Constantin A. Rothkopf, PhD – Short Bio
Constantin Rothkopf is an associate professor (W2) in the psychology department and the director of the Center for Cognitive Science at the Technical University Darmstadt. After obtaining a joint PhD in Brain & Cognitive Sciences and Computer Science with Dana Ballard and Mary Hayhoe at the Center for Visual Science at the University of Rochester in 2008, he started a postdoc at the Frankfurt Institute for Advanced Studies (FIAS) working with Jochen Triesch in the theoretical neuroscience group. In 2009 he started as a lecturer at the Goethe University, Frankfurt and since 2010 he is the principal investigator of the 'beliefs, representations, and actions group' at FIAS. After a year as a substitute professor in the Institute of Cognitive Science at the University Osnabrück he is now an associate professor for 'psychology of information processing' in the department of psychology at the Technical University Darmstadt. During the winter semester 2017 he is a visiting professor at the Department of Cognitive Science at the Central European University, Budapest.
Constantin Rothkopf's research interests revolve around the distinction between 'looking' and 'seeing' and how this distinction relates to vision in goal directed behavior. The aim is to better understand the interrelationship between perception and action in humans, i.e. how we use our perceptual systems actively during natural extended behavior to guide decisions and actions with our bodies. This leads to the study of how we use sensory input, specifically vision, form beliefs about the world by carrying out computations on the basis of our cognitive representations, and then employ decision making processes to act in goal directed behavior. To achieve this goal he uses experimental studies in humans as well as computational modeling involving methods from statistical and machine learning. His current focus is on:
- behavioral studies involving eye tracking of human eye 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.