Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Cognitive models can estimate latent parameters from reaction time and accuracy data, capturing aspects like processing speed and decision caution. We examined how these parameters relate to real-world outcomes—education, income, and job prestige in a large online sample. While overall effects were small, variability in processing speed (drift rate) was the strongest and most consistent predictor, highlighting the value of behavioral data science for generating new hypotheses about overlooked measures in cognitive modeling.