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“Natural” stress patterns and dependencies between edge alignment and quantity sensitivity

Published online by Cambridge University Press:  01 January 2026

Megan J. Crowhurst ,
Laura R. Faircloth ,
Allison L. Wetterlin  and
Linda R. Wheeldon
Megan J. Crowhurst*
Affiliation:
Department of Linguistics, University of Texas, Austin, 305 E. 23rd Street STOP B5100, Austin, TX 78712 USA
Laura R. Faircloth*
Affiliation:
Department of Linguistics, University of Texas, Austin, 305 E. 23rd Street STOP B5100, Austin, TX 78712 USA
Allison L. Wetterlin*
Affiliation:
Institutt for fremmedspråk og oversetting, Agder University Kristiansand, Norway
Linda R. Wheeldon*
Affiliation:
Institutt for fremmedspråk og oversetting, Agder University Kristiansand, Norway
*
mcrowhurst@austin.utexas.edu
laura.faircloth1693@gmail.com
allison.wetterlin@uia.no
linda.r.wheeldon@uia.no

Abstract

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We conducted an artificial language learning experiment to study learning asymmetries that might reveal latent preferences relating to, and any dependencies between, the edge aligmnent and quantity sensitivity (QS) parameters in stress patterning. We used a poverty of the stimulus approach to teach American English speakers an unbounded QS stress rule (stress a single CV: syllable) and either a left- or right-aligning QI rule if only light syllables were present. Forms with two CV: syllables were withheld in the learning phase and added in the test phase, forcing participants to choose between left- and right-aligning options for the QS rule. Participants learned the left- and right-edge QI rules equally well, and also the basic QS rule. Response patterns for words with two CV: syllables suggest biases favoring a left-aligning QS rule with a left-edge QI default. Our results also suggest that a left-aligning QS pattern with a right-edge QI default was least favored. We argue that stress patterns shown to be preferred based on evidence from ease-of-learning and participants’ untrained generalizations can be considered more natural than less favored opposing patterns. We suggest that cognitive biases revealed by artificial stress learning studies may have contributed to shaping stress typology.

Keywords

stressartificial language learningquantity sensitivityalignmentnatural sound patterns

Information

Type
Research Article
Information
Phonological Data and Analysis , Volume 4 , Issue 6 , 2022 , pp. 1 - 35
Creative Commons
Creative Common License - CCCreative Common License - BY
Published by the Linguistic Society of America with permission of the authors under a CC BY 3.0 license.
Copyright
Copyright © 2022 Megan J. Crowhurst et al.

Footnotes

*

The authors are grateful to Rajka Smiljanić for recording stimulus materials for the project, and to Scott Myers for his helpful input at various stages of the project. This research was presented at the Manchester Phonology Meeting in May, 2019. We also thank Andrew Nevins for his editorial handling of the submission and the anonymous PDA reviewers for their helpful feedback.

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