Latent Variable Learning for Joint Modeling of Choice and Response Time

Human decision-making is widely understood as a dynamic process of evaluating information and accumulating evidence over time. Represented by sequential sampling models, theory-driven computational approaches offer well-specified accounts of the dynamics that generate choice behaviors. However, because such models must be specified a priori, they typically explore a restricted set of hypothesized mechanisms and may miss structure not anticipated by the modeler. This has motivated growing interest in data-driven approaches that can complement theory-driven modeling by discovering latent structure directly from behavioral data. The present work develops an unsupervised learning framework to infer latent factors and generative structure directly from choice and response time data. The approach adopts a Neural Process architecture in which an encoder performs amortized variational inference over participant-level data to estimate latent factors, and a decoder then conditions on these participant-level latent variables together with trial-wise cues to reconstruct both choices and response times. In Simulation Study 1, we showed that this approach reconstructed choices and response times and recovered both the true dimensionality of the parameter space and the underlying parameter values. Simulation Study 2 extended these results to settings with trial-wise cues. In an application to real data, we applied the model to a large-scale, multi-task study of risk preference and identified latent risk-related factors that generalize across tasks. Together, these results introduce a data-driven framework for jointly modeling choices and response times, enabling the identification of latent structure in decision-making data beyond what is prespecified by traditional cognitive models.