Computational Psychiatry

TD Learning model simulation

By TD learning, the agent learns from future rewards and back-propagates prediction errors by updating estimation values(keep updating beliefs of future rewards at every moment approaching the future). It is one of the core concepts of model-free reinforcement learning.

Rescorla–Wagner model simulation

The Rescorla-Wagner rule is based on a simple linear prediction of the reward associated with a stimulus. R-W model captures critical aspects of Pavlovian experiment(classical conditioning).