mlsanityData to 7 Oct 2026

Meta-learning in natural and artificial intelligence

arXiv 2011.13464 ↗First posted 26 Nov 2020cs.AIcited by 39

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Abstract (arXiv)

Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology, and is currently studied in various forms within neuroscience. The aim of this review is to recast previous lines of research in the study of biological intelligence within the lens of meta-learning, placing these works into a common framework. More recent points of interaction between AI and neuroscience will be discussed, as well as interesting new directions that arise under this perspective.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 5; 2023: 9; 2024: 7; 2025: 6; 2026: 4.

39 papers.

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  1. 2022Language models show human-like content effects on reasoning taskscited by 171
  2. 2021Abstraction and Analogy-Making in Artificial Intelligencecited by 103
  3. 2023The Transient Nature of Emergent In-Context Learning in Transformerscited by 72
  4. 2023Learning from models beyond fine-tuningcited by 47
  5. 2023Modeling rapid language learning by distilling Bayesian priors into artificial neural networkscited by 40
  6. 2021Towards mental time travel: a hierarchical memory for reinforcement learning agentscited by 38
  7. 2021Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentscited by 35
  8. 2024The broader spectrum of in-context learningcited by 33
  9. 2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
  10. 2021Symbolic Behaviour in Artificial Intelligencecited by 24
  11. 2021Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligencecited by 21
  12. 2022The least-control principle for local learning at equilibriumcited by 16
27 more
  1. 2021A contrastive rule for meta-learningcited by 14
  2. 2025EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solvingcited by 10
  3. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  4. 2025Meta-Cultural Competence: Climbing the Right Hill of Cultural Awarenesscited by 8
  5. 2024From Frege to chatGPT: Compositionality in language, cognition, and deep neural networkscited by 7
  6. 2022Prospective Learning: Principled Extrapolation to the Futurecited by 6
  7. 2025Whither symbols in the era of advanced neural networks?cited by 6
  8. 2026Measuring Progress Toward AGI: A Cognitive Frameworkcited by 6
  9. 2023RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learningcited by 5
  10. 2024Distilling Symbolic Priors for Concept Learning into Neural Networkscited by 5
  11. 2021Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningcited by 4
  12. 2021Rediscovering Affordance: A Reinforcement Learning Perspectivecited by 4
  13. 2023Discovering Adaptable Symbolic Algorithms from Scratchcited by 4
  14. 2023Few-Shot Classification & Segmentation Using Large Language Models Agentcited by 2
  15. 2024Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Modelscited by 2
  16. 2025Efficient Visual Appearance Optimization by Learning from Prior Preferencescited by 2
  17. 2022Modeling Human Behavior Part I -- Learning and Belief Approachescited by 1
  18. 2022Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learningcited by 1
  19. 2023Dynamic Link Prediction for New Nodes in Temporal Graph Networkscited by 1
  20. 2024Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfercited by 1
  21. 2025The Price equation reveals a universal force-metric-bias law of algorithmic learning and natural selectioncited by 1
  22. 2025AutoIntent: AutoML for Text Classificationcited by 1
  23. 2026How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopiccited by 1
  24. 2023Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement Learning Adaptationcited by 0
  25. 2024Learning-to-learn enables rapid learning with phase-change memory-based in-memory computingcited by 0
  26. 2026Transformer See, Transformer Do: Copying as an Intermediate Step in Learning Analogical Reasoningcited by 0
  27. 2026Learning Options for Compositional Motor Control with Adapter Bankscited by 0