Meta-learning in natural and artificial intelligence

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512

Prototypical Networks for Few-shot LearningPreprint

Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 6,245

Statistical learning by 8-month-old infants

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996) · Science · doi:10.1126/science.274.5294.1926 · Cited by 5,659

Matching Networks for One Shot LearningPreprint

Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04080 · Cited by 4,975

Human-level concept learning through probabilistic program induction

Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817

Optimization as a Model for Few-Shot Learning

Ravi, S., & Larochelle, H. (2017) · International Conference on Learning Representations · Cited by 2,766

Building machines that learn and think like people

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533

Identifying Transfer Learning in the Reshaping of Inductive Biases

Székely, A., Török, B., Kiss, M. M., Janacsek, K., Németh, D., & Orbán, G. (2024) · Open Mind · doi:10.1162/opmi_a_00158 · Cited by 4

Multi-Level Stacking

Boldrin, F. C., Cantão, A. H., Tinós, R., & Baranauskas, J. A. (2022) · doi:10.5753/eniac.2022.227346 · Cited by 2

Learning when to learn: Context-specific instruction encodingPreprint

Xu, S., Chai, M., Abrahamse, E. L., Liefooghe, B., & Braem, S. (2020) · doi:10.31234/osf.io/7tvx3 · Cited by 2

Research on Artificial Intelligence Algorithm System for Medical Institutions Based on Construction Engineering and E-commerce

Zhao, P., Luo, Z., Yang, G., Cai, W., Li, L., & Qin, Y. (2024) · Highlights in Science Engineering and Technology · doi:10.54097/kfgjw731 · Cited by 1

Structure transfer and consolidation in visual implicit learning

Garber, D., & Fiser, J. (2025) · eLife · doi:10.1101/2024.06.20.599904 · Cited by 1

A Concise Survey on Meta-Learning and Its Applications

Ramkumar, D., Maithili, C., Palaskar, S. P., Toley, M. C., Jadhav, A., & Pande, S. D. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-4359-9_39 · Cited by 1

Combining meta reinforcement learning with neural plasticity mechanisms for improved AI performance

Liu, L., & Xu, Z. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320777 · Cited by 1

AI discovers learning algorithm that outperforms those designed by humans

Lehman, J. (2025) · Nature · doi:10.1038/d41586-025-03398-6 · Cited by 1

Learning and Using Different Task Rule Updating Strategies Require Extensive Practice

Xu, S., Wen, T., Egner, T., Verguts, T., & Braem, S. (2026) · Cognitive Science · doi:10.1111/cogs.70262 · Cited by 1

The Price Equation Reveals a Universal Force–Metric–Bias Law of Algorithmic Learning and Natural Selection

Frank, S. A. (2025) · Entropy · doi:10.3390/e27111129 · Cited by 4

Shared sensitivity to data distribution during learning in humans and transformer networks

Lerousseau, J. P., & Summerfield, C. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02359-3 · Cited by 4

Cognitive flexibility versus stability via activation-based and weight-based adaptations

Xu, S., Verguts, T., & Braem, S. (2026) · Communications Psychology · doi:10.1038/s44271-026-00397-9 · Cited by 5

Assured Learning for Intelligent Dynamic Systems: A Metacognitive Framework

Puente, R., Moncayo, H., Budihartono, M., Mirmirani, M., & Dey, S. (2026) · AIAA SciTech Forum · doi:10.2514/6.2026-1595 · Cited by 1

From Hallucination to Certainty: Meta-Knowledge Guided Self-Correcting Large Language Models

Zhang, W., Dai, G., Luo, D., Wang, Y., & YE, C. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3797906 · Cited by 2

The Tower Foraging Park: A paradigm for studying cognitive and motor processes underlying behavioral flexibility in freely moving mice

Schaffhauser, M., Orjollet--Lacomme, T., Amroune, K., Morvan, T., Fortoul, A., Lechelon, M., & Robbe, D. (2026) · iScience · doi:10.1016/j.isci.2026.116498 · Cited by 1

Design and Analysis of Intelligent Agricultural Monitoring System Based on Biological Intelligence Optimization Algorithm

Chen, Z., Liao, Z., Qian, D., & Li, J. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/1129419 · Cited by 1

Meta-learning. An Approach Applied to Clinical Data

Amador, S., Turbay, K., Lendínez, A. M., Espinilla, M., Mora, H., & Gil, D. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-48642-5_23 · Cited by 1

On-Orbit Proximity Operations About Controlled Uncooperative Targets Using Meta-Predictor

Fu, J., Chen, L., Zhang, D., & Shao, X. (2024) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g007718 · Cited by 1

Meta-learned models as tools to test theories of cognitive development

Nussenbaum, K., & Hartley, C. A. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000281 · Cited by 1

Meta doubly robust: Debiasing CVR prediction via meta-learning with a small amount of unbiased data

Li, P., Tong, X., Wang, Y., & Zhang, Q. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112898 · Cited by 1

Meta-learning contributes to cultivation of wisdom in moral domains: Implications of recent artificial intelligence research and educational considerations

Han, H. (2025) · International Journal of Ethics Education · doi:10.1007/s40889-025-00208-5 · Cited by 2

Compositional pretraining improves computational efficiency and matches animal behavior on complex tasksPreprint

Hocker, D. L., Constantinople, C. M., & Savin, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.12.575461 · Cited by 1

Identifying Transfer Learning in the Reshaping of Inductive BiasesPreprint

Székely, A., Török, B., Kiss, M. M., Janacsek, K., Németh, D., & Orbán, G. (2024) · doi:10.31234/osf.io/jvumf · Cited by 1

Natural and Artificial Intelligence Interactions in Digital Networking:A Multilayer Network Model for Economic Value CreationPreprint

Visconti, R. M. (2024) · Preprints.org · doi:10.20944/preprints202408.2132.v1 · Cited by 1

Structure transfer and consolidation in visual implicit learningPreprint

Garber, D., & Fiser, J. (2025) · eLife · doi:10.7554/elife.100785.2 · Cited by 1

Meta-learning is expressed through altered prefrontal cortical dynamicsPreprint

Sun, X., Comrie, A. E., Kahn, A. E., Monroe, E. J., Washington, C. B., Joshi, A., Guidera, J. A., Denovellis, E. L., Krausz, T. A., Zhou, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.01.697272 · Cited by 1

Transfer Machine Learning Algorithms

Singh, N., & Roy, N. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822548-6.00125-4

Transformação digital aplicada no planejamento de demanda baseado em inteligência artificial

Correia, F. (2022) · doi:10.31414/em.2022.d.131526

Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction

Friess, S., Peter Tiňo, B., Menzel, S., Zhao, X., Sendhoff, B., & Yao, X. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892483

Biologically plausible gated recurrent neural networks for working memory and learning-to-learnPreprint

van den Berg, A. R., Roelfsema, P. R., & Bohté, S. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.06.547911

Label-Guided Compressed Prototypical Network for Incremental Few-Shot Text Classification

Wang, Y., Hu, M., Xu, X., Luo, W., & Luo, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44693-1_16

Meta-Learning on Clinical Data for Diagnosis Support Systems: A Systematic Review

Amador, S., Mora, H., Gil, D., & Ramírez-Gordillo, T. (2024) · Springer proceedings in complexity · doi:10.1007/978-3-031-44721-1_57

Emphasizing Data Quality for the Identification of Chili Varieties in the Context of Smart Agriculture

Suwarningsih, W., Kirana, R., Khotimah, P. H., Rozie, A. F., Riswantini, D., Nugraheni, E., Munandar, D., Arisal, A., & Ahmadi, N. R. (2024) · Interdisciplinary Journal of Information Knowledge and Management · doi:10.28945/5257 · Cited by 1

Machine Learning Opportunities in Flight Test: Preflight Checks

Walker, J., & Claudio, D. (2024) · SN Computer Science · doi:10.1007/s42979-024-02874-6

Research on Artificial Intelligence Algorithm System for Medical Institutions Based on Construction Engineering and E-commerce

Zhao, P., Luo, Z., Yang, G., Cai, W., Li, L., & Qin, Y. (2024) · Highlights in Science Engineering and Technology · doi:10.54097/grnnwa14

Modeling Normal and Abnormal Circuit Development with Recurrent Neural NetworksPreprint

Zavitz, D., Ching, S., & Goodhill, G. J. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041507

Cognitive flexibility versus stability via activation-based and weight-based adaptationsPreprint

Xu, S., Verguts, T., & Braem, S. (2024) · doi:10.31234/osf.io/w8meh

Structure transfer and consolidation in visual implicit learningPreprint

Garber, D., & Fiser, J. (2024) · eLife · doi:10.7554/elife.100785.1

Structure transfer and consolidation in visual implicit learningPreprint

Garber, D., & Fiser, J. (2024) · eLife · doi:10.7554/elife.100785

Meta-learning goes hand-in-hand with metacognition

Fields, C., & Glazebrook, J. F. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000256

Meta-Learning (Learning to Learn): Investigating How Meta-Learning Algorithms Can Improve Learning Efficiency Across Tasks

Kashyap, G. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem39464

Machine Learning Model to Define Water Potability Considering Distinctive Chemical Contaminants

Moheuddin, M. S., Sourov, M. A. K., Ritom, T. T., Hossain, M. I., Jennifer, S. S., Shamim, M. H., & Reza, A. W. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73318-5_3

Biologically plausible gated recurrent neural networks for working memory and learning-to-learn

van den Berg, A. R., Roelfsema, P. R., & Bohté, S. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0316453

Método otimizado para predição da relevância de variáveis e redução de dimensionalidade pautado pela abordagem de seleção Wrapper

Andrade, C. A. (2024) · doi:10.11606/d.55.2024.tde-15012025-113931

CSP-DCPE: Category-Specific Prompt with Deep Contextual Prompt Enhancement for Vision–Language Models

Wu, C., Wu, Y., Xu, Q., & Zi, X. (2025) · Electronics · doi:10.3390/electronics14040673

Structure transfer and consolidation in visual implicit learningPreprint

Garber, D., & Fiser, J. (2025) · eLife · doi:10.7554/elife.100785.3

Divide and conquer: How avian “prefrontal” and hippocampal neurons process extinction learning in complementary waysPreprint

Sevincik, C. S., Packheiser, J., Donoso, J. R., Cheng, S., Rose, J., Güntürkün, O., & Pusch, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.21.644535

Rational causal induction from events in timePreprint

Gong, T., Pacer, M., Griffiths, T. L., & Bramley, N. R. (2025) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/dpkxv_v2

Meta-Learning Strategies for Engineering Education Reform: Leveraging Convolutional Neural Networks and Reinforcement Learning Perspectives

Babavali, S. F., Kavitha, K., Mary, S. S. C., Khan, S. A., Parida, P. K., & Bala, B. K. (2024) · IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST) · doi:10.1109/giest62955.2024.10959771

Climbing the Tower of Meta-mutations - The Role of Higher-Order Mutations

Gašperov, B., & Šter, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90062-4_11

Dynamic Regulation of the Serotonin–Dopamine Interaction Within a Meta-reinforcement Learning Framework Encompassing the Prefrontal Cortex and Basal Ganglia

Robertazzi, F., Vissani, M., & Falotico, E. (2025) · International Journal of Neural Systems · doi:10.1142/s0129065725500406

Artificial Intelligence and Its Impact on the Mental Health Field

Hernández-Lugo, M. D. L. C., Díaz‐Guerra, D. D., & Pérez, G. A. J. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-90921-4_70

Learning when to learn: Context-specific instruction encodingPreprint

Xu, S., Chai, M., Liefooghe, B., Abrahamse, E. L., & Braem, S. (2025) · doi:10.31234/osf.io/7tvx3_v2

Efficient Visual Appearance Optimization by Learning from Prior Preferences

Li, Z., Liao, Y.-C., & Holz, C. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747622

Deciding When to Align: Computational and Neural Mechanisms of Goal-Directed Social Alignment

Sobeh, A., & Shamay‐Tsoory, S. (2025) · Brain Sciences · doi:10.3390/brainsci15111200

Adaptive MLOps Pipeline Optimization via Graph Meta-Learning for Healthcare Models

Jose, J. J., & Leelipushpam, P. G. J. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280338

Meta-Learning Approach, Techniques and Applications: A Review

Khaleel, S., & Mahmood, R. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13921-4_36

Architecting Infinite Realities With MetaIntelligence and Generative AI for Metaverse Creation

Abuhashish, F., Maqableh, W. F., Yousef, N., Sunar, M. S., & Ismail, I. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9958-4.ch006

Cognitive Meta-Learning-Based AI Models for Multimodal Signals

Bose, A., Das, B., Das, C., & Mandal, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40379-8.00011-9

The research on adaptive and machine learning methods in underwater acoustic channel estimation

Zhang, Y. (2022) · theses.fr (ABES) · doi:10.70675/a58a92a6zc496z4ef0z965fza79d4c083149

Learning higher-order functions for computation, memorization and control with artificial neural networks

Chalvidal, M. (2023) · theses.fr (ABES) · doi:10.70675/d35a8a98zd3cfz4df4za6e9zf26f90d6f965

Hierarchical Adaptive Meta‐Learning for Geographic Knowledge Graph Representation Learning

Yao, H. L., Xie, Z., Chen, R., Gao, L., Li, S., Song, H., & Liang, Q. (2026) · Transactions in GIS · doi:10.1111/tgis.70245

3D-MANN: A 3D NAND flash-based reconfigurable architecture for Memory Augmented Neural Networks

Zhou, J., Zhao, Q., Xu, C., Zhang, J., Tian, Y., Chen, K., Li, Q., Wang, Q., Ye, T., & Huo, Z. (2026) · Microelectronics Journal · doi:10.1016/j.mejo.2026.107339

A few-shot image classification method based on Ball k-means algorithm

Bai, R. (2026) · Journal of Computing and Electronic Information Management · doi:10.54097/7pgzq265

Region-adaptive representation learning for geographic knowledge graph

Yao, H. L., Gao, L., Chen, R., Xie, Z., Li, X., Song, H., & Li, S. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109414

The role of AI on the entrepreneurial and innovative mindset of university students in MENA and Canada and the overall impact on the student's readiness

Moustafa, A., Rabie, N., Mejri, S., & AlShehhi, M. R. (2026) · Journal of International Students · doi:10.32674/ndnmwe04

Adaptive and Robust Control of Diamond Quantum Sensors via Meta‐Learning

Jauch, I., Tighineanu, P., Tritschler, P., Strohm, T., Fuchs, T., & Jelezko, F. (2026) · Advanced Quantum Technologies · doi:10.1002/qute.70385

Pretraining of Embodied Recurrent Networks Bridges the Gap Between Artificial and Cortical Neural Activities

Bu, X., Jiang, H., Guo, T., Li, H., & Chen, Y. (2026) · Biomimetics · doi:10.3390/biomimetics11080569

Intelligent agents: architectures, learning paradigms, deployment models, and applications

Dritsas, E., & Τρίγκα, Μ. (2026) · Evolutionary Intelligence · doi:10.1007/s12065-026-01259-3

A collaborative framework for rapid model adaptation and deployment based on meta-learning

Wang, J. (2026) · Array · doi:10.1016/j.array.2026.101351

Meta-DR: A Meta-Learning Framework for Optimizing the Selection of Dimensionality Reduction Techniques

Silva, D. J. D., Amorim, L., & Pimentel, B. A. (2026) · IEEE Access · doi:10.1109/access.2026.3744896

Meta-learning, social cognition and consciousness in brains and machines

Langdon, A., Botvinick, M., Nakahara, H., Tanaka, K., Matsumoto, M., & Kanai, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.10.004 · Cited by 87

The reinforcement metalearner as a biologically plausible meta-learning framework

Vriens, T., Horan, M., Gottlieb, J. P., & Silvetti, M. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000219

Leveraging insights from neuroscience to build adaptive artificial intelligence

Mathis, M. W. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02169-w · Cited by 11

Meta-learning and the evolution of cognition

Veit, W., & Browning, H. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000177 · Cited by 1

Meta-learning in active inference

Penacchio, O., & Clemente, A. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x24000074 · Cited by 1