Infants learn to navigate the complexity of the physical and social world at an outstanding pace, but how they accomplish this learning is still largely unknown. Recent advances in human and artificial intelligence research propose that a key feature to achieving quick and efficient learning is meta-learning, the ability to make use of prior experiences to learn how to learn better in the future. Here we show that 8-month-old infants successfully engage in meta-learning within very short timespans after being exposed to a new learning environment. We developed a Bayesian model that captures how infants attribute informativity to incoming events, and how this process is optimized by the meta-parameters of their hierarchical models over the task structure. We fitted the model with infants’ gaze behavior during a learning task. Our results reveal how infants actively use past experiences to generate new inductive biases that allow future learning to proceed faster.

meta-learning, infant, computational modeling, eye-tracking
This content is only available as a PDF.

Author notes

Competing Interests: The authors declare no conflict of interests.

© 2023 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
2023
Massachusetts Institute of Technology
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.