Fitting a Drift–Diffusion Item Response Theory Model to Complex Cognition Response Times

Drift–Diffusion Models (DDMs) have been widely successful in modeling fast decision response times. DDM describes the underlying (cognitive) decision process as a function of a diffusion process drifting toward a decision threshold. A few studies have shown that introducing within-trial variability in DDM parameters or describing DDM parameters as a function of item properties improves the DDM model fit for response times of the Complex Decision Task (CDT) as well. One such extension of DDM is the item response theory-based Q-diffusion model (QDM). QDM has been successful in modeling response times of CDT such as chess ability assessment. The current study further examined whether QDM can fit response times corresponding to certain problem-solving tasks. First, the drift rate parameter of standard DDM was extended to approximate the within-trial variability in the reasoning process as discussed in existing meta-reasoning studies that examine such within-trial dynamics for problem-solving tasks. Then, the response times were simulated using the standard DDM and the mentioned extension of the standard DDM. Then, the goodness of fit of QDM was examined using a Bayesian model fit method—Posterior Predictive Check (PPC). PPC analysis revealed that the fitted QDM was able to effectively describe the simulated response time mean. However, the fitted QDM was not able to describe the simulated response time variance.