Exploring the conceptual structure of nouns and verbs

Concepts are essential to mental life - they allow us to think about the world, and to communicate these thoughts. Much of psycholinguistic research converges on the view that the meaning of a concept can be decomposed into individual features (eg a tiger has eyes, is carnivorous, etc). Within this framework, the statistical properties of individual features determine how concepts are processed in language tasks. For example, the higher the number of features (NOF) in a concept, and the more highly correlated the features (feature correlation; eg many animals have the features has eyes and has ears), the faster the concept is processed. Moreover, highly distinctive features (eg a cow's udder) facilitate subsequent recognition of their concept. However, most of these claims have been based on data from one type of concept only - concrete nouns. One major class of concepts that has been neglected yet is central to our thinking about the world is verbs. In a series of four behavioural experiments, we aim to examine whether variables that affect how nouns are represented and processed (feature correlation, feature distinctiveness and NOF) apply to verbs as well. The results of these studies will provide the foundation of a more comprehensive model of conceptual knowledge across word classes.

概念是心理活动的核心要素——它们使我们能够思考世界并传达这些想法。心理语言学研究普遍认为，概念的含义可分解为若干独立特征（例如，老虎有眼睛、是食肉动物等）。在此框架下，单个特征的统计属性决定了概念在语言任务中的加工过程。例如，概念中的特征数量（NOF）越多，特征间的相关性（feature correlation）越高（如许多动物同时具有“有眼睛”和“有耳朵”的特征），概念的加工速度就越快。此外，高度独特的特征（如奶牛的乳房）能促进对其概念的后续识别。然而，这些结论大多仅基于一类概念的数据——具体名词。动词作为另一大类概念，虽被忽视却对我们认识世界至关重要。通过四项行为实验，我们旨在探究影响名词表征与加工的变量（特征相关性、特征独特性及特征数量）是否同样适用于动词。这些研究结果将为构建跨词类的全面概念知识模型奠定基础。