Research

Skill

Skill acquisition represents an ideal model system for the adaptive mind. Acquiring complex skills (e.g., sports) involves extensive practice, which elicits manifold changes of body and mind to enable stable and ever-higher levels of performance. Here we investigate some of the most advanced mechanisms of adaptation—learning and practice. This allows us to test how the other Key Areas are integrated into highly optimized performance.

New project-related publications
McKay, C., Wijeakumar, S., Rafetseder, E., & Shing, Y. L. (2021).
Disentangling Age and Schooling Effects on Inhibitory Control Development: An fNIRS Investigation.
bioRxiv
Meibodi, N., Abbasi, H., Schubö, A., & Endres, D. M. (2021).
A model of selection history in visual attention.
In Proceedings of the Annual Meeting of the Cognitive Science Society 43(43), 707-713.
Preißler, L., Jovanovic, B., Munzert, J., Schmidt, F., Fleming, R.W., & Schwarzer, G. (2021).
Effects of visual and visual-haptic perception of material rigidity on reaching and grasping in the course of development
Acta Psychologica, 221, November, 103457
Schultheis, M., Straub, D., & Rothkopf, C. A. (2021).
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
arXiv preprint
Thoma, N., Yu, Z., Ventola, F., & Kersting, K. (2021).
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting.
Proceedings of the 4th Workshop on Tractable Probabilistic Modeling (TPM 2021).
Trick, S., & Rothkopf, C. A. (2021).
A Normative Model of Classifier Fusion.
arXiv preprint
Ventola, F., Dhami, D. S., & Kersting, K. (2021).
Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits.
Proceedings of the 30th International Conference on Inductive Logic Programming (ILP).
Yu, Z., Dhami, D. S., & Kersting, K. (2021).
Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits.
arXiv preprint
Yu, Z., Ventola, F. G., & Kersting, K. (2021).
Whittle networks: A deep likelihood model for time series.
International Conference on Machine Learning (PMLR), 12177-12186.
Yu, Z., Zhu, M., Trapp, M., Skryagin, A., & Kersting, K. (2021).
Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression.
Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021).
Zečević, M., Dhami, D. S., Rothkopf, C. A., & Kersting, K. (2021).
Structural Causal Interpretation Theorem.
arXiv preprint