A comparison of single and multiple diffusion process models of continuous-outcome decision making

Continuous-outcome decision tasks, in which responses are made on continuous scales, are increasingly used to characterize the processes involved in speeded decision making. These tasks yield entire distributions of response errors rather than just error proportions. Two diffusion process models of continuous-outcome decisions have recently been proposed, the circular diffusion model, and the spatially continuous diffusion model, which generalize the two main classes of diffusion process models of two-choice decisions to continuous spaces. The circular diffusion model, which generalizes the two-boundary Wiener model, assumes evidence is accumulated by a two-dimensional Wiener process with a vector-valued drift rate on the interior of a disk, whose bounding circle represents the decision criterion. The spatially continuous diffusion model, which generalizes the class of multiple accumulator models, assumes evidence is accumulated by a Gaussian random process with a function-valued drift rate to a criterion. We compared the ability of the models to account for the distributions of errors and response times on three perceptual tasks that manipulated both the kind and the amount of evidence entering the decision process, which produced qualitatively different patterns of response times and errors. Despite some differences in their predicted joint distributions, both models generally provided good accounts of performance in all three tasks. Our findings reinforce and extend findings from two-choice studies showing that diffusive evidence processes and not model architecture are the main determinants of successful model performance, and show the theoretical unity of diffusion models across different domains. (PsycInfo Database Record (c) 2026 APA, all rights reserved).