The anatomy of choice: active inference and agency

By Karl J. Friston, Philipp Schwartenbeck, Thomas H. B. FitzGerald, Michael Moutoussis, Timothy Edward John Behrens, R. J. Dolan. Frontiers in Human Neuroscience, 2013

335 citations recorded by the source corpus.

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback–Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action—constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution—that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Subfields: Cognitive Neuroscience, History and Philosophy of Science, Philosophy

View Paper: https://www.semanticscholar.org/paper/3144baaeb4cab2bf48a8140de03080c03c4488b9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In History and Philosophy of Science

In Philosophy

Citations in the corpus

Affective Value in the Predictive Mind (2017, background): “PP proposes that specialization (functional segregation) in the brain stems from conditional independence of different representations—representations that have predictive relations organize into regions with tight interconnections (Friston et al. 2013; Stansbury et al. 2013).”
Active Inference: A Process Theory (2017, methodology): “Indeed, earlier versions have been used to model waiting games (Friston et al., 2013), the urn task and evidence accumulation (FitzGerald, Schwartenbeck, Moutoussis, Dolan, & Friston, 2015), trust games from behavioral economics (Moutoussis, Trujillo-Barreto, ElDeredy, Dolan, & Friston, 2014; Schwar”
Active inference and epistemic value (2015, methodology): “We have previously discussed the biological plausibility of this scheme in terms of recursive neuronal message passing (Friston et al., 2013) and have associated dopamine with the posterior precision of beliefs about control states (Friston et al., 2014).”
The graphical brain: Belief propagation and active inference (2017, background): “In this sense, the brain conforms to the imper-atives for all sentient creatures; namely, to restrict itself to a limited number of attracting states (Friston, 2013).”
Active Inference, Curiosity and Insight (2017, methodology): “It has been used to model waiting games (Friston et al., 2013), two-step maze tasks (Friston et al.”
Uncertainty in perception and the Hierarchical Gaussian Filter (2014, background): “Another possible choice for the inverse decision temperature is π 2 (cf. # This choice is interesting because it is similar to the hypothesis (Friston et al., 2013; Schwartenbeck et al., 2013) that precision serves as the inverse decision temperature in active inference.”
A tutorial on the free-energy framework for modelling perception and learning (2017, methodology): “This tutorial is planned to be complementary to existing literature so it does not focus on the relationship to other theories and experimental data, and on applications to more complex tasks which are described elsewhere (Friston, 2010; Friston et al., 2013).”