On the efficiency of neurally-informed cognitive models to identify latent cognitive states

DOI10.1016/j.jmp.2016.06.007zbMath1396.91618OpenAlexW2477208937MaRDI QIDQ2407644

Andrew Heathcote, Birte U. Forstmann, Matthias Mittner, Guy E. Hawkins

Publication date: 6 October 2017

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

Full work available at URL: https://hdl.handle.net/10037/10466

zbMATH Keywords

simulationcognitive modelbehavioral datamodel recoveryneural data

Mathematics Subject Classification ID

Decision theory (91B06) Cognitive psychology (91E10) Applications of statistics to psychology (62P15)

Related Items (3)

Model-based cognitive neuroscience ⋮ Estimating across-trial variability parameters of the diffusion decision model: expert advice and recommendations ⋮ A tutorial on joint models of neural and behavioral measures of cognition

Uses Software

• R
• BayesDA
• rtdists
• HDDM

Cites Work

• Hierarchical Bayes models for response time data
• How cognitive modeling can benefit from hierarchical Bayesian models
• Advances in prospect theory: cumulative representation of uncertainty
• How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters
• Bayesian Measures of Model Complexity and Fit
• The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks
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