Caching mechanisms for habit formation in Active Inference

• Published in: Neurocomputing (Amsterdam) Vol. 359; pp. 298 - 314
• Main Authors: Maisto, D., Friston, K., Pezzulo, G.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 24.09.2019; Elsevier Science Publishers
• Subjects: Active Inference; Caching; Deliberative control; Habitisation; Habitual control; Deliberative control; Habitisation; Habitual control; Active Inference; Caching
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2019.05.083

A popular distinction in the human and animal learning literature is between deliberate (or willed) and habitual (or automatic) modes of control. Extensive evidence indicates that, after sufficient learning, living organisms develop behavioural habits that permit them saving computational resources. Furthermore, humans and other animals are able to transfer control from deliberate to habitual modes (and vice versa), trading off efficiently flexibility and parsimony – an ability that is currently unparalleled by artificial control systems. Here, we discuss a computational implementation of habit formation, and the transfer of control from deliberate to habitual modes (and vice versa) within Active Inference: a computational framework that merges aspects of cybernetic theory and of Bayesian inference. To model habit formation, we endow an Active Inference agent with a mechanism to “cache” (or memorize) policy probabilities from previous trials, and reuse them to skip – in part or in full – the inferential steps of deliberative processing. We exploit the fact that the relative quality of policies, conditioned upon hidden states, is constant over trials; provided that contingencies and prior preferences do not change. This means the only quantity that can change policy selection is the prior distribution over the initial state – where this prior is based upon the posterior beliefs from previous trials. Thus, an agent that caches the quality (or the probability) of policies can safely reuse cached values to save on cognitive and computational resources – unless contingencies change. Our simulations illustrate the computational benefits, but also the limits, of three caching schemes under Active Inference. They suggest that key aspects of habitual behaviour – such as perseveration – can be explained in terms of caching policy probabilities. Furthermore, they suggest that there may be many kinds (or stages) of habitual behaviour, each associated with a different caching scheme; for example, caching associated or not associated with contextual estimation. These schemes are more or less impervious to contextual and contingency changes.