A diffusion-based framework for modelling systematic, time-varying cognitive processes

As people engage in tasks over extended periods, their psychological states change systematically due to factors such as practice, learning, and/or boredom. However, the dominant frameworks for modeling cognitive processes, such as evidence accumulation models, only consider a single estimate of a process across the duration of an experiment. Our study describes, develops, and assesses the ParAcT-DDM framework: the Parameters Across Time Diffusion Decision Model, which unifies previous modeling efforts from practice and decision-making research. Specifically, our framework models time-varying changes to diffusion decision model parameters by assuming that rather than being constant across time, their estimates follow theoretically informed time-varying (e.g., trial-varying or block-varying) functions. Focusing on two diffusion model parameters: drift rate (task efficiency) and threshold (caution), our empirical results show that ParAcT-DDM variants vastly outperform the standard diffusion model in four existing data sets, including one where participants completed a practice block before data recording began, suggesting that time-varying cognitive processes often occur in typical cognitive experiments, even when the experimental design explicitly tries to remove practice effects. Finally, we find that the existence of time-varying processes causes systematic biases in the parameter estimates of the standard diffusion model, suggesting that our ParAcT-DDM framework can be crucial to ensuring the robustness of inferences against time-varying changes, regardless of whether these changes are of direct interest.