What you learn is what you see: using eye movements to study infant cross-situational word learning.
What you learn is what you see: using eye movements to study infant cross-situational word learning.
复制标题
所学即所见：利用眼球运动来研究婴儿跨情境单词学习。
DOI：
10.1111/j.1467-7687.2010.00958.x
复制
发表时间：
2011
影响因子：
3.7
通讯作者：
Smith,LindaB
中科院分区：
文献类型：
--
作者：
Yu,Chen;Smith,LindaB
最近的研究表明，成年人和幼儿都具有强大的统计学习能力，可以解决世界到世界的映射问题。然而，使统计学习成为可能和强大的潜在机制尚不清楚。为了对这一问题提供新的见解，本文报告的研究使用眼球跟踪器记录了14个月大婴儿在统计学习任务中的眼动数据。对这种细粒度的时间数据进行了各种测量，如注视持续时间和移动率(注视从一个视觉对象转移到另一个视觉对象的次数)，显示出统计学习能力强和弱的学习者之间不同的眼动模式。此外，还开发了一种信息论测量方法，并将其应用于凝视数据，以量化试探性学习不确定性的程度。接下来，将简单的联想统计学习模型应用于眼动数据，并将这些模拟结果与幼儿的经验结果进行比较，表明两者之间存在很强的相关性。这表明，具有选择性注意的联想学习机制可以为跨情景统计学习提供一个认知上可信的模型。这项工作代表了使用眼动数据推断统计单词学习中潜在的实时过程的第一步。
Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word‐to‐world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment‐by‐moment eye movement data of 14‐month‐old babies in statistical learning tasks. Various measures are applied to such fine‐grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information‐theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross‐situational statistical learning. The work represents the first steps in using eye movement data to infer underlying real‐time processes in statistical word learning.