Lecturer: Martin Takac Fields: Artificial Intelligence/Cognitive Science
Content
How do we know that a system – living or artificial – understands something? If it makes sense of its experience and ascribes it meaning – how is this meaning represented within the system? In my course I will start with basic overview of semantic theories and grounded cognition. I will cover grounding of abstract concepts and language syntax, developmental approach to grounding meaning in AI and also analyze modern large-scale language models and semantics in deep neural networks.
Knott, A. & Takac, M. (2021). Roles for Event Representations in Sensorimotor Experience, Memory Formation, and Language Processing. Topics in Cognitive Science 13(1). 187-205.
Borghi A.M., Barca L., Binkofski F., Tummolini L. (2018) Varieties of abstract concepts: development, use and representation in the brain. Phil. Trans. R. Soc. B, 373: 20170121
Smith, L. & Gasser, M. (2005). The Development of Embodied Cognition: Six Lessons from Babies. Artificial Life. Vol. 11, Issues 1-2, pp. 13 – 30.
Zaadnoordijk, L., Besold, T.R. & Cusack, R. (2022). Lessons from infant learning for unsupervised machine learning. Nature Machine Intelligence 4, 510–520.
Roy, N. et al (2021): From Machine Learning to Robotics: Challenges and
Martin Takac received his PhD in artificial intelligence from Comenius University in Bratislava where he currently works as associate professor in cognitive science. His research specializes on computational modelling of sense-making and meaning construction. He is also a co-creator of cognitive architecture of BabyX – a virtual infant.
Lecturer: Raoul Bongers Fields: Human Movement Sciences
Content
The lectures will explain fundamental issues in motor control, motor coordination and motor learning from an Action-perception perspective. To this end we employ a joint perspective from Ecological Psychology and a Dynamical Systems approach to movement coordination. Thee are all systems-perspectives. I will focus on four themes: – The motor system is organised in synergies – Information-movement couplings control actions – Learning new synergies – Embodying hand prostheses
Literature
Kristoffersen, M. B., Franzke, A. W., Sluis, C. K. van der, Murgia, A. & Bongers, R. M. Serious gaming to generate separated and consistent EMG patterns in pattern-recognition prosthesis control. Biomedical Signal Processing and Control 62, 102140 (2020).
Pacheco MM, Lafe CW and Newell KM (2019) Search Strategies in the Perceptual-Motor Workspace and the Acquisition of Coordination, Control, and Skill. Front. Psychol. 10:1874. doi: 10.3389/fpsyg.2019.01874
Profeta, V. L. S. & Turvey, M. T. Bernstein’s levels of movement construction_ A contemporary perspective. HUMAN MOVEMENT SCIENCE 57, 111–133 (2017).
Richardson, M.J., Shockley, K., Fajen, B.R., Riley, M.A., Turvey, M.T., 2008. Ecological psychology: six principles for an embodied–embedded approach to behavior. In: Calvo, P., Gomila, A.B.T.-H., of, C.S. (Eds.), Perspectives on Cognitive Science. Elsevier, San Diego, pp. 159–187. https://doi.org/10.1016/B978-0-08-046616-3.00009-8.
Zhao, H. & Warren, W. H. On-line and model-based approaches to the visual control of action. Vision Research 110, 190–202 (2015).
Lecturer
Raoul Bongers
Dr. Bongers received his PhD from the Radboud University Nijmegen in the area of developmental psychology. For 20 years he works now at the Department of Human Movement Sciences of the University Medical Center Groningen. His research focuses on motor coordination and motor learning from an action-perception perspective. He is interested in fundamental issues in motor learning, in particular how people learn to coordinate their degrees of freedom in new synergies. He applies these insight to develop rehabilitation strategies in upper limb prosthesis for more than 15 years and recently also to stroke rehabilitation.
Affiliation: Dept of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands Homepage:https://www.rug.nl/staff/r.m.bongers/
Lecturer: Markus F. Peschl Fields: knowledge creation, innovation, creativity, enactive cognition
Content
The guiding question for this course will be: How do novel knowledge and innovation, or generally speaking, novelty come into the world? More specifically, we will take a closer look at foundations and perspectives of creativity, knowledge creation, and innovation. We propose to understand them as socio-epistemic processes that unfold in interaction between a (group of) cognitive system(s) and its (material) environment leading to the creation of artifacts. Moreover, we will discuss what it means to bring forth novel and sustainable knowledge/innovations in a future-oriented manner both on an individual and collective/organizational level as well as what are enabling (environmental) factors and conditions supporting such processes („Enabling Spaces“).
In discussing and questioning classic approaches to creativity and innovation, we will follow concepts that are inspired by the enactivist approach to cognition, such as De Jaegher’s et al. (2007, 2021) participatory sense-making or engaged epistemology, as well as Malafouris’ (2013) Material Engagement Theory. They suggest that, in a creative process, a (group of) cognitive/creative agent(s) does not primarily pursue a hylomorphic activity of imposing their own preconceived ideas/knowledge on the world/matter, but engage in a process of “creative thinging“ (Malafouris 2014). In other words, this means that by actively engaging with the world, making sense of it, and (co-)creating „things“/artifacts (→ „thinging“), one taps into not yet realized unfolding (future) potentials or „learns from the future as it emerges“ (Scharmer 2016); the becoming of reality turns into a source for novelty/novel knowledge. This turns the classic understanding of creativity and knowledge creation on its head, as „creative agency“ is—at least in part—shifted from the creator’s mind to the environment and to interacting/engaging with the world. This entails that (epistemic) control has to be given up (or at least reduced) in favor of openness to the affordances and potentials of a world in becoming. Creative activities have to be conceived as processes of co-becoming, undergoing, and correspondence with the world (e.g., Ingold 2013, 2014). We will discuss theoretical issues as well as (practical) consequences of such a perspective in terms of necessary alternative cognitive skills, mindsets/attitudes, and enablers, such as developing a sense for potentials, openness, „epistemic humility“, or enabling environmental (infra-)structures, etc.
Literature
De Jaegher, H. and E. Di Paolo (2007). Participatory sense-making. An enactive approach to social cognition. Phenomenology and the Cognitive Sciences 6(4), 485–507.
De Jaegher, H. (2021). Loving and knowing: reflections for an engaged epistemology. Phenomenology and the Cognitive Sciences 20(5), 847–870.
Ingold, T. (2013). Making. Anthropology, archaeology, art and architecture. Abingdon, Oxon; New York, NY: Routledge.
Ingold, T. (2014). The creativity of undergoing. Pragmatics & Cognition 22(1), 124–139.
Ingold, T. (2022). Creation beyond creativity. In T. Ingold (Ed.), Imagining for real. Essays on creation, attention and correspondence, pp. 15–28. Abingdon, Oxon; New York, NY: Routledge.
Malafouris, L. (2013). How things shape the mind. A theory of material engagement. Cambridge, MA: MIT Press.
Malafouris, L. (2014). Creative thinging: The feeling of and for clay. Pragmatics & Cognition 22(1), 140–158.
Peschl, M.F. (2019). Design and innovation as co‐creating and co‐becoming with the future. Design Management Journal 14(1), 4–14.
Peschl, M.F. (2020). Theory U: From potentials and co-becoming to bringing forth emergent innovation and shaping a thriving future. On what it means to \”learn from the future as it emerges\”. In O. Gunnlaugson and W. Brendel (Eds.), Advances in Presencing, pp. 65–112. Vancouver: Trifoss Business Press.
Scharmer, C.O. (2016). Theory U. Leading from the future as it emerges. The social technology of presencing (second ed.). San Francisco, CA: Berrett-Koehler Publishers.
Lecturer
Markus Peschl
Markus F. Peschl (*1965) is professor of cognitive science and philosophy of science at the University of Vienna, Dept. of Philosophy. His areas of research and expertise include innovation and alternative approaches to creativity, cognitive science (4E/enactive cognition), organizational theory and strategy, design, and spaces for knowledge- and innovation work (Enabling Spaces). He is one of the founders of the inter-faculty interdisciplinary Vienna Cognitive Science Hub and the head of the International Middle European Joint Masters Program in Cognitive Science (MEi:CogSci) and the Extension Curriculum on Innovation & Knowledge Creation. Markus is head of the OCKO – Organizing Cognition in Knowing Organizations Research Group. He spent several years at the University of California, San Diego (UCSD, cognitive science, neuroscience, and philosophy department) and at the University of Sussex for post-doctoral research. Furthermore, he studied philosophy in France. He is co-founder and CSO of the theLivingCore Innovation and Knowledge Architects and holds several guest professorships at European Universities.
This course is intended for all participants, who are curious about perceiving different aspects of their mind, their body, things that surround us, and dreams. During the course we will focus on the broad field of self-experience. We are working with techniques from the fields of self-awareness, mindfulness, body perception, biography reflection, interpersonal and intrapersonal communication and different schools of psychotherapy.
Every session will have a different theme. Sessions 1 will be about self-experience of the mind, session 3 will be about self-experience of the body, and session 4 will be about self-experience and things. Session 2 will be about experiencing dreams. During this session we will use a special technique called “social dreaming”. For this session there will be a short introduction on the evening before the session. You are welcome to join us for just one session or to join us for up to four sessions and experience exercises with different foci. Previous knowledge is not necessary for this course, just bring an open and curious mind.
Lecturer
Annekatrin Vetter is a clinical psychologist, psychotherapist and psychoanalyst. She is doing analytic and psychotherapeutic outpatient treatment in private practice in Cologne. Moreover, she works as a lecturer for clinical psychology at the Rheinische Fachhochschule Köln and as a trainer for coaches. Besides that, she currently works on a research project about treatment integrity in Mentalization based group therapy.
Prof. Dr. Katharina Krämer is a psychologist and psychoanalytic psychotherapist. She works as a professor for psychology at the Rheinische Fachhochschule Köln, Germany, and as a psychotherapist in private practice. In 2014, Katharina Krämer received her doctoral degree from the University of Cologne, Germany, on a thesis investigating the perception of dynamic nonverbal cues in cross-cultural psychology and high-functioning autism. She works with patients with different mental disorders, focusing on adult patients with autism. Her research interests include the application of Mentalization-Based Group-Therapy with patients with autism and the vocational integration of patients with autism.
Dr. Sophia Reul is a psychologist and psychoanalytic psychotherapist. She works as a clinical psychologist at a psychiatric hospital Evangelisches Krankenhaus Bergisch Gladbach, Germany), and as a psychotherapist in private practice. She works with patients with different mental disorders. In 2020, Sophia Reul received her doctoral degree from the Westfälische Wilhelms-University Münster, Germany, on a thesis investigating the efficiency of neuropsychological diagnostic for early neurodegenerative diseases. Her research interests include the application of Mentalization-Based Group-Therapy with patients with autism.
In this course I will provide an overview of the cognitive, perceptual, and neural mechanisms underpinning bodily self-awareness. Following Gallagher’s distinction of ownership vs. agency and using the rubber hand illusion as an experimental model, I will discuss how sensory and motor processes give rise the experience of the own body. The course consists of two talks introducing the experimental and theoretical findings, followed by a hands-on session with the rubber hand illusion demonstrating some of the practical caveats when performing this experiment.
Literature
Ehrsson HH. Multisensory processes in body ownership. In: Sathian K, Ramachandran VS, eds. Multisensory Perception: From Laboratory to Clinic. Academic Press: Elsevier; 2020:179-200.
Graziano, M. S. A., & Botvinick, M. (2002). How the brain represents the body: insights from neurophysiology and psychology (pp. 136–157). In: Common Mechanisms in Perception and Action: Attention and Performance XIX. Eds. W. Prinz and B. Hommel. Oxford University Press, Oxford England
Kalckert, A. (2017) I am moving my hand – Ownership, Agency, and the body. in “Sensation of movement”. Ed. Thor Gruenbaum and Mark Schram Christensen. Psychology Press: Current Issues in Consciousness Research
Kalckert, A. & Ehrsson, H.H. (2012). Moving a rubber hand that feels like your own: a dissociation of ownership and agency. Frontiers in Human Neuroscience, Volume 6, Article 40
Lecturer
Dr. Andreas Kalckert received his Ph.D. from the Karolinska Institute, Sweden (Brain, Body, and Self Lab, Department of Neuroscience). After his Ph.D. he worked as a lecturer in psychology at the University of Reading Malaysia. He works now as a Senior lecturer in Cognitive Neuroscience at the Department of Cognitive Neuroscience and Philosophy at the University of Skövde (Sweden). In his research, he specializes in the cognitive and perceptual processes of bodily self-awareness. His particular interests lies in the interaction of the Sense of Ownership and Sense of agency in body illusion paradigms such as the rubber hand illusion.
This is a crash course on dynamical systems, held at the Interdisciplinary College already several times – there is a demand! The presentation is meant to be introductory, understandable for a general natural / neural / cognitive science audience. Here is the planned schedule:
Session 1: Part I: Introduction: so many ways to classify models of dynamical systems! – Part II: A zoo of finite-state models: finite-state automata with and without input, deterministic and non-deterministic, probabilistic), hidden Markov models and partially observable Markov decision processes.
Session 2: Finite-state models continued: Cellular automata, dynamical Bayesian networks. Part III: A zoo of continuous state models: iterated function systems, ordinary differential equations, stochastic differential equations, delay differential equations, partial differential equations, (neural) field equations. Part IV: What is a state? Takens’ theorem.
Session 3: Part V: State-free models of temporal systems. The engineering view on “signals”. Describing sequential data by grammars. Chomsky hierarchy. Exponential and power-law long-range interactions. Part VI: qualitative theory of dynamical systems. Attractors, structural stability.
Session 4: Part VI continued: bifurcations. Phase transitions. Topological dynamics. Discussion: attractors and symbols. Part VII: non-autonomous dynamical systems. Basic definitions. Nonautonomous attractor concepts.
Herbert Jaeger studied mathematics and psychology at the University of Freiburg and obtained his PhD in Computer Science (Artificial Intelligence) at the University of Bielefeld. After a 5-year postdoctoral fellowship at the German National Research Center for Computer Science (Sankt Augustin, Germany) he headed the “Intelligent Dynamical Systems” group at the Fraunhofer Institute for Autonomous Intelligent Systems AIS (Sankt Augustin, Germany). From 2003 to 2019 he was Associate Professor for Computational Science at Jacobs University Bremen, and since 2019 he is Full Processor for Computing in Cognitive Materials at the University of Groningen. His current research revolves around formal theory-building for non-digital computing.
The brain, the cause of – and solution to – all of life’s problems. According to our brains it is the most fascinating structure in the known universe. Consisting of about 86 billion neurons where each can form thousands of connections to other neurons it is also the most complex structure in the known universe. In this course we would like to give you a rough guide and introduction to the basic principles, fundamental theories, and methods of neuroscience. We will demonstrate that neuroscience can be seen as a multi-modal, multi-level, multi-disciplinary research framework that aims at addressing the challenges of this megalomaniac scientific endeavor. We will see that different frameworks and methods can lead to conflicting empirical evidence, theoretical assumptions, and heated debates. However, we argue that this might be the only way to uncover the mysteries of our brain. In this course we will cover a variety of scopes and perspectives. We will teach some of the fundamentals of neuroscience in human and non-human animals, but we will also explore some explanatory gaps between the different levels of inference. On a phenomenal level we will investigate the functions of individual neurons and small networks. We will discuss if and how we can learn from (genetically modified) model animals about neural functions. To what degree is this relevant for understanding human brain function, such as learning and decision making? On the other hand, we will also investigate the state of the art in human brain mapping and cognitive neuroscience. Can findings from neuroimaging tell us anything at all about neurobiology – or are they just fancy illustrations that are better suited for children’s books?
Objectives: – To understand the anatomy and function of neurons – To understand the interaction of neurons in a functional network – To understand central methods and theories used in neurobiology and human cognitive neuroscience – To understand the scope of different methods and theoretical frameworks
Literature
Cacioppo JT, Berntson GG, Lorig TS, Norris CJ, Rickett E, Nusbaum H. Just because you’re imaging the brain doesn’t mean you can stop using your head: a primer and set of first principles. J Pers Soc Psychol. 2003 Oct;85(4):650-61. [Link]
Park HJ, Friston K. Structural and Functional Brain Networks: From Connections to Cognition. Science, 2013 Nov;6158(342):1238411 [Link]
Till Bockemühl studied biology and philosophy at Bielefeld University. He did his diploma thesis as well as his doctoral thesis with Volker Dürr in the lab of Holk Cruse at Bielefeld University. Currently, he is a postdoctoral researcher in the lab of Ansgar Büschges at the University of Cologne. His main research interests comprise the motor control of locomotion, neuroethology, and computational neurobiology. To investigate these topics, he uses the fruit fly Drosophila and the ever-expanding toolkit of methodological opportunities this model organism has to offer.
Ronald Sladky. My research focuses on the amygdala and emotion processing in the human brain. In addition, I am always working on new neuroimaging, data processing, and modeling methods. One of these new methods is real-time functional MRI, where people can learn to regulate their own brain states while they are inside the MRI scanner. This method is not only a promising therapeutic tool, it will also allow for completely new ways of discovering how our brains work.
Machine learning is concerned with automatically learning models (patterns, regularities, correlations) from known data which generalize to new data. To do so, it 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 my focus is on lots of examples and pictures.
In more detail, the course will have four sessions with the following topics:
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
Classic machine learning tasks and methods to solve them: The distance perspective on ML, Regression, Classification, Dimensionality Reduction, Clustering
Artificial neural networks and deep learning: Neural network modules, recipes for neural networks, adversarial attacks
Reinforcement learning and ethics
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
Literature is optional and more regarded as ‘further/complementary reading’:
Benjamin Paaßen received their doctoral degree in intelligent systems in 2019 from Bielefeld University on the topic of ‘Metric Learning for Structured Data’. Afterwards, they received a DFG research fellowship for a stay at The University of Sydney in Australia and Humboldt-University of Berlin. Since 2021, they are deputy head of the educational technology lab at the German Research Center for Artificial Intelligence (DFKI). Their research foci are machine learning on structured data and artificial intelligence for education.
