ArticleFrontiers in artificial intelligence2020
Deep Active Inference and Scene Construction.
Article in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Towards an active inference account of deep meditative deconstruction.Neuroscience of consciousness · 2026Article
- Article
- Arousal coherence, uncertainty, and well-being: an active inference account.Neuroscience of consciousness · 2024Review
- Epistemic Communities under Active Inference.Entropy (Basel, Switzerland) · 2022Article
- The Free Energy Principle for Perception and Action: A Deep Learning Perspective.Entropy (Basel, Switzerland) · 2022Review
- Embodied Object Representation Learning and Recognition.Frontiers in neurorobotics · 2022Article
- Bayesian mechanics for stationary processes.Proceedings. Mathematical, physical, and engineering sciences · 2021Article
- Memory and Markov Blankets.Entropy (Basel, Switzerland) · 2021Article
- Active Vision for Robot Manipulators Using the Free Energy Principle.Frontiers in neurorobotics · 2021Article
- Understanding, Explanation, and Active Inference.Frontiers in systems neuroscience · 2021Article
- Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference.Neuroscience of consciousness · 2021Article
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6 authors.
Funding
Abstract
Adaptive agents must act in intrinsically uncertain environments with complex latent structure. Here, we elaborate a model of visual foraging-in a hierarchical context-wherein agents infer a higher-order visual pattern (a "scene") by sequentially sampling ambiguous cues. Inspired by previous models of scene construction-that cast perception and action as consequences of approximate Bayesian inference-we use active inference to simulate decisions of agents categorizing a scene in a hierarchically-structured setting. Under active inference, agents develop probabilistic beliefs about their environment, while actively sampling it to maximize the evidence for their internal generative model. This approximate evidence maximization (i.e., self-evidencing) comprises drives to both maximize rewards and resolve uncertainty about hidden states. This is realized via minimization of a free energy functional of posterior beliefs about both the world as well as the actions used to sample or perturb it, corresponding to perception and action, respectively. We show that active inference, in the context of hierarchical scene construction, gives rise to many empirical evidence accumulation phenomena, such as noise-sensitive reaction times and epistemic saccades. We explain these behaviors in terms of the principled drives that constitute the
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.