Behavioral/Cognitive fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning

What are the neural dynamics of choice processes during reinforcement learning? Two largely separate literatures have examined dynamics of reinforcement learning (RL) as a function of experience but assuming a static choice process, or conversely, the dynamics of choice processes in decision making but based on static decision values. Here we show that human choice processes during RL are well describedby adrift diffusionmodel (DDM)of decisionmaking inwhich the learned trial-by-trial reward values are sequentially sampled, with a choice made when the value signal crosses a decision threshold. Moreover, simultaneous fMRI and EEG recordings revealed that this decision threshold is not fixed across trials but varies as a function of activity in the subthalamic nucleus (STN) and is further modulatedby trial-by-trialmeasures of decision conflict andactivity in thedorsomedial frontal cortex (pre-SMABOLDandmediofrontal theta in EEG). These findings provide convergingmultimodal evidence for amodel in which decision threshold in reward-based tasks is adjusted as a function of communication frompre-SMA to STNwhen choices differ subtly in reward values, allowingmore time to choose the statistically more rewarding option. Key words: basal ganglia; decision making; drift diffusion model; prefrontal cortex; subthalamic nucleu