Constantin A. Rothkopf, PhD
Speaker for Darmstadt
Technische Universität Darmstadt
Psychology of Information Processing
Our research focuses on explaining human sequential visuomotor decisions and behavior under the influence of the world's uncertainties and ambiguities through computational modeling. Using the reverse-engineering approach, we devise algorithms for inferring individuals' internal models about the world, tracking their subjective beliefs over time during behavior and learning, and their internal subjective cost and benefits including effort. This naturally touches several areas including perception and action, active inference and active vision, sequential decision making under uncertainty, intuitive physics.
A Dynamic Bayesian Actor Model explains Endpoint Variability in Homing Tasks.
Sometimes I feel the fear of uncertainty stinging clear: How Intolerance of Uncertainty and Trait Anxiety impact fear acquisition, extinction and the return of fear.
Structural Causal Interpretation Theorem.
Improving saliency models' predictions of the next fixation with humans' intrinsic cost of gaze shifts.
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2104-2114).
Large pre-trained language models contain human-like biases of what is right and wrong to do.
Nature Machine Intelligence, 4(3), 258-268.
Reinforcement learning with non-exponential discounting.
Neural Information Processing Systems
Inverse optimal control adapted to the noise characteristics of the human sensorimotor system.
Advances in Neural Information Processing Systems, 34, 9429-9442.
Bayesian Classifier Fusion with an Explicit Model of Correlation.
In International Conference on Artificial Intelligence and Statistics (pp. 2282-2310). PMLR.