Doctoral Dissertation Research: Discovering Semantic Primitives

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

Many words in language have meanings which require a rich and structured representational system. For instance, the meaning of "most" compares the relative sizes of two sets: a sentence like "Most musicians are happy" is true if the happy musicians outnumber the unhappy musicians. However, the representational system which supports these types of set comparisons is not well-understood. Indeed, there are often multiple ways a computational system could realize such meanings. For "most," one might compare the number of happy musicians to the number of unhappy musicians, or to half the total number of musicians (e.g. Hackl 2009, Pietroski et al. 2009). The current project aims to discover the representational system for these types of complex and abstract word meanings. Many results in cognitive science have found that people are biased to learn concepts which are simpler for their representation system: people find it easier to learn a concept like "black chairs" from examples than the more complex "short and black chairs." The current project will teach people novel, language-like concepts. It will use a novel computational model to predict what generalizations people should make in the learning experiment according to different possible representational theories, under the assumption that representational "simplicity" influences learning. This will allow multiple representational systems to be compared to see which best fit human learning patterns. Uncovering the basic operations which underlie linguistic representation is important for understanding the way in which the mind learns and uses complex meanings. Moreover, the representational systems which best describe human learning are likely to provide a good basis for artificial systems which use and learn natural language like humans.