I had a fantastic time presenting my research at the 2024 Meeting of the Society for Mathematical Psychology in Tilburg, Netherlands. My participation was supported by the Women of MathPsych - Professional Representation for Inclusivity and Minority Empowerment (WoMP-PRIME) Travel and Networking Award, which provided valuable opportunities to connect with leading researchers in mathematical psychology.
My talk was titled “An EZ Bayesian hierarchical drift diffusion model for response time and accuracy”. It presented the Hierarchical Bayesian implementation of the EZ-drift diffusion model (EZDDM) we developed in our paper:
The model uses binomial and normal distributions to model the sampling distributions of key EZ-DDM summary statistics, enabling versatile extensions to hierarchical models with latent variable and metaregression structures. Our “EZ Bayesian hierarchical drift diffusion model” (EZBHDDM) serves as a hyper-efficient proxy model to the hierarchical DDM that can be implemented in any probabilistic programming language.
Through simulation studies and applied examples using the graphical Bayesian analysis package JASP, we demonstrated the efficacy and efficiency of our model in real-world applications, showing particular robustness in recovering critical regression parameters from models with metaregression structure.