A nonlinear relationship between prediction errors and learning rates in human reinforcement-learning
Fig 3
Nonlinearity in trial-wise learning rates as a function of prediction errors under an exponential-logarithmic assumption.
(A) The parameters δ and λ from Eq 7 are coarsely sampled for illustration purposes. When the parameter combinations produce horizontal lines over the PEs (i.e., the x-axis), the model is fully reduced to the Rescorla-Wagner rule with a constant learning rate (Red thicker lines, lambda = 0.005, variable delta values shown in figure). (B-C) Change in learning rate vs absolute value of prediction error (PE) trajectories when the value of one of the parameters in the model is fixed to 1. Although it is important to highlight that these parameters interact with each other to set the trajectory of the learning rate/absolute value of prediction error relationship, higher the lambda value the more sigmoidal this relationship will be as opposed to a constant learning rate, whereas higher the delta, more parabolic will be the relationship between prediction errors and learning rates.