Model averaging, optimal inference, and habit formation

• Published in: Frontiers in human neuroscience Vol. 8; p. 457
• Main Authors: FitzGerald, Thomas H. B., Dolan, Raymond J., Friston, Karl J.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.06.2014; Frontiers Media S.A
• Subjects: Accuracy; active inference; Bayesian analysis; Bayesian inference; bistable perception; bounded rationality; Cognition; habit; Mathematical models; Nervous system; Neuroscience; predictive coding; Trends; habit; interference effect; active inference; Bayesian inference; predictive coding
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2014.00457

Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior.