TY - JOUR
T1 - Bayesian Decision Models
T2 - A Primer
AU - Ma, Wei Ji
N1 - Funding Information:
This primer is based on a Bayesian modeling tutorial that I have taught in several places. Thanks to all students who actively participated in these tutorials. Special thanks to my teaching assistants of the Bayesian tutorial at the Computational and Systems Neuroscience conference in 2019, who not only taught but also greatly improved the five case studies and wrote solutions: Anna Kutschireiter, Anne-Lene Sax, Jennifer Laura Lee, Jorge Menéndez, Julie Lee, Lucy Lai, and Sashank Pisupati. A much more detailed didactic introduction to Bayesian decision models will appear in 2020 in book form; many thanks to my co-authors of that book, Konrad Körding and Daniel Goldreich. My research is funded by grants R01EY020958 , R01EY027925 , R01MH118925 , and R01EY026927 from the National Institutes of Health .
Funding Information:
This primer is based on a Bayesian modeling tutorial that I have taught in several places. Thanks to all students who actively participated in these tutorials. Special thanks to my teaching assistants of the Bayesian tutorial at the Computational and Systems Neuroscience conference in 2019, who not only taught but also greatly improved the five case studies and wrote solutions: Anna Kutschireiter, Anne-Lene Sax, Jennifer Laura Lee, Jorge Men?ndez, Julie Lee, Lucy Lai, and Sashank Pisupati. A much more detailed didactic introduction to Bayesian decision models will appear in 2020 in book form; many thanks to my co-authors of that book, Konrad K?rding and Daniel Goldreich. My research is funded by grants R01EY020958, R01EY027925, R01MH118925, and R01EY026927 from the National Institutes of Health.
Publisher Copyright:
© 2019
PY - 2019/10/9
Y1 - 2019/10/9
N2 - To understand decision-making behavior in simple, controlled environments, Bayesian models are often useful. First, optimal behavior is always Bayesian. Second, even when behavior deviates from optimality, the Bayesian approach offers candidate models to account for suboptimalities. Third, a realist interpretation of Bayesian models opens the door to studying the neural representation of uncertainty. In this tutorial, we review the principles of Bayesian models of decision making and then focus on five case studies with exercises. We conclude with reflections and future directions.
AB - To understand decision-making behavior in simple, controlled environments, Bayesian models are often useful. First, optimal behavior is always Bayesian. Second, even when behavior deviates from optimality, the Bayesian approach offers candidate models to account for suboptimalities. Third, a realist interpretation of Bayesian models opens the door to studying the neural representation of uncertainty. In this tutorial, we review the principles of Bayesian models of decision making and then focus on five case studies with exercises. We conclude with reflections and future directions.
KW - Bayes Theorem
KW - Decision Making
KW - Humans
KW - Models, Psychological
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85072783582&partnerID=8YFLogxK
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U2 - 10.1016/j.neuron.2019.09.037
DO - 10.1016/j.neuron.2019.09.037
M3 - Review article
C2 - 31600512
AN - SCOPUS:85072783582
VL - 104
SP - 164
EP - 175
JO - Neuron
JF - Neuron
SN - 0896-6273
IS - 1
ER -