Figure 4.

GDDM components. Our GDDM included extensions related to timing and reward. a, We modeled timing using two types of urgency signals. We implemented a gain function and collapsing bounds, each with and without a time delay. Constant gain and constant bounds indicate the absence of an urgency signal. Red represents bounds. Blue represents gain functions. b, Reward mechanisms are shown with example decision variable trajectories for each mechanism. Initial bias: the integrator starts biased toward the large-reward target, and the leaky integrator decays back to this starting position instead of to the origin. Time-dependent bias: there is a gradual increase in baseline evidence toward the large-reward target over time. Mapping error: once a decision is reached, the monkey chooses the opposite target on a percentage of trials. Lapse error: there is a higher, exponentially distributed probability of making an evidence-independent choice to the large-reward target at any given point throughout the trial, contrasted to equal probabilities in the absence of this mechanism.