Prof. Dr. Martin V. Butz has been a professor at the Department of Computer Science and the Department of Psychology, Faculty of Science, University of Tübingen, Germany, since 2011. His main background lies in computer science and machine learning (Diploma from Würzburg University and PhD from the University of Illinois at Urbana-Champaign, IL, USA; both in Computer Science). Throughout his research career he has been collaborating with researchers from various other disciplines, including cognitive and developmental psychologists, computational neuroscientists, roboticists, and linguists. His research focuses on neuro-computational, cognitive modeling and cognitive science more generally. A current main focus lies in uncovering conceptual, compositional, causal structures from sensorimotor experiences in humans and artificial systems. Important recent publications include his co-authored monograph: “How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective” (Oxford University Press, 2017), a special issue on “Event-Predictive Cognition” (2021, Topics in Cognitive Science), and a computational perspective on human and artificial intelligence (“Towards Strong AI”, 2021, Künstliche Intelligenz).
Developing Event-Predictive Gestalt Models for Perception and Behavior
Hierarchical, compositionally-recombinable models and behavioral primitives constitute crucial components for interacting with our world in a versatile, flexible, adaptive manner. Event-predictive cognition offers a theoretical framework on how such models may develop and may be invoked in a self-motivated manner. In the presentation, I selectively introduce some of our recent recurrent artificial neural network models, sketching-out a pathway on how to develop event-predictive Gestalten and how their anticipatory, self-motivated activation can model human-like behavior. First, I introduce an RNN that infers Gestalten from dynamic motion stimuli, modeling the bi-stable perception of the Silhouette illusion. Next, I briefly show how the sensory information about our world may by structured in an affordance-oriented manner. Finally, on a more abstract level, I show how event-predictive latent codes can develop and can be used not only to solve state-of-the-art reinforcement learning benchmarks but also to mimic the development of anticipatory behavior in infants.