Investigating the Dynamics of Semantic Processing

点击翻译按钮获取中文摘要

The issue addressed in the proposed research is how word meaning is derived. That is, when a word is perceived, how is its meaning brought to mind? This seemingly simple process remains one of the great puzzles in Cognitive Science. In the proposed research I will approach this issue by capitalizing on two facts: a) there is variability in the meaning or semantic information associated with different words, and b) there are behavioral and neural consequences of this variability. That is, words vary in the richness of their meanings, and our research shows that this variability in semantic richness has consequences for word recognition and for memory. Furthermore, one can define semantic richness in many different ways, and as a function of many different conceptualizations of semantics. By examining the extent to which these different richness effects are observed, in recognition behavior and in neural activity and as a function of context, it is possible to test the viability of the various frameworks for semantic processing. It has traditionally been assumed that semantic representations are quite stable, such that the process of generating word meaning involves activation of a fixed set of properties or features. Alternatively, it has been argued that the process of generating word meaning is at least to some degree context dependent such that context determines the meaning information activated. There is recent evidence that the process of deriving meaning from words is a dynamic one; the process varies as a function of task context, and past experience. This is a more flexible view of semantic processing, where a concept's representation is not static but rather is generated in a context-dependent process. In the proposed research I will investigate how context modulates lexical semantics, so that we may better understand how meaning activation is the result of interaction between stimulus-specific and task-specific processes. The work will provide important insights about current theories of semantic representation and will guide future theory development.