Figure 6. Model for updating the representation of a Bayesian prior based on pursuit responses in FEF_SEM.

a: A conceptual model for how FEF_SEM combines the prior on one trial with the likelihood from sensory input to set the gain of visual-motor transmission and to update the prior for the next trial. b: Blue, red, and green curves show distributions across trials of normalized pursuit-related firing rates across the population of FEF_SEM neurons for target motion at 2, 10, and 20 deg/s. c: Symbols plot the root mean squared error between model and neural data as a function of the length of the trial history that influenced the model’s prior. d, e: Open and filled symbols plot the time course of the relative change in preparatory activity between fast and slow contexts for the data and the optimized model (d) and the relative change in preparatory activity between the fast-context and the post-fast, slow, and post-slow contexts (e).