English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
There are no MPG-Authors available
External Ressource
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Frank, M., Gagne, C., Nyhus, E., Masters, S., Wiecki, T., Cavanagh, J., et al. (2015). fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning. The Journal of Neuroscience, 35(2), 485-494. doi:10.1523/JNEUROSCI.2036-14.2015.


Cite as: http://hdl.handle.net/21.11116/0000-0007-6168-7
Abstract
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 described by a drift diffusion model (DDM) of decision making in which 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 modulated by trial-by-trial measures of decision conflict and activity in the dorsomedial frontal cortex (pre-SMA BOLD and mediofrontal theta in EEG). These findings provide converging multimodal evidence for a model in which decision threshold in reward-based tasks is adjusted as a function of communication from pre-SMA to STN when choices differ subtly in reward values, allowing more time to choose the statistically more rewarding option.