T&C Chen Center for Social and Decision Neuroscience Seminar

Abstract: The reinforcement learning and decision-making framework has significantly advanced our understanding of the neurocognitive processes underlying human behavior and psychopathology. However, traditional laboratory paradigms often fail to accurately simulate real-world behaviors and rewards due to their oversimplified contexts and limited ecological validity. To overcome these limitations, my lab has integrated naturalistic paradigms and natural rewards directly into neuroimaging studies.

In one line of research, we employed naturalistic tasks such as real-time driving and video watching paradigms combined with computational approaches to elucidate individual differences in impulsivity and addiction. Specifically, using an inverse reinforcement learning (IRL) algorithm integrated with deep neural networks during a real-time driving task, we successfully inferred dynamic reward values and their neural correlates with fMRI. Additionally, employing naturalistic video-watching paradigms among alcohol users, we observed that individual schemas about alcohol influenced neural synchrony and self-reported craving, with schema alignment significantly mediating craving-related neural responses. In another series of studies, we utilized an MRI compatible vaping device to directly investigate neural processing of primary drug rewards in regular smokers. Preliminary results revealed unique and shared neural signatures of monetary and real rewards.

Collectively, these studies exemplify the potential of naturalistic paradigms and advanced computational approaches to simulate real-world situations and characterize individual differences, offering novel insights into addiction.