A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods

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Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package (2016, methodology): “…and decision sciences further conﬁrmed the validity and usefulness of HBA and other hierarchical approaches (e.g., Ahn et al., 2014; Guitart-Masip et al., 2012; Huys et al., 2011; Katahira, 2016; Lee, 2011; Raja Beharelle, Polania, Hare, & Ruff, 2015; Shiffrin, Lee, Kim, & Wagenmakers, 2008).”
An introduction to good practices in cognitive modeling (2015, background): “In a Bayesian framework the Bayes factor is the ideal criterion [54], but this is rarely easy to directly compute for cognitive process models (although see [55, 56]).”
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013, background): “Under the assumption that participants within each group are similar to each other, but not identical, a hierarchical model can be constructed where individual parameter estimates are constrained by group-level distributions (Shiffrin et al., 2008; Nilsson et al., 2011).”
How cognitive modeling can benefit from hierarchical Bayesian models. (2011, background): “2, the structure in individual differences is directly captured by the process g and its parameters ψ. Shiffrin et al. (2008) provide a worked tutorial example for the case of memory retention.”