Amortized Bayesian Workflow for Modeling Congruency Effects Using the Diffusion Model for Conflict Tasks

The congruency effect, characterized by faster and more accurate performance in congruent compared to incongruent trials, has been consistently observed in various conflict tasks. However, the search for common underlying attentional control processes reveals significant differences between conflict tasks, as evident from different time courses of the congruency effect (i.e., delta functions) and a lack of correlations in performance across different conflict tasks. To address these issues, the Diffusion Model for Conflict Tasks (DMC) has been employed to explicitly model congruency effects and explain different delta function shapes. However, estimating the parameters of the DMC using standard iterative methods faces computational challenges. In this study, we leverage Amortized Bayesian Inference (ABI) with deep learning to address these limitations. We use different prior distributions, levels of model complexity, and automated hyperparameter optimization within a principled workflow. ABI produces well-calibrated posteriors across a wide range of trial counts, estimating individual-level parameters as accurately as iterative methods, while requiring only a fraction of their runtime once the initial training phase is complete. Additionally, we selectively manipulated irrelevant information strength in a flanker task to assess the sensitivity of DMC parameters to experimental effects. As expected, the amplitude of the automatic process was influenced by stimulus spacing in a flanker paradigm. However, the effect on the peak latency parameter depended on the choice of model parameterization and prior hyperparameters. Taken together, we demonstrate the viability of ABI as a scalable, accurate alternative to conventional estimation techniques and provide a reproducible workflow for future cognitive modeling studies.