Photography of Martin V. Butz

Martin V. Butz

Martin V. Butz is a full professor in Cognitive Modeling working at the departments of computer science and psychology, in the Faculty of Science of the University of Tübingen. His research focuses on computational and experimental cognitive science, spanning a large variety of interdisciplinary research, including, for example, the modeling of cognitive processes and behavior with artificial neural networks and the analysis of action control-oriented visual attention. In his upcoming textbook "How the Mind Comes Into Being" (Oxford University Press, to appear early 2017) he introduces cognitive science from a functional and computational perspective, integrating insights from all relevant disciplines into an overarching perspective on the phylogenetic and ontogenetic development of the human mind.

PPP Contributions


  • Belardinelli, A., Stepper, M. Y. & Butz, M. V. (2016). It's in the eyes: Planning precise manual actions before execution. Journal of Vision, 16, 18. 10.1167/16.1.18.

  • Butz, M. V. (2008). How and Why the Brain Lays the Foundations for a Conscious Self. Constructivist Foundations, 4, 1-42.

  • Butz, M. V. (2016). Towards a Unified Sub-Symbolic Computational Theory of Cognition. Frontiers in Psychology, 7, 10.3389/fpsyg.2016.00925.

  • Butz, M. V., Herbort, O. & Hoffmann, J. (2007). Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture. Psychological Review, 114, 1015-1046.

  • Butz, M. V. & Kutter, E. F. (in press). How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective. Oxford, UK: Oxford University Press.

  • Butz, M. V. & Pezzulo, G. (2008). Benefits of Anticipations in Cognitive Agents. In Pezzulo, G., Butz, M. V., Castelfranchi, C. & Falcone, R. (Eds.) The Challenge of Anticipation: A Unifying Framework for the Analysis andDesign of Artificial Cognitive Systems, Berlin Heidelberg: Springer-Verlag. 45-62.

  • Butz, M. V., Shirinov, E. & Reif, K. L. (2010). Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior. Adaptive Behavior, 18, 315-337.

  • Kneissler, J., Drugowitsch, J., Friston, K. & Butz, M. V. (2015). Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering. Frontiers in Computational Neuroscience, Frontiers Media S.A.. 9, 10.3389/fncom.2015.00047.

  • Schrodt, F. & Butz, M. V. (2016). Just Imagine! Learning to Emulate and Infer Actions with a Stochastic Generative Architecture. Frontiers in Robotics and AI, 3, 10.3389/frobt.2016.00005.