Sangil Lee

The paper examines sensory-WM gradients in frontal cortex, finding that sensory and working memory regions overlap and form gradients rather than sharp boundaries.

This study investigates reduced model-based control in gambling disorder using reinforcement learning tasks, computational modeling, and fMRI, finding intact neural representations of task structure despite behavioral impairments.

The paper manipulates subjective time perception and shows its effects on delay discounting impulsivity and associated neural activity.

The paper improves the validity of neural predictors of deception by identifying and removing confounding signals using novel machine learning methods.

This paper provides behavioral and electrophysiological evidence of sub-second updates in reward predictions using slot machine outcomes.

The paper investigates how the brain represents and updates the value of information (VOI) during decision-making using fMRI and a modified beads task.

The paper investigates how different subcomponents of the default mode network (DMN) respond to the vividness and valence of imagined events, finding dissociable neural responses.

The paper shows that vmPFC activity during intertemporal choice better reflects subjective value than a hexagonal gridlike code.

The paper develops an fMRI-based brain marker predicting individual differences in delay discounting using machine learning, showing significant prediction accuracy in two independent studies.

The paper develops a neural signature to measure the concreteness of prospective thought and shows it decreases with temporal distance in intertemporal decision-making.

This paper introduces a method to build interpretable whole-brain neural decoders that are computationally efficient and scalable.

The paper investigates how the brain represents task-dependent errors during adaptive learning using fMRI and multi-voxel pattern classification.

The paper was withdrawn due to concerns about the reported results, and the authors no longer support the findings.

This paper shows that run-level mean centering improves multivoxel pattern classification accuracy by correcting spurious shifts in activation estimates across scanner runs.