Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution. We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity—the beta-MPT approach (Smith & Batchelder, Journal of Mathematical Psychology 54:167-183, 2010) and the latent-trait MPT approach (Klauer, Psychometrika 75:70-98, 2010). TreeBUGS reads standard MPT model files and obtains Markov-chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within- and between-subjects comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent-trait MPT model.

The diffusion model introduced by Ratcliff (Psychol Rev 85:59–108, 1978) has been applied to many binary decision tasks including recognition memory. It describes dynamic evidence accumulation unfolding over time and models choice accuracy as well as response-time distributions. Various parameters describe aspects of decision quality and response bias. In three recognition-memory experiments, the validity of the model was tested experimentally and analyzed with three different programs: fast-dm, EZ, and DMAT. Each of three central model parameters was targeted via specific experimental manipulations. All manipulations affected mainly the corresponding parameters, thus supporting the convergent validity of the measures. There were, however, smaller effects on other parameters, showing some limitations in discriminant validity.

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Intentional forgetting of information that has recently been encoded is regarded an active and adaptive process and is widely studied using the item-method or the list-method directed forgetting (DF) paradigm. In the present research, we tested whether inter-individual differences in working-memory capacity (WMC), that have been identified as a relevant predictor of DF within the list method, are also related to stronger DF effects within the item method. Furthermore, we investigated relationships between WMC and item-method DF at different processing stages by applying the multinomial storage-retrieval model hierarchically to our data. Results showed that individuals with high WMC are better able to store to-be-remembered information than individuals with low WMC, whereas WMC was not related to retrieval of to-be-remembered information or to either storage or retrieval of to-be-forgotten information. Implications for theoretical accounts of item-method DF are discussed.