One of the implicit assumptions of the Drift-Diffusion-Model (DDM) is that samples are weighted equally, regardless of their magnitude or the time at which they are sampled. Other sequential sampling models, like the leaky competing accumulator, incorporate a leaky parameter that weights early and late information differently. However, it is also possible that valence and magnitude of the stimuli play a crucial, non-linear role in determining the speed of information processing (i.e., the drift rate). We use a yes-no task with sequentially-presented information from Gaussian bandits to explore factors that may influence the drift rate parameter in the DDM. Exploratory analysis is still in progress.