04 December – Our collaborative LOEWE Research Cluster WhiteBox has been approved for funding by the Hessian State Ministry of Higher Education, Research and the Arts. WhiteBox is joining the twin disciplines AI and Cognitive Science. Starting 2021, an interdisciplinary team will develop whitebox methods to foster human understanding of natural and artificial intelligence and its results.
29 September – Constantin gave a talk 'Reverse engineering behavioral costs and benefits with inverse reinforcement learning' in the Workshop 'Inferring and testing optimality in perception and neurons' at the Bernstein Conference
15 September – Constantin is part of the ELLIS (European Laboratory for Learning and Intelligent Systems) faculty of the newly launched ELLIS Unit at TU Darmstadt
31 August - we are part of the founding members of the Hessian Center for AI
06 July - new article in the popular science magazine 'Spektrum der Wissenschaft' together with Albert Newen, Nele Russwinkel and Martin V. Butz
24 June – Constantin gave a talk as part of smartstart's 'midsummerbrains- computational neuroscience from my point of view'
15 June – Niteesh Midlagajni joined the lab as PhD student- welcome!
02 June - Inga Ibs joined the lab as PhD student- welcome!
15 May 2020 – invited keynote at the virtual Symposium on Explainable Machine Learning for Biomedical Data Analysis and Care Robotics, Universität Oldenburg, Germany
01 April 2020 – the German Federal Ministry of Education and Research (BMBF) has approved our collaborative grant PlexPlain with Kristian Kersting, Frank Jaekel, and Florian Steinke
16 March 2020 - the lab is closed due to the Covid-19 pandemic
15 February 2020 – Fabian Kessler has joined the lab as PhD student- welcome!
10-14 February 2020 – Constantin is teaching at the Minds and Machines Workshop in Cognitive Science, Artificial Intelligence and Education at the University of Luxembourg together with Pedro Cardoso-Leite, Paul Schrater, and Christophe Schommer
Psychology of Information Processing
Our group works on understanding how humans use their perceptual systems, especially vision, actively during natural extended behavior to guide decisions and actions with their bodies. This leads to the study of how the visual system uses sensory input, forms beliefs about the world by carrying out computations on the basis of its representations, and then carries out actions in goal directed behavior.
Both experimental evidence from the area of active vision as well as theoretical advances in control theory and machine learning show that perception, action, and learning can only be separated under very restricted circumstances demonstrating the importance of the behavioral goal i.e. the current task. Our work therefore emphasizes the necessity to consider perception, cognition, decision making and actions jointly and investigates their interplay.
To make progress in this area we use an integrated approach requiring us to apply different methodological techniques. Our current focus is on:
- behavioral studies involving eye tracking of human eye and body movements during complex naturalistic tasks in natural and virtual environments,
- building models of tasks and developing algorithms that learn how to solve these tasks in virtual agents,
- developing models for the representation and quantification of extended sequential human behavior,
- simulation of learning algorithms in naturalistic virtual environments,
- developing learning algorithms with an emphasis on the learning of sensory representations for actions.
