This section showcases my research projects focused on understanding cognition and brain function by integrating computational modeling with neuroimaging, bridging theoretical insight and experimental data.
TMS OFC fMRI
Why do people naturally shift their preference toward dessert after a savory meal—even without any external cues? This subtle yet reliable change suggests that our brain is capable of internally tracking shifts in physiological state and updating reward preferences accordingly. In this project, I explored the neural basis of such dynamic preference changes. Using non-invasive brain stimulation—transcranial magnetic stimulation (TMS)—I investigated how different subregions of the orbitofrontal cortex (OFC) contribute to this flexible adaptation.
TMS OFC fMRI
How does the brain figure out what went wrong when our expectations about rewards are mistaken? In this study, we combined non-invasive brain stimulation (TMS) and fMRI to examine how different brain regions talk to each other when learning about reward identity—such as expecting chocolate but receiving vanilla instead. We found that when communication networks in the orbitofrontal cortex (a region that tracks what we expect) were temporarily disrupted, the midbrain’s learning signals were also altered.
decision-making model development fMRI
How does the brain turn complex sensory information into decisions? During my PhD, I developed a model-based framework that links behavior and brain activity by integrating computational decision models with fMRI data. Focusing on two-alternative choice tasks, this approach embeds cognitive processes like evidence accumulation into a dynamical system. I validated the model using extensive simulations and Bayesian parameter recovery, demonstrating its ability to reveal interpretable and robust connections between neural signals and latent decision-making mechanisms.