The drift diffusion model as the choice rule in reinforcement learning.

ABSTRACT: Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could captu

SUBMITTER: Pedersen ML 

PROVIDER: S-EPMC5487295 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature