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http://dx.doi.org/10.19066/cogsci.2022.33.3.002

Analysis tool for the diffusion model using GPU: SNUDM-G  

Lee, Dajung (Department of Psychology, Seoul National University)
Lee, Hyosun (Cognitive Program, Seoul National University)
Koh, Sungryong (Department of Psychology, Seoul National University)
Publication Information
Korean Journal of Cognitive Science / v.33, no.3, 2022 , pp. 155-168 More about this Journal
Abstract
In this paper, we introduce the SNUDM-G, a diffusion model analysis tool with improved computational speed. Although the diffusion model has been applied to explain various cognitive tasks, its use was limited due to computational difficulties. In particular, SNUDM(Koh et al., 2020), one of the diffusion model analysis tools, has a disadvantage in terms of processing speed because it sequentially generates 20,000 data when approximating the diffusion process. To overcome this limitation, we propose to use graphic processing units(GPU) in the process of approximating the diffusion process with a random walk process. Since 20,000 data can be generated in parallel using the graphic processing units, the estimation speed can be increased compared to generating data through sequential processing. As a result of analyzing the data of Experiment 1 by Ratcliff et al. (2004) and recovering the parameters with SNUDM-G using GPU and SNUDM using CPU, SNUDM-G estimated slightly higher values for certain parameters than SNUDM. However, in term of computational speed, SNUDM-G estimated the parameters much faster than SNUDM. This result shows that a more efficient diffusion model analysis for various cognitive tasks is possible using this tool and further suggests that the processing speed of various cognitive models can be improved by using graphic processing units in the future.
Keywords
diffusion model; GPU; random walk;
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