Prof. Dr. Angelika Lingnau has held the Chair of Cognitive Neuroscience at the University of Regensburg since the end of 2018. In this position, she mainly teaches the students of the master’s course in ‘Psychology’. There she focuses in particular on the topics: ‘Cognitive Neuroscience of Perception and Action’, ‘Cognitive Neuroscience of Social Vision’ and ‘Machine Learning in Neuroscience’.
Before she came to Regensburg, she went through numerous – also international – stations (such as employments in Italy and Great Britain). She studied Psychology from 1996 to 2001 at the Technical University of Braunschweig. She obtained her doctorate in 2004 at the same university. The title of her doctoral thesis is: “Seeing without a fovea? Eye movements in reading and visual search with an artificial central scotoma “.
Chair of Cognitive Neuroscience
Chair of Cognitive Neuroscience, Institute of Psychology, Faculty of Human Sciences
I mainly teach the students of the master’s course in ‘Psychology’ on the following topics:
- Cognitive Neuroscience of Perception and Action
- Cognitive Neuroscience of Social Vision
- Machine Learning in Neuroscience
AI and machine learning are playing an increasing role in the field of cognitive neurosciences, e.g. in the evaluation and interpretation of imaging methods (fMRI, M/EEG). In my courses (e.g. Machine Learning in Neuroscience; Research Colloquium Cognitive Neuroscience) I respond to these developments, e.g. by discussing current research in this area with the students.
In the seminar ‘Machine Learning in Neuroscience’ (together with Jens Schwarzbach, Working Group Biomedical Imaging), we use problem-based learning methods to enable students with different prior knowledge to work on issues independently and to familiarize themselves with the relevant specialist literature as well as to compile and deepen the newly acquired knowledge in the seminar.
Ability for independent scientific work; Ability to acquire new knowledge and to link this with existing knowledge – also from other specialist areas; Knowledge of machine learning methods used in the field of cognitive and clinical neurosciences
- to arouse curiosity about these topics
- to enable students to evaluate studies that use these methods
- to lay the foundations for using these methods in advanced courses
At the Chair of Cognitive Neuroscience, we deal with the question of how the human brain is organized in order to make sense out of diverse sensory impressions. The focus is on the perception, recognition and planning of human actions and the underlying organizational principles.
For example, we use machine learning methods to distinguish on the basis of brain activation patterns which of three actions a person will perform next, or whether a person is imagining a face or a house. Recently, we have been investigating the question of what features neural networks use to differentiate between different actions (e.g. in the form of images or videos) and to what extent these features are the same as those used by human test subjects.
I studied and did my doctorate in psychology with a focus on neuro- and cognitive psychology and then gained experience and knowledge in the field of functional imaging (focus on fMRI) as part of several postdoc positions. In the past few years, I gained knowledge in the field of AI / machine learning mainly through self-study, but also through participation in courses and lectures on the subject of neural networks / deep learning.
Question 10: Do you personally find it easy or difficult to deal with topics that are related to AI?
Rather easy.
In the field of cognitive neurosciences, the relation to AI opens up completely new accesses and research approaches that would have been inconceivable just a few years ago.
For example, I am working with colleagues from clinical psychology and media informatics on a new paradigm using virtual reality in order to be able to better examine the processes that underlie the understanding of action intentions. Such interdisciplinary collaborations, which are actively supported by the UR Fellows program, for example, have become for me an indispensable part of many research questions.
Knowledge in the field of AI / machine learning is playing an important role in more and more specialist areas, which, in addition to further developments in research, also has an impact on the needs in teaching. The establishment of the Faculty of Computer Science and Data Science provides the necessary conditions for this, and I am already looking forward to working with my future colleagues.
I don’t know much about this initiative yet, but I am pleased that this information is being gathered. So far I’ve mainly contributed with this interview (which lasted long enough …).
I think everything is said so far …
Ms. Lingnau, thank you very much for taking the time to answer these questions. We wish you a nice day!
Ms. Lingnau, thank you very much for taking the time to answer these questions. We wish you a nice day!