The psychometric, behavioral, and neurological role of empirically-identified semantic components

我的研究兴趣是理解单词是如何有意义的（语义学）。在过去，语义学的许多解释都是循环的，因为它们都是根据语义规则、判断或特征来解释的。我特别感兴趣的是自然化的语义学，例如，解释它的条款是兼容的其他工作在生物科学和不作出任何特殊的假设语言作为特殊的。该应用程序特别关注研究人类行为与从大型语料库（共现模型）中预测单词上下文的语义计算模型中提取的主成分的关系。共现模型将大型文本语料库中的目标词的上下文表示为对目标词的上下文进行编码的向量（数字串），从而从词的使用中引导词义。在共现模型中，词向量之间的距离可以用来估计两个词的语义相似度。单个词的向量与来自单个语义类别的多个词的平均向量（我称之为类别定义向量，或CDV）的距离是类别成员关系的良好度量。例如，10种哺乳动物的载体的最近邻居将是其他哺乳动物。最近的共现模型是预测模型，其中使用了一个令人惊讶的简单计算工具（一个简单的三层神经网络）来预测单词的上下文。该模型与动物学习模型Rescorla-Wagner模型密切相关。因此，新的预测模型是令人兴奋的，因为它们表明，词汇语义可以解释使用标准的比较心理学判别学习原则。我将进行一系列实验，帮助我们理解语义模型是如何组织的，以及它们的组织是否反映在人类的心理组织中。这些实验寻找语义成分的行为或神经相关性，这些语义成分是从一个在30亿字文本语料库上训练的语言预测共现模型中数学推导出来的（使用主成分分析）。行为相关性是人类反应的模式，可能反映了语义成分的结构，即实验参与者应该更快地对我们在数学上发现的最重要的方差轴上最强的单词做出决定。神经学上的相关性是与这些变量轴相关的大脑活动模式。这项研究之所以重要，是因为它将为动物学习理论对词汇语义的解释提供行为和神经学上的可验证性测试，从而为将高阶认知与这些完善的、完全指定的、简单的学习模型统一起来开辟了一条道路。

My research interest is in understanding how words have meaning (semantics). In the past many explanations of semantics have been circular since they have been cast in terms of semantic rules, judgments, or features. I am particularly interested in naturalizing' semantics, e.g. explaining it in terms that are compatible with other work in the biological sciences and that do not make any special assumptions about language as special'. This application focuses specifically on studying the relation of human behavior to the principal components extracted from a computational model of semantics derived from predicting word context in a large corpus (a co-occurrence model). Co-occurrence models represent a target word's context in a large corpus of text as a vector (a string of numbers) that encodes the target word's context, thereby bootstrapping word meaning from word usage. In co-occurrence models, the distance between word vectors can be used to estimate semantic similarity of two words. Distances of a single word's vector from the average vector of multiple words from a single semantic category (which I call the category-defining vector, or CDV) are good measure of category membership. For example, the closest neighbours of the vectors for ten mammals will be other mammals. Recent co-occurrence models are prediction models, in which a surprisingly simple computational tool (a simple three-layer neural network) is used to predict a word's context. This model is closely related to an animal learning model, the Rescorla-Wagner model. The new prediction models are therefore exciting because they suggest that lexical semantics may be explicable using standard discriminant learning principles from comparative psychology. I will undertake a series of experiments that can help us understand how semantic models are organized, and whether their organization is mirrored in human psychological organization. These experiments look for either behavioral or neurological correlates of semantic components that have been derived mathematically (using principal components analysis) from a prediction co-occurrence model of language trained on a corpus of three billion words of text. The behavioral correlates are patterns of human response that may reflect the structure of the semantic components, i.e. experimental participants should be faster to make decisions about the words most strongly loaded on the most important axes of variance that we have found mathematically. The neurological correlates are patterns of brain activity that correlate with those axes of variance. The proposed research is important because it will provide tests of the behavioral and neurological plausibility of animal learning theory accounts of lexical semantics, thereby opening a path to unifying higher-order cognition with these well-established, fully-specified, and simple models of learning.