Hierarchical Bayesian mixture models of processing architectures and stopping rules

DOI10.1016/j.jmp.2019.04.005zbMath1437.91368OpenAlexW2944528797WikidataQ127888360 ScholiaQ127888360MaRDI QIDQ2176774

Nathan J. Evans, Gabriel Tillman

Publication date: 5 May 2020

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

Full work available at URL: https://doi.org/10.1016/j.jmp.2019.04.005

zbMATH Keywords

mixture modelsBayesian methodscognitive modellingsystems factorial technology

Mathematics Subject Classification ID

Bayesian inference (62F15) Cognitive psychology (91E10)

Related Items

Editorial on developments in systems factorial technology: theory and applications

Uses Software

• powder
• jpnb4

Cites Work

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• Nice guys finish fast and bad guys finish last: Facilitatory vs. inhibitory interaction in parallel systems
• Semiparametric Bayesian approaches to systems factorial technology
• A practical guide to the probability density approximation (PDA) with improved implementation and error characterization
• Understanding the influence of distractors on workload capacity
• Systems factorial technology provides new insights on global-local information processing in autism spectrum disorders
• Serial and within-stage independent parallel model equivalence on the minimum completion time
• A hierarchical Bayesian approach to distinguishing serial and parallel processing
• Spatio-temporal properties of elementary perception: an investigation of parallel, serial, and coactive theories
• The importance of complexity in model selection
• Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: a tutorial
• SOME RESULTS CONCERNING THE IDENTIFIABILITY OF PARALLEL AND SERIAL PROCESSES