David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Cognitive Science 35 (1):184-197 (2011)
There is a growing consensus that the mental lexicon contains both abstract and word-specific acoustic information. To investigate their relative importance for word recognition, we tested to what extent perceptual learning is word specific or generalizable to other words. In an exposure phase, participants were divided into two groups; each group was semantically biased to interpret an ambiguous Mandarin tone contour as either tone1 or tone2. In a subsequent test phase, the perception of ambiguous contours was dependent on the exposure phase: Participants who heard ambiguous contours as tone1 during exposure were more likely to perceive ambiguous contours as tone1 than participants who heard ambiguous contours as tone2 during exposure. This learning effect was only slightly larger for previously encountered than for not previously encountered words. The results speak for an architecture with prelexical analysis of phonological categories to achieve both lexical access and episodic storage of exemplars
|Keywords||Episodic models Speech perception Lexical tone Phonological abstraction Mandarin Chinese|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Jessica Maye, Richard N. Aslin & Michael K. Tanenhaus (2008). The Weckud Wetch of the Wast: Lexical Adaptation to a Novel Accent. Cognitive Science 32 (3):543-562.
James M. McQueen, Anne Cutler & Dennis Norris (2006). Phonological Abstraction in the Mental Lexicon. Cognitive Science 30 (6):1113-1126.
Dennis Norris (1994). Shortlist: A Connectionist Model of Continuous Speech Recognition. Cognition 52 (3):189-234.
Citations of this work BETA
Holger Mitterer, Odette Scharenborg & James M. McQueen (2013). Phonological Abstraction Without Phonemes in Speech Perception. Cognition 129 (2):356-361.
Similar books and articles
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.
Kenny Smith, Andrew D. M. Smith & Richard A. Blythe (2011). Cross-Situational Learning: An Experimental Study of Word-Learning Mechanisms. Cognitive Science 35 (3):480-498.
George Kachergis, Chen Yu & Richard M. Shiffrin (2013). Actively Learning Object Names Across Ambiguous Situations. Topics in Cognitive Science 5 (1):200-213.
Stanka A. Fitneva & Morten H. Christiansen (2011). Looking in the Wrong Direction Correlates With More Accurate Word Learning. Cognitive Science 35 (2):367-380.
De-Fu Yap, Wing-Chee So, Ju-Min Melvin Yap, Ying-Quan Tan & Ruo-Li Serene Teoh (2011). Iconic Gestures Prime Words. Cognitive Science 35 (1):171-183.
Heather Bortfeld (2004). Which Came First: Infants Learning Language or Motherese? Behavioral and Brain Sciences 27 (4):505-506.
Yanping Liu, Erik D. Reichle & Ding‐Guo Gao (2013). Using Reinforcement Learning to Examine Dynamic Attention Allocation During Reading. Cognitive Science 37 (8):1507-1540.
Dennis Norris, James M. McQueen & Anne Cutler (2000). Merging Information in Speech Recognition: Feedback is Never Necessary. Behavioral and Brain Sciences 23 (3):299-325.
Robert Daland & Janet B. Pierrehumbert (2011). Learning Diphone-Based Segmentation. Cognitive Science 35 (1):119-155.
Keith S. Apfelbaum & Bob McMurray (2011). Using Variability to Guide Dimensional Weighting: Associative Mechanisms in Early Word Learning. Cognitive Science 35 (6):1105-1138.
Sara Finley (2012). Testing the Limits of Long-Distance Learning: Learning Beyond a Three-Segment Window. Cognitive Science 36 (4):740-756.
Katrin Skoruppa & Sharon Peperkamp (2011). Adaptation to Novel Accents: Feature-Based Learning of Context-Sensitive Phonological Regularities. Cognitive Science 35 (2):348-366.
Added to index2010-10-13
Total downloads8 ( #169,856 of 1,100,983 )
Recent downloads (6 months)5 ( #58,761 of 1,100,983 )
How can I increase my downloads?