ArticlePLoS computational biology2020
Discovery of hierarchical representations for efficient planning.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Dorsomedial prefrontal cortex links abstract planning to motor execution.Communications biology · 2026Article
- It Is What It Isn't: Introducing a Constraint-Based Approach to Structure Learning.Entropy (Basel, Switzerland) · 2026Article
- Hybrid neural-cognitive models reveal how memory shapes human reward learning.Nature human behaviour · 2026Article
- Learning to select computations in recurrent neural circuits.bioRxiv : the preprint server for biology · 2026Article
- Prefrontal Mechanisms of Rule Learning.bioRxiv : the preprint server for biology · 2026Article
- Article
- Planning to be incremental: Scene descriptions reveal meaningful clustering in language production.Cognition · 2026Article
- Language-Invariant Strategies of Navigating Transitions in Joint Activities: Forms and Functions of Coordination Markers.Cognitive science · 2025Article
- Cognitive maps for hierarchical spaces in the human brain.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Adaptive planning depth in human problem-solving.Royal Society open science · 2025Article
- Schemas, reinforcement learning and the medial prefrontal cortex.Nature reviews. Neuroscience · 2025Review
- Two-dimensional neural geometry underpins hierarchical organization of sequence in human working memory.Nature human behaviour · 2025Article
- Expert navigators deploy rational complexity-based decision precaching for large-scale real-world planning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Learning to Decompose: Human-Like Subgoal Preferences Emerge in Neural Networks Learning Graph Traversal.Open mind : discoveries in cognitive science · 2025Article
- Cognitive Control.Annual review of psychology · 2025Review
- An algorithmic account for how humans efficiently learn, transfer, and compose hierarchically structured decision policies.Cognition · 2025Article
- A cellular basis for mapping behavioural structure.Nature · 2024Article
- Approximate planning in spatial search.PLoS computational biology · 2024Article
- Building machines that learn and think with people.Nature human behaviour · 2024Review
- Cognitive Graphs: Representational Substrates for Planning.Decision (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We propose that humans spontaneously organize environments into clusters of states that support hierarchical planning, enabling them to tackle challenging problems by breaking them down into sub-problems at various levels of abstraction. People constantly rely on such hierarchical presentations to accomplish tasks big and small-from planning one's day, to organizing a wedding, to getting a PhD-often succeeding on the very first attempt. We formalize a Bayesian model of hierarchy discovery that explains how humans discover such useful abstractions. Building on principles developed in structure learning and robotics, the model predicts that hierarchy discovery should be sensitive to the topological structure, reward distribution, and distribution of tasks in the environment. In five simulations, we show that the model accounts for previously reported effects of environment structure on planning behavior, such as detection of bottleneck states and transitions. We then test the novel predictions of the model in eight behavioral experiments, demonstrating how the distribution of tasks and rewards can influence planning behavior via the discovered hierarchy, sometimes facilitating and sometimes hindering performance. We find evidence that the hierarchy discovery process unfolds incrementally across trials. Finally, we propose how hierarchy discovery and hierarchical planning might be implemented in the brain. Together, these findings present an important advance in our understanding of how the brain might use Bayesian inference to discover and exploit the hidden hierarchical structure of the environment.
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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.