Fig. 2.

The interface between model and process This Bayesian network shows how the generative process (filled circles) gives rise to sensory data, and how the generative model (unfilled circles) proposes this data is generated. Arrows connecting two variables indicate that the second variable is conditionally dependent on the first. Note that, as described in the main text, action of the extraocular muscles (EOM) in the real world causes changes in velocity (i.e. accelerations); while fictive fixation locations cause changes in position in the generative model. The relationship between the vectors in this graph and the variables of Fig. 1 are shown on the right.