17.3653, Calls: Phonetics, Phonology, Psycholingusitics/USA

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LINGUIST List: Vol-17-3653. Mon Dec 11 2006. ISSN: 1068 - 4875.

Subject: 17.3653, Calls: Phonetics, Phonology, Psycholingusitics/USA

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1)
Date: 10-Dec-2006
From: Andries W Coetzee < coetzee at umich.edu >
Subject: Experimental Approaches to Optimality Theory 

	
-------------------------Message 1 ---------------------------------- 
Date: Mon, 11 Dec 2006 11:32:40
From: Andries W Coetzee < coetzee at umich.edu >
Subject: Experimental Approaches to Optimality Theory 
 


Full Title: Experimental Approaches to Optimality Theory 
Short Title: ExpOT 

Date: 18-May-2007 - 20-May-2007
Location: Ann Arbor, MI, USA 
Contact Person: Andries Coetzee
Meeting Email: expot at umich.edu
Web Site: http://www.ling.lsa.umich.edu/expot/ 

Linguistic Field(s): Phonetics; Phonology; Psycholinguistics 

Call Deadline: 07-Jan-2007 

Meeting Description:

Experimental Approaches to Optimality Theory (ExpOT)

Invited speakers: René Kager (Utrecht) and Joe Pater (UMass) 

Over the past few decades, experimental data have been used increasingly as evidence in phonological theorizing. This is no less true of Optimality Theory (OT) as is evidenced by the growing body of OT literature that uses experimental data. The purpose of this workshop is twofold. On the one hand, we want to investigate the extent to which experimental data can be used to fine-tune OT analyses. On the other hand, we want to consider the challenges that non-categorical experimental data may pose to OT. 

Second Call for Abstracts

Experimental Approaches to Optimality Theory (ExpOT)

Invited speakers: René Kager (Utrecht) and Joe Pater (UMass) 

Over the past few decades, experimental data have been used increasingly as evidence in phonological theorizing. This is no less true of Optimality Theory (OT) as is evidenced by the growing body of OT literature that uses experimental data. The purpose of this workshop is twofold. On the one hand, we want to investigate the extent to which experimental data can be used to fine-tune OT analyses. On the other hand, we want to consider the challenges that non-categorical experimental data may pose to OT.

We invite abstracts for 30 minute talks (with 10 minutes discussion) on any topic that combines experimental approaches with OT. For the purpose of this workshop, we give a broad interpretation to ''experimental approaches'', so that it includes experiments as diverse as psycholinguistic/processing tasks (word-likeness, phoneme identification, lexical decision, etc.), as well acoustic/articulatory experiments. We also do not want to limit contributions to papers that argue for OT. Papers that use experimental evidence to point out shortcomings of OT are equally welcome. Lastly, it is not required that a submission contributes new experimental data. Papers that deal with the general challenges posed to OT by non-categorical experimental data can also be submitted. 

The following are examples of specific topics, but certainly do not exhaust the possibilities: 

(1) Testing analyses through experimentation. It sometimes happens that more than one analysis is possible for some phenomenon, and that more traditional data cannot distinguish the analyses. In such cases, it is often possible to tease apart the analyses with psycholinguistic experiments. For instance, in her analysis of onset clusters, Fleischhacker (2005) uses several kinds of psycholinguistic experiments to argue for an account using faithfulness constraints based on perceptual similarity, and against an account based on sonority driven syllable structure. 

(2) Finding experimental evidence for constraints. Experimental data can also help in determining what the constraints are. Kawahara (to appear) uses several perceptual experiments to argue for the existence of different kinds of Ident[voice]-constraints. Zsiga et al. (2006) uses acoustic data from Tswana to argue against the existence of the constraint *ND. 

(3) Testing hypotheses on the architecture of the grammar. There are certain aspects of the architecture of an OT grammar that allow testing with experimental data. Davidson et al. (2004) uses both articulatory and perceptual experiments to test such basic principles as richness of the base and the initial-sate ranking of M >> F. 

There is also research that points to potential problems in the architecture of OT grammars. Coleman and Pierrehumbert (1997), for instance, show that contrary to what is expected under the assumption of strictness of constraint domination, a nonce form with a single severe constraint violation is not necessarily rated as very bad in word-likeness rating tasks. 

(4) Accounting for gradience in experimental data. Data collected through experiments are non-categorical. Since classic OT is a categorical model of grammar, experimental data pose a problem to classic OT. There have been several proposals for how classic OT can be expanded in order to account for these kinds of non-categorical data (Hayes 2000, Boersma and Hayes 2001, etc.) 

References 

Boersma, Paul and Bruce Hayes. 2001. Empirical tests for the Gradual Learning Algorithm. Linguistic Inquiry, 32. 

Coleman, John and Janet Pierrehumbert. 1997. Stochastic phonological grammars and acceptability. In 3rd Meeting of the ACL Special Interest Group in Computational Phonology: Proceedings of the Workshop, 12 July 1997. Somerset: Association for Computational Linguistics. p. 49-56 

Fleischhacker, Heidi. 2005. Similarity in Phonology: Evidence from Reduplication and Loan Adaptation. Ph.D. Dissertation, UCLA. 

Hayes, Bruce. 2000. Gradient well-formedness in Optimality Theory. In Joost Dekkers, Frank van der Leeuw, and Jeroen van de Weijer, eds. Optimality Theory: Phonology, Syntax and Acquisition. Oxford: Oxford University Press. 

Kawahara, Shigeto. to appear. A faithfulness scale projected from a perceptibility scale: The case of [+voice] in Japanese. Language, 82(3). 

Zsiga, Elizabeth, Maria Gouskova, and One Tlale. On the Status of Voiced Stops in Tswana: Against *ND. In C. Davis, A. Deal, Y. Zabbal, eds. Proceedings of NELS 36. Amherst: GLSA.




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