The Diffusion Decision Model (DDM; Ratcliff, 1978) is a widely used framework in cognitive modeling, providing psychologically meaningful parameters such as drift rate, boundary separation, and non-decision time to understand decision-making processes. While traditionally applied to small experimental datasets, recent studies (e.g., von Krause et al., 2022) have demonstrated its potential in analyzing large-scale datasets, offering broader insights into decision-making at the population level. In this study, we aim to apply the DDM to the EPIC-Norfolk dataset (The EPIC-Norfolk Study, 2021), a large-scale dataset that provides a unique opportunity to examine the relationship between response time (RT) measures and dementia. The dataset includes data from 8,623 participants aged 48–92 years, with dementia diagnoses tracked over a 10-year follow-up period, during which approximately 5% of participants developed dementia. Participants completed the Visual Sensitivity Task (VST), a one-choice task designed to measure visual processing speed by detecting the presence of triangles. This data-driven study aim to employ the one-boundary DDM (Ratcliff & van Dongen, 2011) to investigate drift rate and its variability, as well as other key parameters, across demographic subgroups such as age and gender. This study is the first to use trial by trial VST in an older age group thus, combining cognitive modeling to this rich dataset will provide us unique information regarding general population and dementia.