How cognitive modeling can benefit from hierarchical Bayesian models.

By Michael D. Lee. 2011

317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/0995ec153211ec97bf93a8fcebffd12e7f8457a2

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In Artificial Intelligence

In Developmental and Educational Psychology

Citations in the corpus

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).”
Bayesian Cognitive Modeling: A Practical Course (2014, methodology): “• Lee, M. D. & Sarnecka, B. W. (2010). A model of knower-level behavior in numberconcept development.”
A Tutorial on Bayesian Nonparametric Models (2011, background): “Lee (2010) provides an overview for cognitive psychologists. process (Thibaux and Jordan, 2007) have been developed, allowing an infinite number of latent components to be shared by multiple individuals.”
A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. (2014, background): “These advantages are not unique to the model presented here, they are inherent to hierarchical models (Gelman & Hill, 2007; Lee, 2011).”
Mathematical Modeling (2012, unknown): “bayesian modeling The term Bayesian model has become somewhat of a buzz phrase in recent years, and it is now used very broadly in reference to any model that takes advantage of the Bayesian statistical approach to processing information (Chater, Tenenbaum, & Yuille, 2006; Kruschke, 2010; Lee, 2011)”
Ten simple rules for the computational modeling of behavioral data (2019, background): “…hierarchical Bayesian approaches make it possible to fit all participants simultaneously, integrating assumptions about their dependence (e.g. one single group, multiple groups, effects of covariates of interest such as age and so on; Lee, 2011; Lee and Wagenmakers, 2014; Wiecki et al., 2013).”
Using MCMC chain outputs to efficiently estimate Bayes factors (2011, background): “These tools allow model builders to easily obtain approximate samples frommarginal posterior distributions formany useful and relevant models in psychological science (Lee, 2011).”