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

经验识别的语义成分的心理测量、行为和神经学作用

• 批准号: RGPIN-2018-04679
• 负责人: Westbury, Chris
• 金额: $ 2.11万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668421
• 关键词: psychometric; behavioral; neurological; role; empirically

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.

我的研究兴趣是理解单词如何具有意义（语义）。过去，许多语义解释都是循环的，因为它们是根据语义规则、判断或特征来表达的，我对“自然化”语义特别感兴趣。例如，用与生物科学中的其他工作兼容的术语来解释它，并且不对语言做出任何“特殊”的特殊假设。该应用程序专门致力于研究人类行为与从计算模型中提取的主要成分之间的关​​系。的语义源自预测大型语料库中的单词上下文（共现模型）将大型文本语料库中的目标单词上下文表示为对目标单词上下文进行编码的向量（一串数字），从而引导单词含义。在共现模型中，词向量之间的距离可用于估计两个词的语义相似度。单个词的向量与单个语义的多个词的平均向量的距离。类别（我称之为类别定义向量，或 CDV）是类别成员资格的良好衡量标准，例如，十种哺乳动物的向量的最近邻居将是其他哺乳动物。令人惊讶的简单计算工具（一个简单的三层神经网络）用于预测单词的上下文，该模型与动物学习模型 Rescorla-Wagner 模型密切相关，因此新的预测模型令人兴奋，因为它们表明词汇。语义可以使用比较心理学中的标准判别学习原理来解释。我将进行一系列实验，这些实验可以帮助我们如何组织语义模型，以及它们的组织是否反映在人类心理组织中。语义成分的相关性是从在 30 亿个文本单词的语料库上训练的语言预测共现模型以数学方式推导出来的（使用主成分分析）。行为相关性是人类反应的模式，可以反映语言的结构。语义成分，也就是说，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负载最强烈的单词做出决定。神经相关性是与这些方差轴相关的大脑活动模式。拟议的研究很重要，因为。它将提供对词汇语义的动物学习理论解释的行为和神经学合理性的测试，从而开辟一条将高阶认知与这些完善的、完全指定的和简单的学习模型相统一的道路。