Uncertainty–guided learning with scaled prediction errors in the basal ganglia
Fig 5
Variables of the Kalman filter and its approximations.
A Posterior variance in the Kalman filter (solid, Eq 19) and the steady–state Kalman filter (dotted, Eq 20), as a function the number of observations. Different colours correspond to different levels of process noise, and the values are plotted for the standard deviation of the observation noise of σ = 1. B The learning rate of the steady–state Kalman filter k∞ (blue, Eq 21) and the approximation (orange) which corresponds to the effective learning rate in the SPE model. We show the learning rates as a function of the observation noise σ for process noise of ν = 1.