Active inference and learning

Neurosci Biobehav Rev. 2016 Sep;68:862-879. doi: 10.1016/j.neubiorev.2016.06.022. Epub 2016 Jun 29.

Abstract

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.

Keywords: Active inference; Bayesian inference; Bayesian surprise; Epistemic value; Exploitation; Exploration; Free energy; Goal-directed; Habit learning; Information gain.

Publication types

  • Review

MeSH terms

  • Choice Behavior
  • Dopamine
  • Habits
  • Learning*
  • Reward

Substances

  • Dopamine