ArticleAdvances in neural information processing systems2020
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics.
Article in Advances in neural information processing systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Neural and computational correlates of strategic aborting and long-run policy optimization in the dorsolateral prefrontal cortex.Nature communications · 2026Article
- Computational elements of natural vision.Journal of vision · 2025Review
- Causal inference during closed-loop navigation: parsing of self- and object-motion.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Causal inference during closed-loop navigation: parsing of self- and object-motion.bioRxiv : the preprint server for biology · 2023Article
- Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.Nature machine intelligence · 2023Article
- Putting perception into action with inverse optimal control for continuous psychophysics.eLife · 2022Article
- Sensory Evidence Accumulation Using Optic Flow in a Naturalistic Navigation Task.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Supporting generalization in non-human primate behavior by tapping into structural knowledge: Examples from sensorimotor mappings, inference, and decision-making.Progress in neurobiology · 2021Article
- Rational thoughts in neural codes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
A fundamental question in neuroscience is how the brain creates an internal model of the world to guide actions using sequences of ambiguous sensory information. This is naturally formulated as a reinforcement learning problem under partial observations, where an agent must estimate relevant latent variables in the world from its evidence, anticipate possible future states, and choose actions that optimize total expected reward. This problem can be solved by control theory, which allows us to find the optimal actions for a given system dynamics and objective function. However, animals often appear to behave suboptimally. Why? We hypothesize that animals have their own flawed internal model of the world, and choose actions with the highest expected subjective reward according to that flawed model. We describe this behavior as
Identifiers
34712038PMC8549572W2967576524What OpenQuestion holds
Registered trials
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.