ArticleJournal of the Royal Society, Interface2015
Divide et impera: subgoaling reduces the complexity of probabilistic inference and problem solving.
Article in Journal of the Royal Society, Interface, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Perceived Complexity as Normalized, Integrated, Localized Shannon Entropy.Entropy (Basel, Switzerland) · 2026Article
- Humans Select Subgoals That Balance Immediate and Future Cognitive Costs During Physical Assembly.Cognitive science · 2025Article
- Resilience phenotypes derived from an active inference account of allostasis.Frontiers in behavioral neuroscience · 2025Article
- A field experiment on the effects of weekly planning behaviour on work engagement, unfinished tasks, rumination, and cognitive flexibility.Journal of occupational and organizational psychology · 2023Article
- Humans decompose tasks by trading off utility and computational cost.PLoS computational biology · 2023Article
- A space of goals: the cognitive geometry of informationally bounded agents.Royal Society open science · 2022Article
- A weighted constraint satisfaction approach to human goal-directed decision making.PLoS computational biology · 2022Article
- Canonical neural networks perform active inference.Communications biology · 2022Article
- Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle.Entropy (Basel, Switzerland) · 2021Article
- Active listening.Hearing research · 2021Article
- Trust as Extended Control: Human-Machine Interactions as Active Inference.Frontiers in systems neuroscience · 2021Article
- The Thalamus as a Blackboard for Perception and Planning.Frontiers in behavioral neuroscience · 2021Article
- Generative models, linguistic communication and active inference.Neuroscience and biobehavioral reviews · 2020Review
- Degeneracy and Redundancy in Active Inference.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Modules or Mean-Fields?Entropy (Basel, Switzerland) · 2020Article
- Caching mechanisms for habit formation in Active Inference.Neurocomputing · 2019Article
- GenHap: a novel computational method based on genetic algorithms for haplotype assembly.BMC bioinformatics · 2019Article
- The Gravity of Objects: How Affectively Organized Generative Models Influence Perception and Social Behavior.Frontiers in psychology · 2019Article
- Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis.PLoS computational biology · 2018Article
- Planning and navigation as active inference.Biological cybernetics · 2018Article
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3 authors.
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Abstract
It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabilistic inference: subgoals are selected using a sampling method that considers the descriptive complexity of the resulting sub-problems. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. Furthermore, we show that the proposed method offers a mechanistic explanation of the neuronal dynamics found in the prefrontal cortex of monkeys that solve planning problems. Our computational framework provides a novel integrative perspective on subgoaling and its adaptive advantages for planning, control and learning, such as for example lowering cognitive effort and working memory load.
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