RT Journal Article
SR Electronic
T1 Modeling Cognitive Processes with Neural Reinforcement Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 084111
DO 10.1101/084111
A1 S.E. Bosch
A1 K. Seeliger
A1 M.A.J. van Gerven
YR 2016
UL http://biorxiv.org/content/early/2016/10/29/084111.abstract
AB Artificial neural networks (ANNs) have seen renewed interest in the fields of computer science, artificial intelligence and neuroscience. Recent advances in improving the performance of ANNs open up an exciting new avenue for cognitive neuroscience research. Here, we propose that ANNs that learn to solve complex tasks based on reinforcement learning, can serve as a universal computational framework for analyzing the neural and behavioural correlates of cognitive processing. We demonstrate this idea on a challenging probabilistic categorization task, where neural network dynamics are linked to human behavioural and neural data as identical tasks are solved.