://doi.org/10.1007/s10339-024-01239-4
• Rambelli, G., Chersoni, E., Collacciani, C., & Bolognesi M. (2024). Can Large Language Models Interpret Noun-Noun Compounds? A Linguistically-Motivated Study on Lexicalized and Novel Compounds. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Pp: 11823–11835. https://aclanthology.org/2024.acl-long.637/
• Collacciani, C., Rambelli, G., & Bolognesi, M. (2024). Quantifying Generalizations: Exploring the Divide Between Human and LLMs’ Sensitivity to Quantification. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Pp: 11811–11822.  https://aclanthology.org/2024.acl-long.636/
• Mazzuca, C., Villani, C., Lamarra, T., Bolognesi, M. M, & Borghi, A. (2024). Abstract Sentences elicit more Uncertainty and Curiosity than Concrete Sentences. Proceedings of the Annual Meeting of the Cognitive Science Society, 46. Retrieved from https://escholarship.org/uc/item/7cj2g289
• Collacciani, C., Ravelli, A., & Bolognesi, M. (2024). Specifying Genericity through Inclusiveness and Abstractness Continuous Scales. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Pp: 15126–15136. https://aclanthology.org/2024.lrec-main.1315/
• Rambelli, G. & Bolognesi, M. (2024). The Contextual Variability of English Nouns: The Impact of Categorical Specificity beyond Conceptual Concreteness. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Pp: 15854–15860. https://aclanthology.org/2024.lrec-main.1377/
• Genovese, F., Bolognesi, M. M., Di Iorio, A., & Vitali, F. (2024). The advantages of gamification for collecting linguistic data: A case study using Word Ladders. Online Journal of Communication and Media Technologies, 14(2), e202426. https://doi.org/10.30935/ojcmt/14443
• Villani C, Loia A, Bolognesi MM. (2024). The semantic content of concrete, abstract, specific, and generic concepts. Language and Cognition. Published online 2024:1-28. doi:10.1017/langcog.2023.64
• Bolognesi, M.M., Collacciani, C., Ferrari, A., Genovese, F., Lamarra, T., Loia, A., Rambelli, G., Ravelli, A.A., Villani, C. (preprint). Word Ladders: A Mobile Application for Semantic Data Collection. arXiv:2404.00184 [cs.CL] https://doi.org/10.48550/arXiv.2404.00184
• Bolognesi, M.M., Caselli, T. (2023). Specificity ratings for Italian data. Behav Res 55, 3531–3548 https://doi.org/10.3758/s13428-022-01974-6
• Rambelli, G. & Bolognesi, M. (2023). Contextual Variability Depends on Categorical Specificity rather than Conceptual Concreteness: A Distributional Investigation on Italian data. Proceedings (selected papers) of IWCS 2023, Nancy, France. https://aclanthology.org/2023.iwcs-1.2
• Collacciani, C., & Rambelli, G. (2023). Interpretation of Generalization in Masked Language Models: An Investigation Straddling Quantifiers and Generics. In Proceedings of the 9th Italian Conference on Computational Linguistics – CLiC-it 2023. https://ceur-ws.org/Vol-3596/paper17.pdf

Lecturer

Marianna Bolognesi is a linguist specializing in cognitive and distributional semantics at the Department of Modern Languages, Literatures and Cultures at the University of Bologna. She was a Marie S. Curie Research Fellow at the University of Amsterdam and a research associate at the University of Oxford before joining UniBo as a tenure-track researcher. In 2022 she was awarded an ERC grant for the project ABSTRACTION, which investigates how abstraction operates in thought, language, and creativity, both in humans and in artificial intelligence. She is also vice-PI and work unit coordinator of the national PRIN 2022 project WEMB, which explores how vector representations of word meaning relate to human mental representations. Her research combines psycholinguistic experiments and computational modelling in a cross-disciplinary perspective.

Affiliation: University of Bologna, Italy
Homepage: https://www.unibo.it/sitoweb/m.bolognesi/en

Lecturer: Celeste Kidd
Fields: Cognitive Learning, Curiosity & Metacognition, AI & Misinformation

Content

In this lecture series, I will discuss our lab’s research about how people (and some non-human animals) come to know what they know about the world. The world is a sea of information too vast for anyone to acquire entirely. How do people navigate the information overload, and how do their decisions shape their knowledge and beliefs? We’ll discuss recent empirical work about the core cognitive systems that people use to guide their learning about the world—including attention, curiosity, and metacognition (thinking about thinking). We discuss the evidence that people play an active role in their own learning, starting in infancy and continuing through adulthood, and how many of these mechanisms are shared with non-human animals. We’ll talk about why we are curious about some things but not others, and how our past experiences and existing knowledge shape our future interests. We’ll also discuss why people sometimes hold beliefs that are inconsistent with evidence available in the world, and how we might leverage our knowledge of human curiosity and learning to design systems that better support access to truth and reality.

A running theme throughout this series will be the importance of uncertainty in guiding our learning and collaborative knowledge building. We will be taking a comparative approach in outlining the types of cognitive representations of uncertainty shared among biological intelligence, but lacking in artificial ones, to explain how current generative AI models cannot be trusted to disseminate information to people without problems. We’ll discuss several core tenets of human psychology that can help build a bridge of understanding about what is at stake when discussing regulation and policy options to prevent widespread adoption of these AI technologies from permanently distorting human beliefs in problematic ways.

Lecturer

Celeste Kidd studied print journalism and linguistics at the University of Southern California, where she earned a dual honors degree in 2007. Kidd moved to the University of Rochester for her graduate studies, where she worked in brain and cognitive studies and earned her PhD in 2013. She worked with Richard N. Aslin, an expert on infant learning. Kidd held visiting positions at Stanford University and the Massachusetts Institute of Technology. Kidd is a professor of psychology at the University of California, Berkeley.

Affiliation: UC Berkeley
Homepage: https://psychology.berkeley.edu/people/celeste-kidd

Lecturer: Terry Stewart
Fields: Computational Neuroscience

Modern AI deep-learning systems are very impressive, however they take a large amount of training data and can be very power-hungry. This large amount of training time can be thought of as an attempt to find good ways for the neural network to represent information. Biological brains, in contrast, tend to learn new tasks quickly, perhaps because they have already learned a good general-purpose neural representation of data. This means that if we can understand how brains represent information, we can then take those styles of representation and force AI systems to use exactly that same style of representation. In this talk I will show some examples of doing this, and show how it reduces training time and makes AI systems more robust.

Unfortunately, Lara Wilkin had to cancel due to sickness. Her course description is still available here:

ET1 – From a Creative User and Professional Peer Counselor to a Researcher in Upper Limb Prosthetics

Lecturer: Lara Wilkin
Fields: Research – Upper Limb Prosthetics, Professional Peer Counselor, Creative Background

Content

Growing beyond oneself as an expert in one\’s own field and looking beyond the familiar horizon is often initially overlooked until one\’s competence is proven. Research is interdisciplinary, and who, if not the users themselves, possesses the in-depth knowledge of everyday requirements and challenges?

By combining a natural-scientific and technical affinity with a creative background, Lara Wilkin is engaged both in research and through her voluntary work and networks to pave new ways, raise awareness, and advocate for those affected. Her journey spans from being a creative user and professional peer worker to becoming a researcher in the field of upper limb prosthetics.

Lecturer

Lara Wilkin – Academic and Professional Background Ms. Lara Wilkin possesses a comprehensive interdisciplinary expertise in the fields of upper limb prosthetics and design, which is complemented by her academic career, professional experience, and extensive further education and training. She is currently pursuing a PhD in Upper Limb Prosthetics at Bauhaus University (since 2024). Her professional career includes positions in various institutions and companies, including her work at Saphenus Medical Technology (2023–2025). Additionally, she completed advanced courses in rehabilitation technology, prosthetics, and biomedical engineering at TU Wien (2023–2024). Since 2021, Ms. Wilkin has been actively engaged as a volunteer prosthesis usage trainer for all hand prosthesis models and works as a workshop leader and speaker, addressing medical associations and prosthetic care professionals in collaboration with the Federal Ministry of Labour and Social Affairs, as well as presenting at the World Congress of OTWorld. In the field of advocacy and voluntary work, she assumed the role of Germany’s first official representative for upper limb amputations at the Federal Association for People with Arm or Leg Amputations (BMAB) in July 2022. From November 2023 to September 2024, she also served as Vice Chair of LVamp NRW e.V. Prior to that, she was a board member of the State Association for People with Arm or Leg Amputations NRW e.V. from February 2022 to November 2023. Ms. Wilkin is a functional bilateral upper limb amputee, having undergone a transradial amputation in 2019 and experiencing another accident in February 2022. Her personal experience allows her to integrate both scientific and technological perspectives into her work. Her dedication to prosthetics is reflected in the founding of the Arm Prosthesis Community (2021), where she serves as an expert, consultant, and network partner. Since 2020, she has worked as a research assistant in the field of information technology, where her activities deepened her interest in biomedical engineering, biomedical information technology, and robotics. Additionally, since 2020, she has been officially trained in peer counseling for amputees and has been active both nationally and internationally, advising patients, medical professionals, and relatives on amputation-related topics. Her academic journey began with a Bachelor of Arts (2014), followed by a Master of Arts (2020). Since 2014, she has also worked as a freelance communication designer, collaborating with esteemed clients such as FH Dortmund (including projects for NASA/JPL), the Federal Association of Digital Publishers and Newspaper Publishers e.V., Adobe, Schweppes, Nescafé, and other major companies. Her creative work has earned multiple international awards, with exhibitions and competition wins in Italy, the USA, and China. In recognition of her scientific and practical expertise, she has become a member of leading international and national professional associations, including: * ISPO – International Society for Prosthetics and Orthotics (Germany e.V.) * BMAB – Federal Association for People with Arm or Leg Amputations e.V. * LVamp NRW – State Association for People with Arm or Leg Amputations e.V. * Arm Prosthesis Community * World Design Consortium * IDC – International Design Club * IAD – International Association of Designers * ICCI – International Council of Creative Industries * ISPM – International Society of Product Manufacturers * IBSP – International Bureau of Service Providers * AIBA – Alliance of International Business Associations Through her continuous education, including participation in specialized congresses and training sessions, such as the 2nd Amputation Course at the University Hospital Hamburg for Surgeons, where osseointegration and various amputation techniques were tested on body donor, as well as international world and annual congresses (ISPO, BMAB, etc.), Ms. Wilkin has expanded her global network. Her interdisciplinary background, which combines creative expertise, hands-on technical knowledge, personal experience, and scientific proficiency, makes her one of the very few experts worldwide who can evaluate all commercially available hand prosthesis models both through personal testing as a user and from a scientific perspective.

Affiliation: Lara
Homepage: www.linkedin.com/in/larawilkin

Content

Beyond the normal use of your senses, what else is possible? There have been different approaches to augment your senses. Here you will learn and experience more about space perception. How do you perceive space? What is space perception, and can it be changed? People’s experiences and skills vary greatly on how easily they can orient themselves in unknown environments. What happens if you always knew where north is, as well? You can try it with the feelSpace belt, which always points towards north via vibration. You will perform your own little experiments to aim for a new perception of space. 

Throughout the course and the whole IK it will be possible to lend out compass belts. 

Spacial perception can also be augmented through sound, e.g. as known from bats through echolocation. Also humans are capable of using echoes to learn a lot about their environment which has proven to be a helpful orientation skill for many blind people. But how do blind people learn to use echolocation? And can sighted people learn to use echolocation? We will present you the basics of how it is instructed. You will learn that you can hear more than you might guess.

1.     Session: Introduction

You will learn what sensory substitution and augmentation is and what is currently done to enable the use of information via unusual paths. We will present some examples and introduce to you the compass belt from feelSpace and the research behind it that proves how using this tool can help to gain a new sense of space. We will try out the compass belt and do some small experiments to experience the new “north-sense”.

2.     Session: From research to startup: feelSpace

You will learn more about the story of feelSpace, the startup that develops tactile belts to make navigation easy for everyone. In groups you will develop your own ideas on using the presented technology and plan little experiments that you conduct in this session and throughout the course. 

3.     Session: Echolocation

Some blind people use echolocation to “see” their environment. How do they do it and what is possible with this method? In this session we teach you the basics and we will do some practical exercises to show you how you can “sense” the space around you. This session can be visited independently to the others. 

4.     Session: Finalizing and presenting your sensory augmentation experiments

You have time to rap up your results and short presentations and present them to the group. We will discuss your ideas and approaches and finish with a feedback round to discuss the experiences of the course participants with the compass belt and the entrepreneurial approach.

Lecturer

Julia Wache studied Cognitive Science in Vienna and Potsdam. She finished her PhD in Trento working on the Emotion Recognition via physiological signals and mental effort in the context of using a tactile belt for orientation. In parallel she participated in the EIT Digital doctoral program to learn entrepreneurial skills. In 2016 she joined the feelSpace GmbH that develops and sells naviBelts, tactile navigation devices especially designed for the visually impaired.

Affiliation: feelSpace
Homepage: feelspace.de

Lio Franz studied Linguistics and Computer Science at the University of Bielefeld and graduated M.A. in Linguistics, while also teaching new students the ropes of their field. During the pandemic they worked at the health office, learning much about the processes and bureaucracy around health administration and surveillance. Since 2023 they are part of the feelSpace GmbH team, supporting their endeavor to enable a greater accessibility via sensory augmentation through the naviBelt; which can be used as training for a more accurate sense of space, and guide our customers as a navigation tool of independence.

Affiliation: feelSpace
Homepage: feelspace.de

Lecturer: Ivana Kajic
Fields: Artificial Intelligence, Machine Learning, Cognitive Science

Content

Recent years have seen a proliferation of machine learning models that are capable of producing high-quality images that faithfully depict concepts described using natural language. Such models can generate images that represent arbitrary objects, object attributes, and complex relations between objects. In this talk, I will show that despite these impressive advancements, such models can still struggle with relatively simple tasks. Specifically, I will demonstrate that even the most advanced models have only a rudimentary notion of number sense. Their ability to correctly generate a number of objects in an image is limited to small numbers, and it is highly dependent on the linguistic context the number term appears in. I will further highlight challenges associated with evaluation of different model capabilities, including evaluation of numerical reasoning, and talk about different automated approaches that can be used to evaluate models in a more interpretable way by leveraging existing tools in machine learning and cognitive science.

Literature

• Kajić, I., Wiles, O., Albuquerque, I., Bauer, M., Wang, S., Pont-Tuset, J., & Nematzadeh, A. (2024). Evaluating Numerical Reasoning in Text-to-Image Models. 38th Conference on Neural Information Processing Systems.
• Testolin, A., Hou, K., & Zorzi, M. (2024). Large-scale Generative AI Models Lack Visual Number Sense. arXiv preprint arXiv:2402.03328.
• Wiles, O., Zhang, C., Albuquerque, I., Kajić, I., Wang, S., Bugliarello, E., Onoe, Y., Knutsen, C., Rashtchian, C., Pont-Tuset, J. & Nematzadeh, A. (2024). Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings. arXiv preprint arXiv:2404.16820.
• Kajić, I., & Nematzadeh, A. (2023). Evaluating Visual Number Discrimination in Deep Neural Networks. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45).
• Nieder, A. (2020). The adaptive value of numerical competence. Trends in Ecology & Evolution, 35(7), 605-617.
• Dhariwal, P., & Nichol, A. (2021). Diffusion models beat GANs on image synthesis. Advances in neural information processing systems, 34, 8780-8794.

Lecturer

Ivana Kajić is a Senior Research Scientist at Google DeepMind in Montréal, Canada. Her research interests include applying methods and techniques from cognitive science in analysis and characterization of behavior of machine learning models. Specifically, this includes designing evaluation protocols, benchmarks and metrics to comprehensively understand capabilities and limitations of large vision-language models that in recent years have demonstrated strong performance in a variety of tasks. She completed her PhD thesis titled “Computational Mechanisms of Language Understanding and Use in the Brain and Behaviour” in 2020 at the University of Waterloo in Canada.

Affiliation: Google DeepMind
Homepage: www.ivanakajic.me

Lecturer: Catherine Sibert
Fields: Cognitive Modeling, Neuroscience, Cognitive Architectures

Content

The human brain is an incredibly complex system, and many approaches and perspectives are taken in an effort to better understand how it works. While this diversity of approaches has led to many insights, too often they are taken in isolation from one another, and it can be difficult to incorporate the findings from one perspective into others. Modeling-based approaches to understanding cognition, with their origins in computer science, artificial intelligence, and information processing, struggle to connect with researchers with a stronger neuroscience focus, as the models often do not incorporate or function at a higher level of abstraction than what is known about the biological brain. However, what we do understand about the brain is often at a lower level of abstraction than can be easily connected to the higher level cognitive components of models. In this course, we will discuss the strengths and weaknesses of the modeling approach, what kind of information we can actually extract from the brain, and some examples of how the two approaches can be combined to form a broader perspective of how to study the brain.

Lecturer

Catherine Sibert is an assistant professor of Human Computer Collaboration at the Faculty of Science and Engineering of the University of Groningen. Her research focuses on the use of AI tools and frameworks in the analysis of human brain data, with an emphasis on how models of whole-brain cognition can provide insight into the underlying mechanisms of thought. She is also interested in how understanding the brain can inform and improve AI systems to collaborate more effectively with human users.

Affiliation: University of Groningen
Homepage: www.catherinesibert.com

Lecturer: Logan Bentley, Harriet Crisp
Fields: Education, Artificial Intelligence, Machine Learning

Content

Join us for an interactive, hands-on workshop where you’ll design AI-powered interventions to tackle real-world challenges in early childhood education. In this course we will merge academic expertise with industry-driven insights – equipping and inspiring you to create solutions addressing the global learning crisis.

The course will begin with foundational instruction in core concepts and practices of early childhood education, emphasizing its critical importance in shaping equitable futures. Then we’ll explore the transformative potential of AI in education, presenting case studies from EIDU (https://www.eidu.com/) where traditional pedagogical interventions have been augmented with AI. These will include:
– Structured Pedagogy: How we leverage large language models (LLMs) to generate unique and relevant lesson plans that empower teachers.
– Personalized Learning: The use of machine learning to tailor individual students’ learning paths and maximize their learning outcomes.

With this foundation, we’ll move into an immersive design workshop where you prototype practical, AI-driven interventions targeting improvement for early learners’ literacy and numeracy. You’ll be guided with instruction on best practices, proven frameworks, and lessons from our own experience.

The learning crisis is a global challenge; solving it demands collaboration across disciplines, industries, and borders. Let’s explore together how we all can make a lasting impact.

Literature

• Friedberg, A (2023). Can A/B Testing at Scale Accelerate Learning Outcomes in Low- and Middle-Income Environments? (https://link.springer.com/chapter/10.1007/978-3-031-36336-8_119)
• Sun, C, Major, L, Moustafa, N, Daltry, R, and Freidberg, A (2024) Learner Agency in Personalised Content Recommendation: Investigating Its Impact in Kenyan Pre-primary Education. (https://link.springer.com/chapter/10.1007/978-3-031-64312-5_25)
• Sun, C, Major, L, Moustafa, N, Daltry, R, and Freidberg, A (2024) Teacher-AI Collaboration in Content Recommendation for Digital Personalised Learning among Pre-primary Learners in Kenya (https://dl.acm.org/doi/10.1145/3657604.3664662)

Lecturer

Logan is an accomplished software engineer with over a decade of experience in the education and gaming industries. He is an engineering manager at EIDU, where for the last years his team has been focused on improving learning outcomes for EIDU\’s EdTech interventions in low-income countries.

Affiliation: EIDU GmbH
Homepage: https://www.eidu.com/

Harriet received her Master’s degree in Engineering from the University of Cambridge. She began her career as a Machine Learning Engineer at a data consultancy, where she worked for two years. She then joined EIDU as a Data Scientist, bringing her expertise to the field of educational technology.

Affiliation: EIDU GmbH
Homepage: https://www.eidu.com/

Lecturer: Marieke van Vugt
Fields: Psychology, neuroscience, cognitive science

Content

Contemplative practices such as mindfulness are often marketed as methods to augment our minds: making us more concentrated, more happy, more efficient. But is that all these practices are about? And what is actually the evidence? In contemplative science we critically examine how contemplative practices affect mind, brain and body. In this course, we will blend contemplative science with actual practice of the relevant contemplative practice. It seeks to give a broad overview of different practices. In session 1, we will practice mindfulness and discuss evidence for effect of mindfulness on cognition. In session 2, we will practice analytical meditation and discuss how it affects mind and brain. In session 3, we will focus on embodied practices and use contemplative dance as an example. There is relatively little science on this topic, but we will focus on the science of embodied practices in general, and leave space to discuss your own ideas about how to study these practices and/or how to use them in your own life.

Literature

• Fox, K. C., Dixon, M. L., Nijeboer, S., Girn, M., Floman, J. L., Lifshitz, M., … & Christoff, K. (2016). Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations. Neuroscience & Biobehavioral Reviews, 65, 208-228.
• Vago, D. R., & Silbersweig, D. A. (2012). Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness. Frontiers in human neuroscience, 6, 296.

Lecturer

Marieke van Vugt received her PhD from the University of Pennsylvania. She is now an associate professor at the AI department of the University of Groningen, the Netherlands. In her lab, she tries to understand when, how and why we mind-wander, using methods from psychology, neuroscience and AI. She is also interested in the effects of contemplative practices on our mind, especially on our mind-wandering. In addition, she collaborates with Tibetan Buddhist monks on the practice of analytical meditation and monastic debate. Besides her work as an academic, she is also a classical ballet dancer with Amsterdam Amateur Ballet.

Affiliation: University of Groningen
Homepage: https://mkvanvugt.wordpress.com

Lecturer: Cosima Prahm
Fields: Neuroscience, Medicine, Engineering

Content

Although the hand represents only 1% of our body weight, most of our sensorimotor cortex is associated with its control. The loss of a hand therefore not only signifies the loss of the most important tool with which we can interact with our environment, but also leaves us with a drastic sensory-motor deficit that challenges our central nervous system. Restoring hand function is therefore not only an essential part of restoring physical integrity and functional employability, but also closes the neural circuit, thereby reducing phantom sensations and nerve pain.

When there is no longer sufficient anatomy to restore meaningful function, we can resort to complex robotic replacements whose functional capabilities in some respects even surpass biological alternatives, such as conservative reconstructive measures or transplantation of a hand. However, as with replantation and transplantation, the challenge with bionic robotic replacements is to solidly attach it to the skeleton and connect the prosthesis to our neural and muscular system to achieve natural, intuitive control and also provide basic sensory feedback.

This interdisciplinary course will discuss the progressive development of upper extremity robotic prosthetics in the fields of bioengineering, medicine, computer science, and neuroscience. We address the medical basis of biosignals, movement, amputation and restoration, and various systems of prosthetic limbs to restore physical integrity. We will discuss enhancement versus restoration and how to improve the man-machine-interface, exemplified with case studies. 

Sessions in detail: We will cover the fundamentals of bionic extremity reconstruction, including replantation, transplantation, and biological restoration, along with an overview of various upper limb prosthetic types. Participants will explore conventional and advanced prosthetic control methods, including Machine Learning applications and innovative human-machine interfaces like nerve transfers, osseointegration, and implanted electrodes. Cutting-edge concepts, such as self-contained neuromusculoskeletal prostheses and cyborg technologies, will also be discussed. The course will address virtual rehabilitation using mixed reality (XR) environments, focusing on traumatic hand injuries, phantom limb pain, and neural pain management. 

Literature

• Aszmann, O. C., & Farina, D. (2021). Bionic Limb Reconstruction. In O. C. Aszmann & D. Farina (Eds.), Bionic Limb Reconstruction (1st ed.). Springer International Publishing. https://doi.org/10.1007/978-3-030-60746-3
• Prahm, C., Daigeler, A., & Kolbenschlag, J. (2021). Bionische Rekonstruktion der oberen Extremität. In Plastische Chirurgie (3rd ed., pp. 135–145). Kaden.

Lecturer

Cosima Prahm received her PhD in Medicine – Clinical Neuroscience from the Clinical Laboratory for Bionic Extremity Reconstruction at the Medical University of Vienna, Austria. From 2019 to 2024, she led the Research Laboratory in the Department of Hand, Plastic, Reconstructive, and Burn Surgery at the University Clinic of Tübingen, BG Klinik, Germany. Currently, she serves as the Director of the Center for Clinical Research at the ukb Unfallkrankenhaus Berlin, within the Department of Hand, Replantation, and Microsurgery, Charité University Medicine, Berlin. Her research focuses on enhancing human-machine interfaces for upper extremity amputees, advanced prosthetic control, bionic reconstruction, nerve regeneration, organ-on-chip models, and virtual rehabilitation in XR environments.  

Affiliation: 
– Director of the Center for Clinical Research
– Clinic for Hand, Replantation and Microsurgery
– ukb Klinik Trauma Hospital Berlin, Germany
– Charité University Medicine Berlin, Germany

Homepage: www.playbionic.org

Lecturer: Magda Gregorova
Fields: Machine Learning, Deep Learning

Content

In this introductory course we will cover the basics of machine learning (ML) targeting specifically the un-initiated audience. If you are not sure what machine learning actually is, if you have never trained an ML model, if for you deep learning means learning in deep sleep and ChatGPT is a result of dark magic, then this course is meant for you. The course will be organized in four sessions where (1) we will begin from the basic concepts of learning from data reviewing the fundamental ideas and building stones of machine learning, (2) we shall discuss some classical ML algorithms which are still the workhorses for solving many practical problems, (3) we shall explore the more modern deep learning approaches based on neural network models, and (4) we shall uncover some of the magic behind ChatGPT by discussing the concepts of deep generative modelling. The field is vast and very fast paced combining mathematics with computer science while spicing it up with ideas coming from physics, neuroscience, and many other areas. While some math cannot be avoided, it is not my ambition to cover all the technicalities of ML. I will rather endeavor to help you build your own picture of the field based on basic understanding of the underlying fundamental ideas and nature your own intuition for data analysis hoping you will become more comfortable and confident when exploring the ML methods in your future work.

Literature

• Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
• Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.
• Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning. Springer New York Inc.
• Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781107298019
• MacKay, D. J. C. (2003). Information theory, inference and learning algorithms. Cambridge University Press.
• Cover, T.M. and Thomas, J.A. (2006) Elements of Information Theory. John Wiley & Sons, Inc., Hoboken.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into Deep Learning. ArXiv Preprint ArXiv:2106.11342.
• Prince, S. J. D. (2023). Understanding deep learning. The MIT Press. http://udlbook.com
• Tomczak, J. M. (2022). Deep Generative Modeling. Springer International Publishing. https://doi.org/10.1007/978-3-030-93158-2

Lecturer

Magda Gregorova comes from Prague, Czech Republic, where she obtained her Master‘s degree in Statistics (2001) from the University of Economics. She started her career as an applied statistician in the Czech National Bank, where she headed a technical unit on financial statistics and collaborated closely with the ECB and the IMF. After several years in banking she has decided to follow an international career and joined Eurocontrol, the European Organization for the Safety of Air Navigation based in Brussels, Belgium, as a statistical analyst and forecaster. She then moved to Geneva, Switzerland, where she obtained in 2018 a PhD in machine learning from the Computer Science Department of the University of Geneva. She continued as a post-doc in the Data Mining and Machine Learning group of the University of Applied Sciences of Western Switzerland. In 2021 Magda has moved to Germany, where she obtained the research professorship for “Representation and Learning in Artificial Intelligence” at the Faculty of Computer Science and Business Information Systems of the Technical University of Applied Sciences Würzburg-Schweinfurt (THWS). She is a founding member of the THWS research Center for Artificial Intelligence (CAIRO) which she has led from its beginnings in 2022 till mid 2024. Her teaching activities are mainly within the international masters on AI in the areas of deep learning and generative modelling. In her research she focuses on deep unsupervised learning methods for modelling complex high-dimensional distributions and data representations for downstream tasks (https://scholar.google.com/citations?user=68MKCOwAAAAJ&hl=en). In addition to her own research she regularly contributes to the machine learning community through reviewing service (ICML, ICLR, NeurIPS, etc.) and by active participation in outreach and educational events such as IK.

Affiliation: Technische Hochschule Würzburg-Schweinfurt
Homepage: https://fiw.thws.de/en/our-faculty/staff/person/prof-dr-magda-gregorova/