Figure 1.

The Diffusion Decision Model (DDM; taken with permission from Matzke and Wagenmakers, 2009). The DDM assumes that noisy information is accumulated over time from a starting point until it crosses one of the two response boundaries and triggers the corresponding response. The gray line depicts the noisy decision process. “Response A” or “Response B” is triggered when the corresponding boundary is crossed. The DDM assumes the following main parameters: drift rate (v), boundary separation (a), mean starting point (z), and mean non-decision time (T_er). These main parameters can vary from trial to trial: across-trial variability in drift rate (s_v), across-trial variability in starting point (s_z), and across-trial variability in non-decision time (s_Ter). Starting point can be expressed relative to the boundary in order to quantify bias, where $z_r=\frac{z}{a}=0.5$ indicates unbiased responding. Similarly, across-trial variability in starting point can be expressed relative to the boundary: s_zr = $\frac{s_z}{a}$.