ArticlePsychological review2021
Temporal and state abstractions for efficient learning, transfer, and composition in humans.
Article in Psychological review, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 32 citations in OpenAlex.
- Naturally disengaging control to reveal habits.Journal of experimental psychology. General · 2026Article
- Dorsomedial prefrontal cortex links abstract planning to motor execution.Communications biology · 2026Article
- Entorhinal Cortex Signals Dimensions of Past Experience That Can Be Generalized in a Novel Environment.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Action subsampling supports policy compression in large action spaces.PLoS computational biology · 2025Article
- Neural evidence that humans reuse strategies to solve new tasks.PLoS biology · 2025Article
- Humans learn generalizable representations through efficient coding.Nature communications · 2025Article
- Naturally disengaging control to reveal habits.Research square · 2025Article
- An algorithmic account for how humans efficiently learn, transfer, and compose hierarchically structured decision policies.Cognition · 2025Article
- Review
- The successor representation subserves hierarchical abstraction for goal-directed behavior.PLoS computational biology · 2024Article
- Naturalistic reinforcement learning.Trends in cognitive sciences · 2024Review
- Anxiety as a disorder of uncertainty: implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy.Cognitive, affective & behavioral neuroscience · 2023Article
- Visuospatial information foraging describes search behavior in learning latent environmental features.Scientific reports · 2023Article
- Contextual inference in learning and memory.Trends in cognitive sciences · 2023Review
- Credit assignment in hierarchical option transfer.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference · 2022Article
- Hierarchical Reinforcement Learning, Sequential Behavior, and the Dorsal Frontostriatal System.Journal of cognitive neuroscience · 2022Review
- Serotonin neurons modulate learning rate through uncertainty.Current biology : CB · 2022Article
- Advances in modeling learning and decision-making in neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Review
- What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience.Current opinion in behavioral sciences · 2021Article
- Learning Structures: Predictive Representations, Replay, and Generalization.Current opinion in behavioral sciences · 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
2 authors at 1 institution in 1 country.
Funding
Abstract
Humans use prior knowledge to efficiently solve novel tasks, but how they structure past knowledge during learning to enable such fast generalization is not well understood. We recently proposed that hierarchical state abstraction enabled generalization of simple one-step rules, by inferring context clusters for each rule. However, humans' daily tasks are often temporally extended, and necessitate more complex multi-step, hierarchically structured strategies. The options framework in hierarchical reinforcement learning provides a theoretical framework for representing such transferable strategies. Options are abstract multi-step policies, assembled from simpler one-step actions or other options, that can represent meaningful reusable strategies as temporal abstractions. We developed a novel sequential decision-making protocol to test if humans learn and transfer multi-step options. In a series of four experiments, we found transfer effects at multiple hierarchical levels of abstraction that could not be explained by flat reinforcement learning models or hierarchical models lacking temporal abstractions. We extended the options framework to develop a quantitative model that blends temporal and state abstractions. Our model captures the transfer effects observed in human participants. Our results provide evidence that humans create and compose hierarchical options, and use them to explore in novel contexts, consequently transferring past knowledge and speeding up learning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Indexed as
Identifiers
What 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.