Program

How do humans infer the causes and consequences of their actions in complex, uncertain environments? Effective decision-making depends on the ability to link outcomes to the actions or events that caused them. While this has been coined as causal inference in cognitive science, the field of machine learning refers to this ability as credit assignment.

This symposium combines computational modeling, behavioral experiments, neuroimaging, and patient studies to explore the mechanisms of credit assignment. First, Franz Wurm will discuss the importance of prediction errors and dopaminergic signaling and how a tonic manipulation via the dopaminergic precursor tyrosine affects successful credit assignment and surprise minimization. Jutta Peterburs will talk about how cerebellar dysfunction leads to impaired prediction error signaling and suggests weakened credit assignment and cognitive control during feedback and error processing. The credit assignment problem becomes especially acute in social situations with multiple agents and conflicting goals. Marco Wittmann will present insights on how abstract representations form fundamental building blocks for social-decision making and allow quick and accurate causal inference. André Forster presents EEG and computational modeling data exploring how individuals learn to distinguish between task difficulty and their capabilities in a competitive social situation. Lisa Spiering will discuss how people use behavioral flexibility to better assign outcomes to themselves versus others during cooperation and how these processes go awry in depression.

In conclusion, this symposium aims to bring together researchers studying decision-making, learning, social cognition, and those working at the intersection of machine learning, neuroscience, and clinical applications.