My laboratory investigates human perception combining psychophysical experiments with computational modelling. Currently we have four research foci: First, to improve our image-based model of early spatial vision. Second, to connect early spatial vision with mid-level vision: perceived lightness, brightness and contrast in relation to surface reflectance and illumination in images of real scenes. Third, we investigate differences and similarities between deep convolutional neural networks and human object recognition. Fourth, we explore connections between causality from a perceptual as well as a machine learning perspective.
Felix Wichmann received his B.A. (1994) and DPhil (1999) in Experimental Psychology from the University of Oxford. After post-doctoral research at the University of Leuven (2000-2001), he worked as a research scientist in the Empirical Inference Department at the Max Planck Institute for Biological Cybernetics in Tübingen (2001-2007). From 2007 to 2011 he was Associate Professor (W2) at the Technical University of Berlin and since 2011 he is Full Professor (W3) at the Eberhard Karls Universität Tübingen.
Felix Wichmann received his B.A. in Experimental Psychology (1994) as well as his DPhil (1999) from the University of Oxford. He was awarded his doctorate for the thesis Some Aspects of Modelling Spatial Vision: Contrast Discrimination under supervision of Bruce Henning. His education was supported by a scholarship from the German National Academic Foundation (1992-1997), a scholarship from University College Oxford (1992-1994), and a Jubilee Scholarship from St. Hugh's College Oxford (1994-1997). From 1999-2001 he held a Fellowship by Examination at Magdalen College Oxford. After post-doctoral research in Johan Wageman's group at the University of Leuven in Belgium (2000-2001), Felix was a research scientist in Bernhard Schölkopf's Empirical Inference Department at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany (2001-2007). From 2007 to 2011 he was Associate Professor (W2) for Modelling of Cognitive Processes at the Technical University of Berlin and the Bernstein Center for Computational Neuroscience Berlin. Since 2011 he is Full Professor (W3) for Neural Information Processing at the Eberhard Karls Universität Tübingen and the Bernstein Center for Computational Neuroscience Tübingen. Felix served on the editorial board of Vision Research from 2011 until 2017 and thereafter he was a member of the editorial board of the Journal of Vision from 2018 till 2022.
Oxford University, Oxford, UK
DPhil, Experimental Psychology, 1999
Oxford University, Oxford, UK
B.A., Experimental Psychology, 1994
Employment and Positions
- Eberhard Karls Universität Tübingen, Faculty of Science, Department of Computer Science, and Bernstein Center for Computational Neuroscience Tübingen, Germany Full Professor (W3), 2011-present
- Max Planck Institute for Intelligent Systems Tübingen, Germany Adjunct Senior Research Scientist, 2011-2018
- Technische Universität Berlin, Faculty of Computer Science and Electrical Engineering, and Bernstein Center for Computational Neuroscience Berlin, Germany Associate Professor (W2), 2007-2011
- Eberhard Karls Universität Tübingen, Faculty of Information and Cognitive Science, Institute of Psychology Tübingen, Germany Interim Professor for Psychological Research Methods, 2005-2006
- Max Planck Institute for Biological Cybernetics Tübingen, Germany Research Scientist, 2001-2007
- Katholieke Universiteit Leuven, Department of Quantitative and Experimental Psychology Leuven, Belgium Post-doc, 2000-2001
- University of Oxford, Faculty of Psychology and Magdalen College, Oxford, UK Junior Research Fellow, 1998-2001
- Best Poster Award, 2nd place, KogWis 2018 conference of the Gesellschaft für Kognitionswissenschaft e.V. (GK), Darmstadt, Germany, in 2018 for the poster Generating Photorealistic Stimuli for Psychophysical Experiments authored by Bernhard Lang, Guillermo Aguilar, Marianne Maertens and Felix Wichmann.
- Teaching Awards for the academic years 2014/15, 2015/16 and 2016/17, Graduate Training Center, Tübingen, Germany presented by the GTC senior master students of the Neural Information Processing course.
- Teaching Awards for the academic years 2012/13 and 2013/14, Graduate Training Center, Tübingen, Germany presented by the GTC senior master students of the Neural & Behavioural Science course.
- Best Poster Award, Computational Vision & Neuroscience symposium, Tübingen, Germany, in 2008 for the poster Neural population code model for pattern detection authored by Robbe Goris, Tom Putzeys, Johan Wagemans and Felix Wichmann.
- Selected Article Award of the American Psychological Association (APA), Washington DC, USA, in 2002 for the paper The contributions of color to the recognition memory for natural scenes, with Lindsay Sharpe and Karl Gegenfurtner.
- Young Investigator Award, Runner-up, Optical Society of America, Washington DC, USA, 1999.
- Highlighted Power Award at the Annual Meeting of the Optical Society of America (OSA), in Rochester, USA, in 1996 for the poster Does motion-blur facilitate motion detection? with Bruce Henning.
- George Humphrey's Prize of the University of Oxford for Best of Year in the Final Honour Schools of Physiology, Psychology and Philosophy, 1994.
- Prize by Examination, University College Oxford, Oxford, UK, 1993.
Keynotes and Plenary Talks
- Object recognition in man and machine. Keynote, Netherlands Society for Statistics and Operations Research (VVSOR in Dutch), Amsterdam, 2021.
- Machine learning methods for system identification in sensory psychology. Opening lecture, XX. LIPP Symposium 2014, Linguistik 2.0 -- Die Herausforderung der Digital Humanities, LMU, München, 2014.
- Models of early spatial vision: Bayesian statistics and population decoding
36th European Conference on Visual Perception (ECVP), Plenary Symposium Computational Neuroscience meets Visual Perception, Bremen, Germany, August 2013.
- Machine learning methods in psychology
Keynote, 46th Annual Meeting of the Society of Mathematical Psychology, Potsdam, Germany, August 2013.
- Das Sehen durchschauen - Gehirn und Informatik
Plenary Talk, Automatisierungstechnische Verfahren für die Medizin (AUTOMED), Berlin, Germany, March 2009.
Senior Research Scientist
+49 7071 29 70580
Room no. 10-7/A3
Accurate measurements need an accurate experimental setup and precise control devices as well as reliable software. My interest is precision psychophysics and so I administer our pool of fine instruments and servers, help improving our psychophysics lab, and then am able to perform exciting experiments.
Uli studied Physics at the Eberhard Karls Universität Tübingen and received his Diplom (thesis in Universitäts Augenklinik Tübingen) in 1994. In 1999 he finished his PhD in Biology at the Max Planck Institut für biologische Kybernetik in Tübingen. From 1998 until 2011 he worked as an electronic engineer in the Institut für physikalische Chemie of the Johannes Gutenberg Universität in Mainz. Since 2012 he is a Senior Scientist in the Faculty of Science, Department of Computer Science at the Eberhard Karls Universität and Bernstein Center for Computational Neuroscience in Tübingen.
Co-supervised PhD student @ NIP and Prof. Dr. Fred Mast, University of Bern
+49 7071 29 70420
Room no. 10-29/A3
Successful recognition and categorisation both demand the availability of robust mental representations of objects. In contrast to machines, humans learn such representations from relatively little visual input. In his PhD project, Lukas uses a combination of psychophysical experiments and comparison-based machine learning methods to investigate the process in which such representations are learned. More specifically, he studies how brain-internal generative processes, such as mental imagery, may act as a biological form of data augmentation that facilitates the data-efficient acquisition of robust object representations.
Lukas received his B.Sc. and M.Sc. in Psychology from the University of Bern, Switzerland. His PhD project, "Creating to perceive: The role of mental imagery in visual object recognition and categorisation", is founded by a career grant from the Swiss National Science Foundation (SNSF) and is jointly supervised by Felix Wichmann (Neural Information Processing, University of Tübingen) and Fred Mast (Cognitive Psychology, Perception and Research Methods, University of Bern).
Co-supervised PhD student @ NIP and Wieland Brendel's lab
+49 7071 29 70582
Room no. 10-31/A4
Modern Machine Learning systems like Deep Neural Networks perform surprisingly well on hard visual tasks like object recognition and classification. But we do not yet understand the intermediate representations learned by these systems, which makes it hard to explain their decisions. This is a problem, because we cannot entrust AI-systems with important tasks in the real world if we don’t understand them. I work at the intersection of psychology and robustness research to develop methods that can help us better understand neural networks.
Thomas studied Cognitive Science (2015-2019) and Computer Science (2020-2022) at Osnabrück University before joining NIP as a PhD student. He is jointly supervised by Felix Wichmann (Neural Information Processing) and Wieland Brendel (Robust Machine Learning).
PhD student @ NIP since 03/2020
+49 7071 29 70582
Room no. 10-31/A4
How can we estimate robust psychophysical scales with machine learning?
Triplet experiments are an intuitive and robust way to measure the relationship between physical stimuli and their perception. These comparison-based approaches are not new to psychophysics, but they come with major obstacles. To overcome them, I am working on comparison-based machine learning methods to develop a new pipeline for psychophysical studies.
David received his B.Sc. in Cognitive Science (2016) from the University of Tübingen and his M.Sc. in Computer Science (2019) from the University of Freiburg. His Ph.D. project at the Cluster of Excellence "Machine Learning: New Perspectives for Science" is jointly supervised by Felix Wichmann (Neural Information Processing) and Ulrike von Luxburg (Theory of Machine Learning).
Project student @ NIP since 10/2023, Room no. 10-29/A3
MSc student @ NIP since 10/2023
MSc student @ NIP since 05/2023, Room no. 10-29/A3
BSc student @ NIP since 07/2023, Room no. 10-29/A3
Student research assistant @ NIP since 11/2022