A Joint Cognitive Latent Variable Model for Binary Decision-making Tasks and Reaction Time Outcomes

Traditionally, in cognitive modeling for binary decision-making tasks, stochastic differential equations, particularly a family of diffusion decision models, are applied. These models suffer from difficulties in parameter estimation and forecasting due to the non-existence of analytical solutions for the differential equations. In this paper, we introduce a joint latent variable model for binary decision-making tasks and reaction time outcomes. Additionally, accelerated Failure Time models can be used for the analysis of reaction time to estimate the effects of covariates on acceleration/deceleration of the survival time. A full likelihood-based approach is used to obtain maximum likelihood estimates of the parameters of the model.To illustrate the utility of the proposed models, a simulation study and real data are analyzed.