Active inference and learning
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- 作者：Karl Fristona ; k.friston@ucl.ac.uk" class="auth_mail" title="E-mail the corresponding author ; Thomas FitzGeralda ; b ; thomas.fitzgerald@ucl.ac.uk" class="auth_mail" title="E-mail the corresponding author ; Francesco Rigolia ; f.rigoli@ucl.ac.uk" class="auth_mail" title="E-mail the corresponding author ; Philipp Schwartenbecka ; b ; c ; d ; philipp.schwartenbeck.12@ucl.ac.uk" class="auth_mail" title="E-mail the corresponding author ; John O&rsquo ; Dohertye ; jdoherty@hss.caltech.edu" class="auth_mail" title="E-mail the corresponding author ; Giovanni Pezzulof ; giovanni.pezzulo@istc.cnr.it" class="auth_mail" title="E-mail the corresponding author
- 关键词：Active inference ; Habit learning ; Bayesian inference ; Goal-directed ; Free energy ; Information gain ; Bayesian surprise ; Epistemic value ; Exploration ; Exploitation
- 刊名：Neuroscience & Biobehavioral Reviews
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：68
- 期：Complete
- 页码：862-879
- 全文大小：5349 K
文摘
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Optimal behaviour is quintessentially belief based.
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Behaviour can be described as optimising expected free energy.
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Expected free energy entails pragmatic and epistemic value.
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Habits are learned by observing one’s own goal directed behaviour.
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Habits are then selected online during active inference.