research

The main focus of my postdoctoral work is motivation in the context of reinforcement learning. My recent projects include computational modeling of clinical data during learning tasks, building computational models of behavior and value, and investigating the neural representation of changes in motivation.

Current & recent projects

Reinforcement learning

Reinforcement learning (RL) is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time.

How does uncertainty of reward affect impulsive choice behavior? (Burk & Averbeck, 2023)

Motivational disengagement

Motivational disengagement is the process by which an organism stops pursuing a goal, even when the reward is still available. Everyone experiences moments of losing drive despite knowing the payoff. In severe cases, this loss becomes debilitating and requires psychiatric care. We are asking related questions:

How does reward affect motivation during learning? (Burk et al., 2024)
How does reward predict, or not predict, motivational disengagement? (Burk & Averbeck, 2026)

Neural representation

We are interested in the neural representation of learning processes during reinforcement learning tasks. We use a multi-site, multi-probe recording strategy to record neural activity from a range of deep structures shown to be involved in learning and motivation.

Computational psychiatry

There has been a recent focus on using computational methods, behavior, and clinical data together to predict symptom severity and treatment outcomes for a range of clinical disorders (Huys et al., 2016). We have been collaborating with clinical labs to use reinforcement learning models to relate behavior to clinical metrics in various ways:

How does reward processing change in children diagnosed with clinical irritability? (Korom, Burk, et al., 2026)
How does brain structure relate to safety learning and anxiety? (Abend, Burk et al., 2022)