Woo‐Young Ahn

The paper examines how individual alcohol drinking motives influence neural and behavioral craving responses to naturalistic alcohol-drinking videos.

This study uses inverse reinforcement learning to identify value signals in the brain during naturalistic decision making, finding strong associations with dorsal striatum activity.

This study examines how Pavlovian bias affects smoking cessation success and neurocognitive changes during nicotine withdrawal, finding that higher bias correlates with treatment failure and relapse risk.

This study examines longitudinal changes in brain network functional connectivity during smoking cessation, finding that successful quitters show normalization of pre-quit connectivity in salience-executive and salience-default mode networks.

This study compares neural correlates of model-based decision-making in internet gaming disorder and alcohol use disorder using fMRI and computational modeling.

This study investigates how working memory load affects instrumental learning under Pavlovian influence, finding that it slows learning and increases randomness without altering Pavlovian bias.

This study finds that daily caffeinated soda intake in preadolescent children is linked to neurobehavioral risk factors for substance misuse and predicts increased alcohol sipping one year later.

This study uses tDCS to modulate the dorsolateral prefrontal cortex and finds reduced Pavlovian bias in the punishment domain, suggesting a causal role in balancing decision-making systems.

This study uses generative models and facial expression analysis to show that negative affect enhances learning of counterfactual representations and behavioral change.

This study evaluates a Bayesian adaptive design optimization method for measuring delay discounting, showing higher reliability, precision, and efficiency compared to a conventional staircase method across three populations.

Easyml provides a unified framework for building and evaluating machine learning models in R and Python, with standardized recipes and automated reporting.

The paper introduces hBayesDM, an R package for computational modeling of reinforcement learning and decision-making tasks, enabling user-friendly Bayesian analysis and visualization.