Chair for Cognitive Modeling / Faculty of Science / Department of Computer Science
Computer Science & Psychology, Cognitive Modeling
D - 72076 Tübingen
Over the last two decades or so, cognitive science has increasingly acknowledged that the human mind constitutes a predictive, generative system, which builds behavior- and motivation-oriented internal models. These models essentially constitute our beliefs about how the encountered environment (with all its physical, biological, cultural, social, etc. factors) seems to work, that is, our individual world knowledge. When processing information – such as when receiving speech input during a conversation or when reading a text – it is combined with these beliefs for constructing a consistent interpretation. During this process, missing information is filled in and alternative interpretations, due to ambiguous information content, are considered, prioritized, overlooked, revised etc. Similarly, when producing communicative information (such as when speaking, writing, or interacting solely by means of body language) our beliefs about the knowledge of our communication partners are considered for ensuring sufficient mutual understanding.
The RTG-projects concerned with cognitive modeling develop computational models of these processes, aiming at integrating linguistic content with sub-symbolic, conceptual world knowledge, which is learned from sensorimotor experiences. Thus, this work essentially tackles the symbol grounding problem and the frame problem in cognitive science, focusing on ambiguity perception, resolution, and production.
After studying Computer Science (diploma in 2001) and Psychology (minor) at the University of Würzburg, Martin Butz completed his PhD in Computer Science at the University of Illinois at Urbana-Champaign (UIUC), IL, USA in 2004. During his Post-Doc years, he completed his habilitation about computational models of generative encodings of the own body and the surrounding environment at the University of Würzburg in 2011. In the same year, Martin Butz was appointed full professor in Cognitive Modeling at the Department of Computer Science (cooptation in Psychology in 2012) at the University of Tübingen.
While Martin Butz has completed all degrees in computer science, he has worked as a student research assistant and later as a research assistant and research group leader with psychologists, cognitive roboticists, - linguists, and – neuroscientists as well computational cognitive modelers. Accordingly, Martin Butz has published his research work in psychological outlets, such as Psychological Review or Experimental Brain Research, cognitive science journals, such as Cognition or TopiCS in Cognitive Science, as well as machine learning outlets, including Neural Networks or the International Conference on Artificial Neural Networks. His current main research focus lies in Event-Predictive Cognition, as a tool and mechanism to learn conceptual abstractions that make us language-ready. His recent book on “How the Mind Comes into Being” provides an integrative introduction to cognitive science and his approach from a computational perspective.
Martin Butz teaches introductory courses in Computer Science and Cognitive Science as well as advanced courses on Cognitive Modeling, Artificial Neural Networks, and Machine Learning of Behavior. Practical courses and seminars are offered on developing cognitive processes, cognitive systems, conversational agents, cognitive psychological experiments – particularly with eye tracking, motion tracking and within virtual realities – and artificial neural networks.
In various collaborative projects, Martin Butz has published two special issues (in Cognitive Processing and New Ideas in Psychology), seven edited volumes on anticipatory processing, cognition, and on cognitive systems, as well as three monographs. He is on the editorial board of Cognitive Processing and Frontiers in Psychology, Cognition. Moreover, Martin Butz is part of the cluster initiative Machine Learning as well as the SFB initiative “Reflective Eating: Cognition and emotion in the control of food intake”. Meanwhile, he is establishing a collaborative research initiative on “Event-Predictive Cognition” with linguists, psychologists, clinical and cognitive neuroscientists, and computational modelers.
Publications on "Ambiguity"
- Achimova, Asya; Gregory Scontras, Christian Stegemann-Philipps, Johannes Lohmann & Martin V. Butz (2021). “Learning about others: Pragmatic social inference through ambiguity resolution.” Cognition.
- Butz, Martin V. (2021). “Towards strong AI.” Künstliche Intelligenz 35. 91–101. doi: 10.1007/s13218-021-00705-x
- Butz, Martin V., Asya Achimova, David Bilkey & Alastair Knott (2021). “Event‐predictive cognition: A root for conceptual human thought.” Topics in Cognitive Science 13. 10–24. doi: 10.1111/tops.12522
- Gumbsch, Christian; Maurits Adam, Birgit Elsner & Martin V. Butz (2021). “Emergent goal-anticipatory gaze in infants via event-predictive learning and inference.” Cognitive Science 45(e13016). doi: 10.1111/cogs.13016
- Butz, Martin. V.; David Bilkey, Dania Humaidan, Alastair Knott & Sebastian Otte (2019). “Learning, planning, and control in a monolithic neural event inference architecture.” Neural Networks 117. 135-144.
- Belardinelli, Anna; Johannes Lohmann, Alessandro Farnè & Martin V. Butz (2018). “Mental space maps into the future.” Cognition 176. 65–73. doi: 10.1016/j.cognition.2018.03.007.
- Lohmann, Johannes; Phillip A. Schroeder, Hans-Christoph Nuerk, Christian Plewnia & Martin V. Butz (2018). “How deep is your SNARC? Interactions between numerical magnitude, response hands, and reachability in peripersonal space.” Frontiers in Psychology 9. 622. doi: 10.3389/fpsyg.2018.00622.
- Butz, Martin. V. (2017). “Which Structures Are Out There.” Philosophy and Predictive Processing. Hgg. Thomas K. Metzinger & Wanja Wiese. Frankfurt am Main: MIND Group. predictive-mind.net/papers/which-structures-are-out-there
- Johannes Lohmann; Martin V. Butz. (2017). “Lost in space: multisensory conflict yields adaptation in spatial representations across frames of reference.” Cognitive Processing 18. 211–228. doi: 10.1007/s10339-017-0798-5.
- Johannes Lohmann; Bettina Rolke & Martin V. Butz (2017). “In touch with mental rotation: Interactions between mental and tactile rotations and motor responses.” Experimental Brain Research 235. 1063–1079. doi: 10.1007/s00221-016-4861-8.
- Schrodt, Fabian; Jan Kneissler, Stephan Ehrenfeld & Martin V. Butz (2017). “Mario Becomes Cognitive.” TOPICS in Cognitive Science 9.2. 1-31.
- Butz, Martin. V. (2016). “Towards a Unified Sub-Symbolic Computational Theory of Cognition.” Frontiers in Psychology 7. doi: 10.3389/fpsyg.2016.00925.
- Belardinelli, Anna; Marissa Barabas, Marc Himmelbach & Martin V. Butz (2016). “Anticipatory eye fixations reveal tool knowledge for tool interaction.” Experimental Brain Research 234. 2415-2431. doi: 10.1007/s00221-016-4646-0.
- Schrodt, Fabian; Martin V. Butz (2016). “Just Imagine! Learning to Emulate and Infer Actions with a Stochastic Generative Architecture.” Frontiers in Robotics and AI. doi: 10.3389/frobt.2016.00005.
- Schrodt, Fabian; Georg Layher, Heiko Neumann & Martin V. Butz (2015). “Embodied Learning of a Generative Neural Model for Biological Motion Perception and Inference.” Frontiers in Computational Neuroscience 9. doi: 10.3389/fncom.2015.00079.
- Herbort, Oliver; Martin V. Butz (2012). “The continuous end-state comfort effect: Weighted integration of multiple biases.” Psychological Research 76. 345-363.
- Herbort, Oliver; Martin V. Butz (2011). “Habitual and goal-directed factors in (everyday) object handling.” Experimental Brain Research 213. 371-382.
- Butz, Martin. V.; Elshad Shirinov & Kevin L. Reif (2010). “Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior.” Adaptive Behavior 18. 315-337.
- Butz, Martin. V.; Roland Thomaschke, Matthias J. Linhardt & Oliver Herbort (2010). “Remapping motion across modalities: Tactile rotations influence visual motion judgments.” Experimental Brain Research 207. 1-11.
- Butz, Martin. V.; Oliver Herbort & Joachim Hoffmann (2007). “Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture.” Psychological Review 114. 1015-1046.