Cross-Situational statistical word learning:behaviors, mechanisms and constraints

描述(由申请者提供)：在自然主义的学习环境中，有无限数量的可能的单词对世界配对。以往解决这一映射问题的研究主要集中在语言、社会和表征三个方面。这项拟议的研究询问，不确定性问题是否也可以通过另一种方式解决，不是在单一的试验中，而是跨试验，不是在与一个单词和潜在指称的单一遭遇中，而是跨情景。我们认为，一种基于计算跨词、跨指称以及最重要的跨词语共现的分布统计的跨情景学习策略可以最终将单个单词映射到正确的指称，尽管在单个学习时刻存在逻辑上的歧义。因此，建议的研究集中在：(1)记录10-16个月大婴儿的跨情境学习，(2)通过理论模拟和实验研究调查这种学习的机制，以及(3)研究统计学习是如何积累起来的。了解这些机制以及它们可能会如何出错或得到支持，对于理解延缓或改变早期词汇学习的发育性语言障碍的起源肯定是至关重要的。实施有利于发育障碍儿童的程序通常涉及改变或突出学习环境的各个方面。这需要对统计学习的环境和过程的结构和规则有一个原则性的理解。

DESCRIPTION (provided by applicant): There are an infinite number of possible word-to-world pairings in naturalistic learning environments. Previous studies to solve this mapping problem focus on linguistic, social, and representational constraints at a single moment. The proposed research asks if the indeterminacy problem may also be solved in another way, not in a single trial, but across trials, not in a single encounter with a word and potential referent but cross-situationally. We argue that a cross-situational learning strategy based on computing distributional statistics across words, across referents, and most importantly across the co-occurrences of these two can ultimately map individual words to the right referents despite the logical ambiguity in individual learning moments. Thus, the proposed research focuses on: (1) documenting cross-situational learning in infants from 10- to 16-months of age, (2) investigating the kinds of mechanisms that underlie this learning through both theoretical simulations and experimental studies, and (3) studying how statistical learning builds on itself accumulatively. Understanding those mechanisms and how they might go wrong or be bolstered are surely fundamental to understanding the origins of developmental language disorders that delay or alter early lexical learning. Implementing procedures to benefit children with developmental disorders typically involves altering or highlighting aspects of the learning environment. This requires a principled understanding of the structure and regularities of that environment and processes of statistical learning.