Active Inference, Curiosity and Insight

Abstract

This article offers a formal account of curiosity and insight in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how people attain insight and understanding using just a handful of observations, which are solicited through curious behavior. We use simulations of abstract rule learning and approximate Bayesian inference to show that minimizing (expected) variational free energy leads to active sampling of novel contingencies. This epistemic behavior closes explanatory gaps in generative models of the world, thereby reducing uncertainty and satisfying curiosity. We then move from epistemic learning to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries (i.e., invariances or rules) in their generative models. The ensuing Bayesian model reduction evinces mechanisms associated with sleep and has all the hallmarks of "aha" moments. This formulation moves toward a computational account of consciousness in the pre-Cartesian sense of sharable knowledge (i.e., con: "together"; scire: "to know").

Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

“…For now, we omit the probability distribution over future actions and the “precision”, which are only needed for active inference and are discussed later. The generative models in Friston et al (2016a,b, 2017a) are all closely related.…”

“…Most of the expressions we give here can be found in Friston et al (2017b). The exception is Equation (74), which can be obtained from an approximate term in Friston et al (2017a) in the same way that the non-variational Bayesian inference terms in Friston et al (2017b) are obtained from the approximate ones in Friston et al (2015). …”

“…Active inference using different forms of approximate posteriors can be found in Friston et al (2016b). Here, Friston et al (2017a) also includes a knowledge seeking term in addition to the conditional entropy term. In the universal reinforcement learning framework Aslanides et al (2017) also implement a knowledge seeking agent.…”

“…A considerable number of articles have been published on active inference (e.g., Friston et al, 2012, 2015, 2016a,b, 2017a,b; Linson et al, 2018). Active inference defines a procedure for both perception and action of an agent interacting with a partially observable environment.…”

“…Behaviour in active inference is thus the result of a variational inference-like process. On the other hand, the function (i.e., expected free energy) that induces the objective function in active inference is said to be “of the same form” as the variational free energy (Friston et al, 2017a, p. 2673) or even to “follow” from it (Friston et al, 2016b, p. 10). This suggests that expected free energy is the only objective function compatible with active inference.…”