ArticleNature communications2024
Complex behavior from intrinsic motivation to occupy future action-state path space.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Foraging models explain human exploration in uncertain tasks.Nature communications · 2026Article
- Serotonergic modulation of motor subspace dynamics drives a sleep-independent quiescent state.eLife · 2026Article
- From Spontaneous Ignitions to Sensorimotor Cell Assemblies via Dopamine: A Spiking Neurocomputational Model of Infants' Hand Action Acquisition.Brain sciences · 2026Article
- Decision, Inference, and Information: Formal Equivalences Under Active Inference.Entropy (Basel, Switzerland) · 2025Review
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
- The Physics and Metaphysics of Social Powers: Bridging Cognitive Processing and Social Dynamics, a New Perspective on Power Through Active Inference.Entropy (Basel, Switzerland) · 2025Article
- Intrinsic Motivation as Constrained Entropy Maximization.Entropy (Basel, Switzerland) · 2025Article
- Humans forage for reward in reinforcement learning tasks.bioRxiv : the preprint server for biology · 2025Article
- Fluctuations in Sequential Many-Alternative Decisions Reveal Strategies Beyond Immediate Reward Maximisation.Journal of cognition · 2025Article
- Complex behavior from intrinsic motivation to occupy future action-state path space.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Most theories of behavior posit that agents tend to maximize some form of reward or utility. However, animals very often move with curiosity and seem to be motivated in a reward-free manner. Here we abandon the idea of reward maximization and propose that the goal of behavior is maximizing occupancy of future paths of actions and states. According to this maximum occupancy principle, rewards are the means to occupy path space, not the goal per se; goal-directedness simply emerges as rational ways of searching for resources so that movement, understood amply, never ends. We find that action-state path entropy is the only measure consistent with additivity and other intuitive properties of expected future action-state path occupancy. We provide analytical expressions that relate the optimal policy and state-value function and prove convergence of our value iteration algorithm. Using discrete and continuous state tasks, including a high-dimensional controller, we show that complex behaviors such as "dancing", hide-and-seek, and a basic form of altruistic behavior naturally result from the intrinsic motivation to occupy path space. All in all, we present a theory of behavior that generates both variability and goal-directedness in the absence of reward maximization.
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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.