Locating perceptual category centers in multi-dimensional stimulus spaces

Article Sidebar

PDF
Published: Oct 24, 2018
Keywords:
phonetics, perception, phonetic categorization

Main Article Content

Eric Oglesbee
Kenneth de Jong

Abstract

Examining phonetic categorization in multi-dimensional stimulus spaces poses a number of practical problems. The traditional method of forced identification of an entire stimulus space becomes prohibitive when the number and size of stimulus dimensions becomes increasingly large. In response to this, Iverson and Evans (2003) proposed an adaptive tracking algorithm for finding best exemplars of vowels in a multi-dimensional space. Their algorithm converged on best exemplars in a relatively small number of trials; however, the search method took advantage of special properties of the vowel space in order to achieve rapid convergence. In this paper, a more general multi-dimensional search algorithm is proposed and analyzed for inherent biases. Results showed that there are no long-term biases in the search method, and that multiple types of useful data are generated. The proposed search method appears to be a viable approach for generating a first approximation of phonetic categorization in multi-dimensional stimulus spaces.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 6 No. 1 (2006)
Section
Articles

The Indiana University Linguistics Club Working Papers (the "Publisher") and Author(s) agree as follows. 
    
    1. Publication and Promotion: In consideration of the Publisher's agreement to publish the Work, Author hereby grants and assigns to Publisher the non-exclusive right to print, publish, reproduce, or distribute the Work throughout the world in all means of expression by any method known or hereafter developed, including electronic format. Author further grants Publisher the right to use Author's name in association with the Work in published form and in advertising and promotional materials. 
    
    2. Copyright: Copyright of the Work remains in the Author's name. 
    
    3. Prior Publication and Attribution: Author agrees not to publish the Work in print form prior to publication of the Work by the Publisher. Author agrees to notify IULCWP before publishing the Work elsewhere. 
    
    4. Author Representations: The Author represents and warrants that the Work: (a) is the Author's original Work and that the Author has full power to enter into this Agreement; (b) does not infringe the copyright or property of another; (c) contains no material that is obscene, libelous, or defamatory. Author shall indemnify and hold Publisher harmless against loss of expenses arising from breach of any such warranties. 
    
    5. Licensing and Reuse: Reuse of the published Work will be governed by a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY https://creativecommons.org/licenses/by-nc/4.0/ ). This lets others remix, tweak, and build upon the Work non-commercially; although new works must acknowledge the original IULCWP publication and be non-commercial, they do not have to be licensed on the same terms.