Active inference and learning

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license

Publication type: 
Articolo
Author or Creator: 
Friston, Karl
FitzGerald, Thomas
Rigoli, Francesco
Schwartenbeck, Philipp
O'Doherty, John
Pezzulo, Giovanni
Publisher: 
Pergamon., New York, Stati Uniti d'America
Source: 
Neuroscience and biobehavioral reviews 68 (2016): 862–879. doi:10.1016/j.neubiorev.2016.06.022
Date: 
2016
Resource Identifier: 
http://www.cnr.it/prodotto/i/361697
https://dx.doi.org/10.1016/j.neubiorev.2016.06.022
info:doi:10.1016/j.neubiorev.2016.06.022
Language: 
Eng
ISTC Author: 
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