Clerkin Elizabeth M, Hart Elizabeth, Rehg James M, Yu Chen, Smith Linda B
Department of Psychological and Brain Science, Indiana University, Bloomington, IN 47203, USA.
Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Philos Trans R Soc Lond B Biol Sci. 2017 Jan 5;372(1711). doi: 10.1098/rstb.2016.0055.
We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present-a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.
基于日常婴儿视角场景的视觉统计，我们为婴儿如何开始单词学习这一未解决问题提供了一种新的解决方案。对8个半月至10个半月大的婴儿在147次家庭用餐活动中用头戴式摄像机拍摄的视频图像中的可见物体进行了分析。发现图像中杂乱地布满了许多不同的可见物体。然而，物体类别的频率分布严重右偏，以至于一小部分物体普遍存在——这一事实可能会大大减少指称歧义问题。这些婴儿以自我为中心的场景中物体的统计结构与计算模型和统计单词-指称学习实验中使用的训练集有显著不同。因此，研究结果也表明有必要重新审视目前关于婴儿如何开始单词学习的解释。本文是主题为“认知科学中统计学习的新前沿”特刊的一部分。
