Research Interests and Areas of Expertise
- Quantitative Behavioral Research Methods
- Artificial Intelligence (AI)
- Human-Computer Interaction (HCI)
- Judgment and Decision-Making (JDM)
- Social Cognition/Rationality
As illustrated below, psychological and economic experimentation and the development and application of advanced quantitative methods (e.g., Rebholz, Biella, et al., 2024) form the methodological roof of my research program investigating people’s judgment formation, decision-making, and belief updating. The phenomena I study empirically cluster into four pillars: informational and social influence from others (e.g., advice taking; Rebholz & Hütter, 2022); sequential sampling and utilization of external evidence (e.g., Bayesian updating; Rebholz et al., 2023); adaptiveness of belief updating in these contexts (e.g., heuristics and biases; Rebholz, Groß, et al., 2025; but also science communication; Schreiner et al., 2025, in press); and implications of these phenomena for human-computer interaction (e.g., algorithm aversion vs. appreciation; Rebholz, conditionally recommended; Rebholz, Koop, et al., 2024; Rebholz, Uphoff, et al., 2025). This last pillar primarily concerns augmented JDM settings, where human decision-makers retain responsibility for the final decision but receive input from AI and other decision-support systems before or during the judgment formation and belief updating process.