Figure 4.

Graphical representations of the three hierarchical Bayesian models following the convention of Lee and Wagenmakers (2014). Each node represents a variable in the model with arrows indicating what variables are influenced by other variables. The magenta 2 ∗ 1 vector of reaction time and accuracy y_ijk and the blue (p + 1) ∗ 1 vector of p EEG regressors (+1 intercept) x_ijk are observed variables, as indicated by the shaded nodes. Bolded blue variables indicate (p + 1) ∗ 1 vectors, such as the subject j level effects ${\gamma }_{jk}^{\ast }$ of each EEG regressor and the condition k level effects μ_(γ∗)k of each EEG regressor. In Model 3 for each trial i, values of non-decision time τ_ijk, drift rate (evidence accumulation rate) δ_ijk, the diffusion coefficient (evidence accumulation variance) ς_ijk are deterministic linear combinations of single-trial EEG regressors x_ijk and the effects of those regressors ${\gamma }_{jk}^{\ast }$ that vary by subject and condition.