All trial data from [16].

A fundamental question in word learning is how, given only evidence about what objects a word has previously referred to, children are able to generalize to the correct class. How does a learner end up knowing that “poodle” only picks out a specific subset of dogs rather than the broader class and vice versa? Numerous phenomena have been identified in guiding learner behavior such as the “suspicious coincidence effect” (SCE)—that an increase in the sample size of training objects facilitates more narrow (subordinate) word meanings. While SCE seems to support a class of models based in statistical inference, such rational behavior is, in fact, consistent with a range of algorithmic processes. Notably, the broadness of semantic generalizations is further affected by the temporal manner in which objects are presented—either simultaneously or sequentially. First, I evaluate the experimental evidence on the factors influencing generalization in word learning. A reanalysis of existing data demonstrates that both the number of training objects and their presentation-timing independently affect learning. This independent effect has been obscured by prior literature’s focus on possible interactions between the two. Second, I present a computational model for learning that accounts for both sets of phenomena in a unified way. The Naïve Generalization Model (NGM) offers an explanation of word learning phenomena grounded in category formation. Under the NGM, learning is local and incremental, without the need to perform a global optimization over pre-specified hypotheses. This computational model is tested against human behavior on seven different experimental conditions for word learning, varying over presentation-timing, number, and hierarchical relation between training items. Looking both at qualitative parameter-independent behavior and quantitative parameter-tuned output, these results support the NGM and suggest that rational learning behavior may arise from local, mechanistic processes rather than global statistical inference.

词汇学习（word learning）领域的一项基础性问题是：儿童仅能获取某一单词过往所指代对象的相关证据时，如何能够将该单词推广至正确的类别范畴？学习者最终如何知晓，"贵宾犬（poodle）"仅指代犬类中的特定子类，而非涵盖范围更广的犬类整体？反之亦然？目前学界已发现诸多可引导学习者行为的现象，例如"可疑巧合效应（suspicious coincidence effect, SCE）"——即训练样本量的增加会促使学习者形成更狭窄的下位词（subordinate）词义。虽然可疑巧合效应似乎支持了一类基于统计推理（statistical inference）的模型，但事实上，这类理性行为与多种算法过程均相符。值得注意的是，语义推广的宽泛程度还会受到对象呈现时序方式的影响——即同时呈现或是依次呈现。首先，本文将评估词汇学习中影响语义推广的各类因素的实验证据。对现有数据的重新分析表明，训练对象的数量及其呈现时序均会独立影响学习效果。此前的研究文献聚焦于二者间可能存在的交互作用，从而掩盖了这一独立效应。其次，本文提出了一个可统一解释上述两类现象的学习计算模型。朴素推广模型（Naïve Generalization Model, NGM）为基于类别形成的词汇学习现象提供了解释框架。在朴素推广模型中，学习过程具有局部性与增量性，无需对预先设定的假设空间执行全局优化。该计算模型针对七种不同实验条件下的词汇学习人类行为进行了测试，这些条件在呈现时序、样本数量以及训练项之间的层级关系上均有所差异。无论是从不依赖参数的定性行为，还是经过参数调优的定量输出结果来看，这些结果均支持朴素推广模型，并表明理性学习行为或许源自局部的机械过程，而非全局统计推理。