Figure 2:

Hierarchical Bayesian model. The graphical model depicts the hierarchical PD control modeling approach. Shaded circles represent data, while non-shaded circles represent parameters. At each timepoint within a trial, acceleration depends on error and derivative of the error according to the PD control model. K_p and K_d for each trial represent the coefficients of the error term and derivative term, respectively. Trial-level K_p and K_d are drawn from subject-level means (α_p and α_d), which themselves are drawn from group-level means (μ_p, and μ_d). Depending on the particular model, α_p and α_d can be related to individual-level predictors such as self-report scores, demographics, and brain region volumes. The σ terms represent standard deviations. See Methods section for details of the modeling approach.