In order to  understand the complexities of cognition, we follow the long-standing tradition of developing computational or mathematical models of cognition. The current focus is on decision-making processes, that we aim to understand through evidence accumulation models.

Recent projects: 

• The Bayesian Mutation Sampler

• Cognitive strategies in economic choices

• Cognitive strategies in tasks involving visual working memory

• Cognitive models of sequential decisions

Optimal support of decision-making requires an understanding of the cognitive and social processes involved when humans make decisions. To this end, the COBRA lab develops computational models that predict decision-making processes. These models are tuned to individual user characteristics and can be used to reveal individual and group differences in decision-making styles. Consequently, we work on decision-support systems that help people make better decisions.

Recent projects:

• User models of decision styles

• Bias in human-AI interaction

• Value-Alignment in Medical AI

Large, multivariate datasets are not straightforward to analyze. We develop and apply novel ways of analyzing cognitive neuroscience data. Often these methods involve insights from machine-learning, but they always start from the view point that analysis methods are ideally grounded in cognitive theory, in order to facilitate interpretation. 

Recent projects: 

• hmp: a novel python package for detecting cognitive events in neural time series

• fixed-point property analysis of binary mixtures