# Meta-learning in natural and artificial intelligence

arXiv: https://arxiv.org/abs/2011.13464 · HTML: https://mlsanity.com/paper/2011.13464/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 30 of 39)

- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021)
- [The Transient Nature of Emergent In-Context Learning in Transformers](https://mlsanity.com/paper/2311.08360/) (2023)
- [Learning from models beyond fine-tuning](https://mlsanity.com/paper/2310.08184/) (2023)
- [Modeling rapid language learning by distilling Bayesian priors into artificial neural networks](https://mlsanity.com/paper/2305.14701/) (2023)
- [Towards mental time travel: a hierarchical memory for reinforcement learning agents](https://mlsanity.com/paper/2105.14039/) (2021)
- [Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents](https://mlsanity.com/paper/2102.02926/) (2021)
- [The broader spectrum of in-context learning](https://mlsanity.com/paper/2412.03782/) (2024)
- [Symbolic Behaviour in Artificial Intelligence](https://mlsanity.com/paper/2102.03406/) (2021)
- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence](https://mlsanity.com/paper/2106.01288/) (2021)
- [The least-control principle for local learning at equilibrium](https://mlsanity.com/paper/2207.01332/) (2022)
- [A contrastive rule for meta-learning](https://mlsanity.com/paper/2104.01677/) (2021)
- [EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving](https://mlsanity.com/paper/2506.02672/) (2025)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness](https://mlsanity.com/paper/2502.09637/) (2025)
- [From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks](https://mlsanity.com/paper/2405.15164/) (2024)
- [Whither symbols in the era of advanced neural networks?](https://mlsanity.com/paper/2508.05776/) (2025)
- [Prospective Learning: Principled Extrapolation to the Future](https://mlsanity.com/paper/2201.07372/) (2022)
- [Measuring Progress Toward AGI: A Cognitive Framework](https://mlsanity.com/paper/2605.28405/) (2026)
- [RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning](https://mlsanity.com/paper/2303.06679/) (2023)
- [Distilling Symbolic Priors for Concept Learning into Neural Networks](https://mlsanity.com/paper/2402.07035/) (2024)
- [Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning](https://mlsanity.com/paper/2112.08588/) (2021)
- [Rediscovering Affordance: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2112.12886/) (2021)
- [Discovering Adaptable Symbolic Algorithms from Scratch](https://mlsanity.com/paper/2307.16890/) (2023)
- [Few-Shot Classification & Segmentation Using Large Language Models Agent](https://mlsanity.com/paper/2311.12065/) (2023)
- [Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models](https://mlsanity.com/paper/2403.10287/) (2024)
- [Efficient Visual Appearance Optimization by Learning from Prior Preferences](https://mlsanity.com/paper/2507.15355/) (2025)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)
- [Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning](https://mlsanity.com/paper/2209.13530/) (2022)

## 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.