A nonlinear relationship between prediction errors and learning rates in human reinforcement-learning
Fig 5
The learning rates estimated under each reinforcement-learning model.
(A) Hybrid Pearce-Hall model estimates higher learning rates relative to the Rescorla-Wagner model, whereas cubic and exponential-logarithmic models estimate overall lower learning rates. (B) The relationship between PEs and learning rates estimated by the exponential-logarithmic model reveals a nonlinear trajectory very similar to what is proposed by the cubic model. Volatility of different task blocks did not seem to influence learning rate trajectories in terms of how absolute values of the PEs tended to influence learning rates. This relationship was similarly exponential in form across both stable and volatile task-blocks.