The rate at which young children learn new words astounds care-givers and scientists alike. Most models of early language acquisition have only been tested in laboratory settings using highly controlled stimuli. In a recent publication in Science, Vong et al. decided to take a more naturalistic approach. They collected 61 h of video footage of a toddler wearing a head-mounted camera as they went about their day-to-day life. Video frames — paired with transcribed audio — were extracted and used to train a neural network that they dubbed the ‘Child’s View for Contrastive Learning’ model (CVCL). By contrasting two distributed vectors for words and images, CVCL learnt to match most of the words it was tested on with the appropriate visual element, with an average accuracy of 61%. CVCL also demonstrated a modest ability to recognize novel examples of the same objects, and made correct identifications around 34% of the time. Interestingly, CVCL’s classification accuracy was similar to that of another neural network trained on a more extensive database that contained millions of stimuli. These findings suggest that relatively simple associative learning using limited training cues could be sufficient to enable early word learning.

Original reference: Science 383, 504–511 (2024)

幼儿学习新单词的速度令护理人员和科学家都感到震惊。大多数早期语言习得模型仅在实验室环境中使用高度受控的刺激进行了测试。在最近发表在《科学》杂志上的文章中，Vong 等人。决定采取更自然主义的方法。他们收集了一个戴着头戴式摄像头的幼儿日常生活 61 小时的视频片段。视频帧与转录的音频配对，被提取并用于训练神经网络，他们将其称为“对比学习的儿童视角”模型（CVCL）。通过对比单词和图像的两个分布式向量，CVCL 学会了将测试的大部分单词与适当的视觉元素相匹配，平均准确度为 61%。CVCL 还表现出了识别相同物体的新例子的适度能力，并在大约 34% 的时间内做出了正确的识别。有趣的是，CVCL 的分类精度与另一个在包含数百万个刺激的更广泛数据库上训练的神经网络的分类精度相似。这些发现表明，使用有限的训练线索进行相对简单的联想学习可能足以实现早期单词学习。

原始参考文献： Science 383 , 504–511 (2024)