Haskins Laboratories, New Haven, CT, United States of America; Yale Child Study Center, New Haven, CT, United States of America; Department of Psychology, Yale University, New Haven, CT, United States of America; Department of Psychological Sciences, University of Connecticut, Storrs, CT, United States of America.
Haskins Laboratories, New Haven, CT, United States of America.
Cognition. 2021 Aug;213:104680. doi: 10.1016/j.cognition.2021.104680. Epub 2021 Apr 11.
Word learning entails the mapping of an auditory word-form to its appropriate grammatical category (e.g., noun, verb, adjective), but before that mapping can occur, the naïve learner must infer which of the myriad of possible referents of that word was intended by the speaker. This creates a computational explosion of referential ambiguity referred to as the gavagai problem. In a set of corpus analyses of parent-directed speech to young infants, we describe the distributional information available to early word learners, with a focus on nouns and adjectives that refer to whole objects and object properties. And in two experiments on word-learning in adults spanning seven different distributional conditions, we document how variations in the ratio of novel labels for objects and properties affect the robustness of word learning. Our results suggest that the language input to 6- to 20-month-olds is robustly populated with high-frequency object words and high-frequency property words, but their co-occurrence is sparse. Although this distributional information slightly favors object words over property words, a more plausible account of the whole-object bias in early word learning is the inability to encode the details of an object/event during rapid naming. Our results from adults, presented with novel labels for multi-referent objects in a cross-situational statistical learning paradigm, also reveal this whole-object bias as well as the absence of property-label generalization to novel objects, even when the distribution of labels is shifted almost exclusively to property words. These results are discussed in terms of the relative ease of mapping auditory word-forms to whole objects vs. object properties, thereby limiting the combinatorics of the gavagai problem, especially in infants with immature encoding and memory representation abilities.
词汇学习需要将听觉词形映射到适当的语法类别（例如名词、动词、形容词），但在进行这种映射之前，初学者必须推断说话者想要指代的那个词的无数可能的所指对象中的哪一个。这就产生了一种被称为“ gavagai 问题”的指称歧义的计算爆炸。在对幼儿进行的父母指导言语的一系列语料库分析中，我们描述了早期词汇学习者可用的分布信息，重点是指整个对象和对象属性的名词和形容词。在两个跨越七个不同分布条件的成人词汇学习实验中，我们记录了物体和属性的新标签比例的变化如何影响词汇学习的稳健性。我们的结果表明，6 至 20 个月大的婴儿的语言输入中充斥着高频对象词和高频属性词，但它们的共现稀疏。尽管这种分布信息略微偏向于对象词而不是属性词，但在早期词汇学习中整体对象偏好的更合理解释是在快速命名过程中无法对对象/事件的细节进行编码。我们在成人中的结果，在跨情境统计学习范式中呈现出具有多种指称的新对象的标签，也揭示了这种整体对象偏向，以及属性标签对新对象的缺乏泛化，即使标签的分布几乎完全偏向于属性词。这些结果是根据将听觉词形映射到整体对象与对象属性的相对容易程度来讨论的，从而限制了“ gavagai 问题”的组合，尤其是在编码和记忆表示能力不成熟的婴儿中。
