Introduction to Cognitive Science

Cognitive science offers us a collection of interdisciplinary approaches to gain an understanding of the human mind - especially its aspects that are difficult to measure. In the community, we open a window onto the rich world of the mind, through the lenses of psychology, computer science, and phenomenology. Students experience diverse methods used in cognitive science, immersing themselves in the phenomena they seek to explore by collecting and analysing data through novel behavioural measurement techniques.

The community’s series of hybrid cross-campus learning activities (lectures, experiments, presentations, etc.) aims to help students understand the interdisciplinary and collaborative nature of scientific inquiry in the sciences of the mind; integrate findings on selected cognitive phenomena from different disciplinary perspectives; develop skills of collaborative research, and get to understand basic concepts of cognitive science through learning about and researching selected cognitive phenomena.

Learning Community Activities

Past Events
  • Cross-EUTOPIA experiment on emotions and cognition – When? Feb-May 2021 – Where? online – more info
  • Student-led summer conference Mei:CogSci – When? 17-19th June 2021 – Where? online – online – More info available soon
  • Undergraduate Research Day – When? Summer 2021 – Where? online – more info available soon 
How to get involved?

(Students and educators)
Contact the Learning Community lead: Toma Strle (

Learning Community Members

Lead: Urban Kordeš (UL). Email:

Urban Kordeš is a professor of Cognitive Science and first-person research at the University of Ljubljana where he is currently heading the joint master’s programme in cognitive science, Center for Cognitive Science, and Laboratory for Empirical Phenomenology. He holds a bachelor’s degree in Mathematical Physics and a doctorate in the Philosophy of Cognitive Science. His research and educational interests include in-depth empirical phenomenological research, neurophenomenology, as well as challenges of interdisciplinary and collaborative knowledge creation.

Lead: Toma Strle (UL). Email:

Toma Strle is an assistant professor at the University of Ljubljana where he teaches within the joint Middle European interdisciplinary master’s programme in Cognitive Science. His research interests include decision-making, human experience, neurophenomenology, embodied cognition and self-reference.

Partner: Robert Lowe (GU). Email:

Rob Lowe is a Docent in Cognitive Science, and an associate professor, at the University of Gothenburg. His research interests are focused on computational modelling and gamification of cognitive science tasks focused on memory and decision making as affected by (social) interaction and emotion. He also works with robots to study how such models can be used as cognitive controllers in physically embodied artificial agents. He teaches introductory courses in Artificial Intelligence focused on Deep Learning and Deep Reinforcement Learning in the context of gaming.

Partner: Elisabeth Blagrove (UW). Email:

Liz Blagrove teaches and researches at the University of Warwick, across a variety of Psychological Science topics. She specializes in the interaction of cognition and emotion and is fascinated by the expressiveness of the human face.

Partner: Dimitrios Kotzinos (CY). Email:

Dimitris Kotzinos is a professor at the Department of Computer Science of the CY Cergy Paris University, a member of the ETIS Lab and a member of the MIDI team of the lab. His main research interests include data management algorithms, techniques and tools; the development of methodologies, algorithms and tools for web-based information systems, portals and web services; and the understanding of the meaning (semantics) of interoperable data and services on the web. Recently he has started working on studying the formation and evolution of discussions in online social networks using Machine Learning (ML) and Artificial Intelligence (AI) techniques. Additionally, he has started working in the area of accountability, explainability and fairness of the ML and AI algorithms, especially when applied to data engineering and analysis problems.