paper:doi-10-1016-j-tics-2018-01-009Hierarchical Active Inference: A Theory of Motivated Control
Original abstract (expand)
Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.
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Cited / in-corpus / arXiv badges show which signals surfaced each row. Multi-source rows weighted higher.
- Active Inference or Control as Inference? A Unifying ViewAbraham Imohiosen, Jan Peters Joe Watson2020≈ 79%
- On the Relationship Between Active Inference and Control as InferenceAlexander Tschantz, Anil K Seth, Christopher L Buckley Beren Millidge2020≈ 76%
- Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action LoopChristian Guckelsberger (2), Christoph Salge (3 and 4), Sim\'on C. Smith (4 and 5), Daniel Polani (4) ((1) Araya Inc., Tokyo, Japan, (2) Computational Creativity Group, Department of Computing, Goldsmiths, University of London, London, UK, (3) Game Innovation Lab, Department of Computer Science and Engineering, New York University, New York City, NY, USA, (4) Sepia Lab, Adaptive Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield, UK, (5) Institute of Perception, Action and Behaviour, School of Informatics, The University of Edinburgh, UK) Martin Biehl (1)2018≈ 76%
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- Modeling motor control in continuous-time Active Inference: a surveyFederico Maggiore, Antonella Maselli, Francesco Donnarumma, Domenico Maisto, Francesco Mannella, Ivilin Peev Stoianov and Giovanni Pezzulo Matteo Priorelli2024≈ 75%
- Active Inference in Robotics and Artificial Agents: Survey and ChallengesCristian Meo, Corrado Pezzato, Ajith Anil Meera, Mohamed Baioumy, Wataru Ohata, Alexander Tschantz, Beren Millidge, Martijn Wisse, Christopher L. Buckley, Jun Tani Pablo Lanillos2021≈ 75%
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- Deriving time-averaged active inference from control principlesJordan Theriault, Jan-Willem van de Meent, Lisa Feldman Barrett, Karen Quigley Eli Sennesh2022≈ 74%
- Active Inference: A method for Phenotyping Agency in AI systems?Philip Wilson and Axel Constant and Mahault Albarracin and Nicol\'as Hinrichs and Jasmine Moore and Daniel Polani and Karl Friston2026≈ 74%
- Distributional Active InferenceGulcin Baykal, Manuel Hau{\ss}mann, Mustafa Mert \c{C}elikok, Melih Kandemir Abdullah Akg\"ul2026≈ 74%
- Hierarchical Active Inference using Successor RepresentationsRajesh P. N. Rao Prashant Rangarajan2026≈ 74%
- Active Inference and Human--Computer InteractionJohn H. Williamson, Sebastian Stein Roderick Murray-Smith2024≈ 74%
- Active inference for action-unaware agentsKeisuke Suzuki, Ryota Kanai, Manuel Baltieri Filippo Torresan2025≈ 74%
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- Active Inference: A Process Theoryin corpus2017≈ 71%
- Active inference: demystified and comparedin corpus2021≈ 70%
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- Mechanistic Knobs in LLMs: Retrieving and Steering High-Order Semantic Features via Sparse Autoencodersin corpus2026≈ 65%
- ≈ 65%
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- Active Inference, Curiosity and Insightin corpus2017≈ 65%
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