I am a postdoctoral researcher at the Psychological Methods Unit of the University of Amsterdam, working on a Talent Grant obtained from the Amsterdam Brain and Cognition research platform. My research focuses on the intersection between high-level cognition (e.g., religious beliefs, morality), low-level mechanisms (cognitive or neural processing) and methodological advancements. My work typically involves answering substantive questions in the field of social and cognitive psychology using tools such as Bayesian hierarchical modeling, a many-analysts approach, analysis blinding, or multiverse analysis.
Across all my research, I apply Bayesian statistics. Specifically, I have used Bayesian hierarchical modeling, a flexible and powerful method to maximize the informativeness of the data yet constrain inference based on structural features (e.g., people nested in countries, trials nested in people), as well as theoretical predictions (e.g., all countries should show an effect in the same direction).
In my current post-doc position, I will further develop these Bayesian hierarchical models for joint modeling of behavioral (e.g., accuracy, response times) and neural data (e.g., fMRI, EEG). In this project, we aim to provide (1) guidelines for the necessary number of trials and subjects in cognitive tasks given the signal-to-noise ratio and (2) a collection of openly accessible joint modeling pipelines combining behavioral and neural data to optimize this signal-to-noise ratio.