Department of Psychology, Stanford University, Stanford, California, United States of America.
PLoS One. 2011;6(7):e22501. doi: 10.1371/journal.pone.0022501. Epub 2011 Jul 27.
Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions ("there are three balls") structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions ("balls, there are three") will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children's systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early numerical ability predicts later educational outcomes, this simple discovery may have far-reaching consequences.
尽管数字词在日常口语中很常见,但对于大多数孩子来说,学习它们的含义是一个艰巨而漫长的过程,而这种延迟的来源长期以来一直是探究的主题。孩子们首先识别出可以不数而命名的少数小数量,这进一步引发了关于是否存在特定的、容量有限的系统来表示这些小集合,或者较小和较大的集合是否都由相同的系统表示的进一步争论。在这里,我们提出了一种正式的、计算性的数字学习分析,为这两个难题提供了一个可能的解决方案。该分析表明,一旦考虑到环境和学习识别集合的任务的表示要求,用于学习、表示和区分集合大小的连续系统就可以产生处理过程中的有效不连续性。同时,我们的模拟说明了典型的前置名词语言结构(“有三个球”)如何以对辨别学习帮助不大的方式组织信息,同时表明后置名词结构(“球,有三个”)将有助于这种学习。对三岁儿童的一项培训实验证实了这些预测,表明在提供适当结构的后置名词输入的情况下,儿童对数字的理解和能力会迅速显著提高。我们的模拟和结果揭示了辨别学习如何调整儿童代表小集合的系统,以及其容量限制如何自然地源自学习环境和日益复杂的辨别和代表越来越大的集合的任务的混合。它们还解释了为什么孩子们从父母和教育者通常提供的培训中获益甚微。鉴于我们的干预措施非常有效,实施起来非常容易,而且大量研究表明早期的数字能力如何预测以后的教育成果,因此这一简单的发现可能会产生深远的影响。