Decision making and learning are essential for adaptive behavior and often dysfunctional in psychiatric diseases. We examine the cognitive, computational, and neural mechanisms underlying these fundamental abilities in humans. Our research can broadly be divided into the three domains perceptual decision making, reward-based learning and decision making, and decision making in social contexts.
Perceptual decision making refers to choices based on available perceptual input. Especially earlier work from our lab has contributed to the literature on the neural mechanisms underlying perceptual decision making in humans. This work draws on neuroimaging and sequential-choice models.
Many of the current projects examine reward-based learning and decision making, referring to choices based on expected reward and punishment, where the best course of action often has to be learned from obtained choice outcomes. Here, we primarily use computational approaches, including Bayesian inference and reinforcement learning models, combined with neuroimaging.
Finally, decision making in social contexts shares some processes with both perceptual and reward-based decision making but often requires additional abilities, such as emotional processing and theory of mind.
Our primary ongoing projects examine:
- The computational mechanisms of reward-based learning under perceptual uncertainty
- Developmental differences in observational learning
- Memory-based reinforcement learning and decision making in younger adults and across development
- Effort-based decision making
- Lifespan differences in uncertainty-driven learning