Graduate studies at Western
Cognitive Science 35 (2):348-366 (2011)
|Abstract||This paper examines whether adults can adapt to novel accents of their native language that contain unfamiliar context-dependent phonological alternations. In two experiments, French participants listen to short stories read in accented speech. Their knowledge of the accents is then tested in a forced-choice identification task. In Experiment 1, two groups of listeners are exposed to newly created French accents in which certain vowels harmonize or disharmonize, respectively, to the rounding of the preceding vowel. Despite the cross-linguistic predominance of vowel harmony over disharmony, the two groups adapt equally well to both accents, suggesting that this typological difference is not reflected in perceptual learning. Experiment 2 further explores the mechanism underlying this type of phonological learning. Participants are exposed to an accent in which some vowels harmonize and others disharmonize, yielding an increased featural complexity. They adapt less well to this regularity, showing that adaptation to novel accents involves feature-based inferences|
|Keywords||Phonological learning Features Accents Speech perception Dialects|
|Categories||categorize this paper)|
|Through your library||Configure|
Similar books and articles
Sara Finley (2012). Testing the Limits of Long-Distance Learning: Learning Beyond a Three-Segment Window. Cognitive Science 36 (4):740-756.
Yong Xu, Kazuhiro Ueda, Takanori Komatsu, Takeshi Okadome, Takashi Hattori, Yasuyuki Sumi & Toyoaki Nishida (2007). WOZ Experiments for Understanding Mutual Adaptation. AI and Society 23 (2):201-212.
Marc Ettlinger, Amy S. Finn & Carla L. Hudson Kam (2011). The Effect of Sonority on Word Segmentation: Evidence for the Use of a Phonological Universal. Cognitive Science 36 (4):655-673.
Robert M. French & Mark Weaver (1998). New-Feature Learning: How Common is It? Behavioral and Brain Sciences 21 (1):26-26.
Holger Mitterer, Yiya Chen & Xiaolin Zhou (2011). Phonological Abstraction in Processing Lexical-Tone Variation: Evidence From a Learning Paradigm. Cognitive Science 35 (1):184-197.
Jennifer Culbertson & Paul Smolensky (2012). A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal. Cognitive Science 36 (8):1468-1498.
Axel Cleeremans (1998). Implicit Learning. Trends in Cognitive Sciences 2 (10):406-416.
Korbinian Moeller, Elise Klein & Hans-Christoph Nuerk (2013). Influences of Cognitive Control on Numerical Cognition—Adaptation by Binding for Implicit Learning. Topics in Cognitive Science 5 (2):335-353.
Michail Lotman (2000). Russian verse. Sign Systems Studies 28:217-240.
Added to index2010-11-30
Total downloads7 ( #142,523 of 738,687 )
Recent downloads (6 months)2 ( #37,337 of 738,687 )
How can I increase my downloads?