Nicola ­ Schneider

My research focuses on the cognitive and computational foundations of learning and decision-making under uncertainty. My main focus is on modeling cognitive processes over time, particularly by combining reinforcement learning with response time-based approaches such as the diffusion decision model and other sequential sampling models. The goal is to capture dynamic changes in behavior and cognitive parameters to better understand how humans learn from experiences, make decisions, and self-regulate under uncertainty (e.g., exploration-exploitation).

Methodologically, I work primarily with Bayesian modeling approaches and experimental paradigms such as instrumental and associative learning tasks, decisions from experience, and other ecological sampling approaches.

Additionally, I am interested in epistemological and philosophical questions related to scientific research in psychology. In particular, I am concerned with the tension between descriptive and predictive modeling approaches. I also consider qualitative and social anthropological perspectives—especially grounded theory and interpretive-phenomenological analyses—as complementary approaches to gaining psychological insight.

Research interests

• Dynamic Bayesian modeling of cognitive processes
  
• Reinforcement learning and response time models (e.g., DDM)
• Instrumental and associative learning, decisions from experience, and ecological sampling
• Epistemological and scientific philosophical questions of psychological modeling