Poli Francesco, Ghilardi Tommaso, Mars Rogier B, Hinne Max, Hunnius Sabine
Donders Center for Cognition, Radboud University Nijmegen, Nijmegen, The Netherlands.
Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK.
Open Mind (Camb). 2023 Jun 1;7:141-155. doi: 10.1162/opmi_a_00079. eCollection 2023.
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.
婴儿以惊人的速度学习应对物理和社会世界的复杂性，但他们如何完成这种学习在很大程度上仍然未知。人类和人工智能研究的最新进展表明，实现快速高效学习的一个关键特征是元学习，即利用先前经验来学习如何在未来更好地学习的能力。在这里，我们表明，8个月大的婴儿在接触新的学习环境后，能在非常短的时间内成功地进行元学习。我们开发了一个贝叶斯模型，该模型捕捉婴儿如何将信息性归因于传入事件，以及这个过程如何通过他们关于任务结构的层次模型的元参数进行优化。我们在一个学习任务中，将该模型与婴儿的注视行为进行拟合。我们的结果揭示了婴儿如何积极利用过去的经验来产生新的归纳偏差，从而使未来的学习进展得更快。
