Active Inference, Curiosity and Insight

By Karl J. Friston, Marco Lin, ChrisD . Frith, Giovanni Pezzulo, J. Allan Hobson, Sasha Ondobaka. Neural Computation, 2017

311 citations recorded by the source corpus.

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”).

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/dc8fb699df3504befcdf4037fb625d81f08a2876

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In Cognitive Neuroscience

Citations in the corpus

A free energy principle for a particular physics (2019, background): “Under discrete states, the expected free energy can be expressed as follows (see Appendix F for details): 0 0 ( ) [ ( | ) || ( | )] [ ( | )] ( ) ( | ) ( , | , ) i i i i i i i Q risk ambiguity i i G D Q P E s Q P s”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “Deep Goal Hierarchies in Active Inference Active inference views the brain as a statistical organ that forms internal generative models of the (hidden) states and contingencies in the world, and uses these models to continuously generate predictions in the service of perception and adaptive behaviou”
REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics (2019, background): “We propose that mechanisms of BMS and BMR are catalyzed by the time-limited entropic state induced by psychedelics, such that model redundancy can be minimized, and high-level priors refined or “ spring-cleaned.”