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

描述（由申请人提供）：在自然主义的学习环境中，有无数可能的单词与世界的配对。先前解决这一映射问题的研究主要集中在单一时刻的语言、社会和表征约束。拟议的研究提出，不确定性问题是否也可以用另一种方式解决，不是在单个试验中，而是在多个试验中，不是在与一个词和潜在指代物的单一接触中，而是在跨情境中。我们认为，基于计算跨单词、跨指称、最重要的是跨这两者共现的分布统计的跨情境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歧义。因此，本研究的重点是：(1)记录10- 16个月婴儿的跨情境学习；(2)通过理论模拟和实验研究调查这种学习背后的各种机制；(3)研究统计学习如何建立在自身积累的基础上。理解这些机制以及它们如何出错或得到支持，对于理解延迟或改变早期词汇学习的发展性语言障碍的起源无疑是至关重要的。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。这需要对环境和统计学习过程的结构和规律有原则性的理解。

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.