Scaling Cognitive Modeling to Big Data: A Deep Learning Approach to Studying Individual Differences in Evidence Accumulation Model Parameters

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