A constraint -based account of handshape contrast in sign languages

Petra Nichole Eccarius, Purdue University

Abstract

The main goal of this dissertation is to explore the nature of contrast in sign language handshapes. I first demonstrate that the distribution of handshape contrasts is not homogeneous, either within or across sign languages. By using a variety of methodologies (examination of dictionary data, elicited data, and psycholinguistic experimentation), I present examples of differences related to type of contrast (distinctive, active, and prominent—following Clements, 2001); position in the lexical substrata (following the work of Ito and Mester, 1995a, and Brentari and Padden, 2001); iconic relationships (e.g. shape, size, arrangement of parts); and cross-linguistic variation (comparing American Sign Language, Swiss German Sign Language, and Hong Kong Sign Language). ^ I also propose that the distributional differences in handshape contrasts can be explained in terms of a confluence of pressures on language. Using the tenets of Optimality Theory (OT), these differences can be explained by determining how various languages—or lexical components within languages—rank constraints related to those pressures. Specifically, I follow Flemming's (2002) version of OT (Dispersion Theory) in which grammars balance the pressures of articulatory ease and perceptual distinctiveness, as well as the desire to maximize the number of contrasts available for word formation. To this, I propose an additional pressure—one to maintain contrasts borrowed into the language from external sources. These external contrasts can be borrowed from other languages (directly from other sign languages, or indirectly from spoken languages via systems such as fingerspelling), or they can be borrowed from visual aspects of the real world. ^

Degree

Ph.D.

Advisors

Diane K. Brentari, Purdue University.

Subject Area

Language, Linguistics

Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server
.

Share

COinS