The statistical structures of reinforcement learning with asymmetric value updates

DOI10.1016/J.JMP.2018.09.002zbMath1411.91469OpenAlexW2895764062WikidataQ129179277 ScholiaQ129179277MaRDI QIDQ1736016

Kentaro Katahira

Publication date: 29 March 2019

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

Full work available at URL: https://doi.org/10.1016/j.jmp.2018.09.002

zbMATH Keywords

logistic regressionreinforcement learningmodel fittinglearning rateasymmetric value updatechoice perseverance

Mathematics Subject Classification ID

Memory and learning in psychology (91E40) Applications of statistics to psychology (62P15)

Related Items (2)

A Normative Account of Confirmation Bias During Reinforcement Learning ⋮ Biases in estimating the balance between model-free and model-based learning systems due to model misspecification

Uses Software

• Unnamed Item
• lme4
• Rsolnp
• hBayesDM

Cites Work

• Empirical priors for reinforcement learning models
• How hierarchical models improve point estimates of model parameters at the individual level
• The relation between reinforcement learning parameters and the influence of reinforcement history on choice behavior
• Estimating the dimension of a model
• Model-based estimation of subjective values using choice tasks with probabilistic feedback
• A new look at the statistical model identification

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