Prof.
Constantin A. Rothkopf, PhD
Speaker for Darmstadt
Technische Universität Darmstadt
Psychology of Information Processing
Alexanderstraße 10
64283 Darmstadt
Short info
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.
Open Science
What matters for active texture recognition with vision-based tactile sensors.
arXiv preprint 2403, 13701.
Convolutional neural network reveals frequency content of medio-lateral COM body sway to be highly predictive of Parkinson's disease.
medRxiv, 2023-05.
Study Protocol TransTAM: Transdiagnostic Research into Emotional Disorders and Cognitive-Behavioral Therapy of the Adaptive Mind.
OSF preprint
A Dynamic Bayesian Actor Model explains Endpoint Variability in Homing Tasks.
bioRxiv
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.
PsyArXiv Preprints
Structural Causal Interpretation Theorem.
arXiv preprint
Articles
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).
Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties.
Nature Communications, 15(1), 5677.
Models of vision need some action.
Behavioral Brain Science, 46:e405.
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.
Straub, Dominik, and Constantin A. Rothkopf. "Putting perception into action with inverse optimal control for continuous psychophysics.
Elife 11,e76635.
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs.
Advances in Neural Information Processing Systems, 36.
People use Newtonian physics in intuitive sensorimotor decisions under risk. In
Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45).
Modelling dataset bias in machine-learned theories of economic decision-making.
Nature Human Behaviour, 1-13.
Bayesian Classifier Fusion with an Explicit Model of Correlation.
In International Conference on Artificial Intelligence and Statistics (pp. 2282-2310). PMLR.
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.