Bayesian modeling of the Mnemonic Similarity Task using multinomial processing trees

The Mnemonic Similarity Task (MST, Stark et al. in Neuropsychologia 51:2442–2449. https://doi.org/10.1016/j.neuropsychologia.2012.12.014 ) is a modified recognition memory task designed to place strong demand on pattern separation. sensitivity and reliability of the MST make it an extremely valuable tool clinical settings. We develop new cognitive models, based multinomial processing tree framework, for two versions MST. models are implemented as generative probabilistic applied behavioral data using Bayesian graphical modeling methods. demonstrate how combination methods allows flexible powerful inferences about performance These demonstrations include latent-mixture extensions identifying individual differences decision strategies, hierarchical that measure fine-grained ability detect lures. One key finding availability “similar” response reduces strategies more direct measurement memory.