en; 2018 ; Schweickert & Zheng, 2019 ). MPT models for discrete and continuous variables (MPT-DC) are process models with a theoretical rationale that assumes that processes have intrinsic attributes (e.g., processing time), and modeling these variables can help to identify and measure these attributes and their theoretical sources. Therefore, MPT-DC have both methodological and theoretical benefits. These models could yield different conclusions than models that do not jointly analyze quantitative and categorical variables. Furthermore, they allow us to make new inferences about the structure and functioning of cognitive models, study the relationships between dependent variables including trade-offs and lead to a finer understanding of how individuals process information and respond accordingly. Quantitative variables, such as RT (Heck & Erdfelder, 2016 , 2020 ; Klauer & Kellen, 2018 ) and both RT and CLs jointly (Starns, 2021 ) have been included in 2HT models through MPT-DC models. Here we propose to include both RT and CLs jointly in SDT models by a solution based on reparametrizing SDT as multinomial models (estimating SDT models as multinomial models has previously been proposed by Singmann & Kellen, 2013 ). To the best of our knowledge, the extension of both MPT-DC and SDT models to RT and CL data has not yet been developed. In short, MPT-DC allows us to (1) solve methodological problems resulting from studying categorical and continuous variables separately; (2) study discrete latent cognitive states (e.g., as in 2HT models) and the latent continuous variables associated with these states; and (3) estimate parameters of SDT models that include ordinal and continuous variables. The main purpose of the present study is to highlight the advantages of incorporating quantitative variables in both 2HT and SDT models. More specifically, we will try to widen the range of research questions related to these models and to make more accurate conclusions about them. To

Extensions of multinomial processing tree models for continuous variables: A simulation study comparing parametric and non-parametric approaches | Behavior Research Methods | Springer Nature Link # Extensions of multinomial processing tree models for continuous variables: A simulation study comparing parametric and non-parametric approaches - Published: 08 December 2025 - Open access - Original Manuscript - Cite this article - Volume 58, article number 22, (2026) You have full access to this open access article Download PDF Save article View saved research Behavior Research Methods Aims and scope Submit manuscript ## Abstract Both parametric and non-parametric extensions of the multinomial processing tree (MPT) models have been proposed for jointly modeling discrete and continuous variables. Since the two approaches have not yet been compared systematically, we assess their power and robustness in three simulation studies focusing on the weapon identification task. In this context, two statistically equivalent MPT models have been proposed, namely, the preemptive-conflict-resolution model (PCRM) and the default-interventionist model (DIM), which differ only in their assum