A Bayesian hierarchical mixture approach to individual differences: case studies in selective attention and representation in category learning

DOI10.1016/j.jmp.2013.12.002zbMath1309.91118OpenAlexW2067113683MaRDI QIDQ396240

Mohammad Hasan, H. S. Yoon

Publication date: 8 August 2014

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2013.12.002

zbMATH Keywords

parameter estimationmodel selectionBayesian methodhierarchical mixture modelindividual differencescategory learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Cognitive psychology (91E10) Memory and learning in psychology (91E40) Applications of statistics to psychology (62P15)

Related Items (7)

Bayes factors, relations to minimum description length, and overlapping model classes ⋮ Prototypes, exemplars and the response scaling parameter: a Bayes factor perspective ⋮ Algebraic aspects of Bayesian modeling in psychology ⋮ A hierarchical Bayesian approach to assess learning and guessing strategies in reinforcement learning ⋮ An application of multinomial processing tree models and Bayesian methods to understanding memory impairment ⋮ A cognitive latent variable model for the simultaneous analysis of behavioral and personality data ⋮ A Bayesian approach to modeling group and individual differences in multidimensional scaling

Uses Software

• WinBUGS
• BUGS
• BayesGCM
• BayesDA

Cites Work

• Unnamed Item
• Unnamed Item
• How cognitive modeling can benefit from hierarchical Bayesian models
• Relations between prototype, exemplar, and decision bound models of categorization
• A general latent assignment approach for modeling psychological contaminants
• On the interaction between exemplar-based concepts and a response scaling process
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Modeling individual differences using Dirichlet processes

This page was built for publication: A Bayesian hierarchical mixture approach to individual differences: case studies in selective attention and representation in category learning