Research Interests
- Using AI and machine-learning approaches to understand human behavior and cognition.
- Combining computational modeling with machine learning to build better theories of cognition.
- Strengthening the connection between psychological theories and everyday behavior to make psychology more relevant to society.
To accomplish this, I combine experimental and individual differences approaches with computational, statistical, and machine-learning models.
Selected Publications
- Thalmann, M., Binz, M., & Schulz, E. (2026). Understanding behavior through permutation-based predictive modeling. PsyArXiv.
- Thalmann, M. & Schulz, E. (2026). Modeling, Measuring, and Mapping Individual Differences in Mental Object Representations. PsyArXiv.
- Voudouris, K., Thalmann, M., Kipnis, A., Hernández-Orallo, J., & Schulz, E. (2026). Measuring What AI Systems Might Do: Towards A Measurement Science in AI. arXiv.
- Thalmann, M., Witte K., & Schulz, E. (2025). Model-based exploration is measurable across tasks but not linked to personality and psychiatric assessments. Scientific Reports, 15(1), 1-19.
- Haridi, S., Schulz, E., & Thalmann, M. (2025). Context Size and Set Size Effects: The Relevance of Specific Cues When Searching Long-Term Memory. Computational Brain & Behavior.
- Binz, M. et al. (2025). A foundation model to predict and capture human cognition. Nature, 1–8.
