Modelling structured trial-by-trial variability in evidence accumulation

This tutorial presents a comprehensive framework for modelling structured trial- by-trial variability in evidence accumulation models (EAMs). Traditional EAMs assume independent and identically distributed (IID) parameters across tri- als, which fails to capture the temporal dynamics and structured variability inherent in cognitive processes. We introduce Bayesian hierarchical methods to estimate EAMs that incorporate trial-level variability, offering both data- driven and theory-driven approaches. Data-driven methods describe how decision processes vary across trials using trend models, while theory-driven methods explain adaptations in response to specific factors. By moving beyond the IID assumption, our approach allows for a more precise characterisation of dynamic cognitive processes such as learning, adaptation, and fatigue. This tutorial pro- vides step-by-step guidance on implementing these methods using the EMC2 package, demonstrating their application to experimental paradigms involving choice and response time data.