Publications

Abstract

Language processing is rapidly incremental, but evidence bearing upon this assumption comes from very few languages. In this paper we report on a study of incremental processing in Murrinhpatha, a polysynthetic Australian language, which expresses complex sentence-level meanings in a single verb, the full meaning of which is not clear until the final morph. Forty native Murrinhpatha speakers participated in a visual world eyetracking experiment in which they viewed two complex scenes as they heard a verb describing one of the scenes. The scenes were selected so that the verb describing the target scene had either no overlap with a possible description of the competitor image, or overlapped from the start (onset overlap) or at the end of the verb (rhyme overlap). The results showed that, despite meaning only being clear at the end of the verb, Murrinhpatha speakers made incremental predictions that differed across conditions. The findings demonstrate that processing in polysynthetic languages is rapid and incremental, yet unlike in commonly studied languages like English, speakers make parsing predictions based on information associated with bound morphs rather than discrete words.

Abstract

This paper emphasizes the need for empirical research and objective discovery procedures in semantics, and illustrates a method by which these goals may be obtained. The aim of the methodology described is to provide a description of the internal structure of a semantic field by eliciting the description--in an objective, standardized manner--from a representative group of native speakers. This would produce results that would be equally obtainable by any linguist using the same method under the same conditions with a similarly representative set of informants. The standardized method suggested by the author is the Semantic Differential developed by C. E. Osgood in the 1950's. Applying this method to semantic research, it is further hypothesized that, should different members of a semantic field be employed as concepts on a Semantic Differential task, a factor analysis of the results would reveal the dimensions operative within the body of data. The author demonstrates the use of the Semantic Differential and factor analysis in an actual experiment.