Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.
Mischa von Krause, Stefan T. Radev · Psychological Methods · 2026
Recent advances in Bayesian modeling and deep learning have enabled scalable estimationof cognitive process models. In this paper, we present a fully Bayesian workflow thatleverages amortized inference with neural networks to rapidly estimate individualparameters and compare models from big behavioral data. Using data from a large onlineimplicit association test (IAT) sample (N > 5, 000, 000), we investigate how latentparameters, such as drift rate, boundary separation, non-decision times, and theirvariabilities, relate to key socioeconomic variables. Our exploratory findings reveal smallbut consistent associations of cognitive parameters with socioeconomic covariates. Notably,trial-by-trial variability in drift rate, often ignored in prior work, emerged as the strongestpredictor across all socioeconomic covariates. Our primary contribution lies in illustratinghow deep learning-based Bayesian estimation and model comparison can be applied tomine robust insights from large and noisy behavioral datasets. We discuss limitations andimplications for modeling individual differences in large-scale datasets and provide an openpipeline for future use. This work exemplifies how the emerging field of behavioral datascience can extend cognitive modeling to new domains and support data-driven hypothesisgeneration targeting the cognitive underpinnings of individual differences