EHESS, ENS, PSL Research University, CNRS, INRIA, France.
Cognition. 2018 Apr;173:43-59. doi: 10.1016/j.cognition.2017.11.008. Epub 2018 Jan 8.
Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e., building an effective system that mimics infant's achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results.
信息处理科学（机器学习、可穿戴传感器）的显著进展有望彻底改变认知发展的研究。在这里，我们分析了“反向工程”语言发展的条件，即构建一个能够模拟婴儿成就的有效系统，这将如何有助于我们对早期语言发展的科学理解。我们认为，在计算方面，从玩具问题转移到学习情况的全部复杂性是很重要的，并且尽可能使用婴儿可用的感官信号的真实重建作为输入。在数据方面，必须建立可访问但隐私保护的家庭数据存储库。在心理语言学方面，必须构建特定的测试来比较人类和机器在不同语言水平上的基准。我们讨论了这种方法的可行性，并概述了当前的结果。
