PT  - JOURNAL ARTICLE
AU  - Bosch, S.E.
AU  - Seeliger, K.
AU  - van Gerven, M.A.J.
TI  - Modeling Cognitive Processes with Neural Reinforcement Learning
AID  - 10.1101/084111
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 084111
4099  - http://biorxiv.org/content/early/2016/10/29/084111.short
4100  - http://biorxiv.org/content/early/2016/10/29/084111.full
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.