One important modeling decision I made during the trial-by-trial behavioral adaptation project was to reconsider the role of Bayesian modeling in the analysis. I planned initially to include a Bayesian mixed-effects model alongside the frequentist analysis. The goal was to provide a complementary approach and examine whether the main conclusions were consistent across the two methods.
During the modeling phase, however, the Bayesian model became computationally demanding. The dataset contained a large number of trial-level observations and required a hierarchical structure with participant-level variation and nonlinear trial effects. Fitting and evaluating the Bayesian model required much more time and computational resources than the primary frequentist mixed-effects models.
This made me reconsider whether the Bayesian model would meaningfully improve the study. A second modeling approach is useful when it provides additional scientific value. In this case, the main research question could be addressed directly with frequentist mixed-effects models, while the Bayesian model did not provide sufficient additional scientific value to justify its computational complexity.
I removed the Bayesian model from the final analysis and focused the study on frequentist mixed-effects modeling. This kept the analysis closer to the research question and made the project more manageable and reproducible. I will continue developing my Bayesian modeling skills and use them in future research where a Bayesian or computational modeling approach can provide greater scientific value.