Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals such is to formalize theories using parameters that represent distinct processes. We argue systematic quantitative reviews parameter estimates can make an important contribution robust and cumulative modeling. Parameter benefit model development assessment by providing valuable information about expected space, facilitate more efficient design Importantly, provide crucial—if not indispensable—information specification informative prior distributions in Bayesian From perspective, integral part a model, reflecting theoretical knowledge plausible values model's (Lee, 2018 ). In this paper we illustrate how be implemented generate informed Diffusion Decision Model (DDM; Ratcliff McKoon, 2008 ), most widely used speeded decision making. surveyed published literature on empirical applications DDM, extracted reported estimates, synthesized form distributions. Our review establishes comprehensive reference resource DDM various experimental paradigms guide future model. Based challenges faced during review, formulate set general DDM-specific suggestions aiming increase reproducibility gained process.