Figure 1.

Schematics of the (A) linear ballistic accumulator (LBA) and (B) diffusion decision model (DDM), which are commonly applied sequential sampling models in the accumulator-type class and random-walk class, respectively. In both illustrations, the models describe a task in which an individual must decide whether a presented arrow is pointing to the left or the right. The LBA assumes that accumulators for the correct choice (right, in green) and incorrect choice (left, in red) start at a level drawn from a uniform distribution between 0 and parameter A and proceed to gather evidence at linear and deterministic rates over time as they race toward an upper response threshold, set at parameter b. The rates of evidence accumulation on individual trials, represented by the light green and light red traces, are drawn from normal distributions with a mean of v (represented by the green, v_right, and red, v_left, arrows) and a standard deviation of sv. The DDM instead assumes a single decision variable that represents the relative amount of evidence for each of the two possible choices (e.g., evidence for right vs. left; these models are typically applied to two-choice decisions). This variable begins at parameter z and drifts over time between boundaries for each possible response, set at 0 (for left) and parameter a (for right). The drift process on individual trials, represented by the light blue traces, is stochastic and moves toward the boundary for the correct choice at an average rate of v (represented by the blue arrow, v_right–left). Efficiency of evidence accumulation, defined as the rate at which an individual is able to gather relevant evidence from the environment to make accurate choices, can be measured in the LBA by subtracting the average accumulation rate for the incorrect choice (v_left) from that of the correct choice (v_right). Efficiency of evidence accumulation is also measured by the DDM’s single average drift rate parameter (v_right–left). Individuals’ level of response caution (i.e., speed/accuracy trade-off) can be indexed by parameters that represent the distance evidence accumulators must travel to trigger a response in both the LBA (parameter b) and DDM (parameter a). Both models also include parameters for time taken up by perceptual and motor processes peripheral to the decision, t0 and Ter, respectively.