Document Type

Dissertation

Date of Degree

Fall 2011

Degree Name

PhD (Doctor of Philosophy)

Degree In

Psychology

First Advisor

Bob McMurray

Abstract

Listeners use perceptual learning to rapidly adapt to manipulated speech input. Examination of this learning process can reveal the pathways used during speech perception. By assessing generalization of perceptually learned categorization boundaries, others have used perceptual learning to help determine whether abstract units are necessary for listeners and models of speech perception. Here we extend this approach to address the inverse issue of specificity. In these experiments we have sought to discover the levels of specificity for which listeners can learn variation in phonetic contrasts. We find that (1) listeners are able to learn multiple voicing boundaries for different pairs of phonemic contrasts relying on the same feature contrast. (2) Listeners generalize voicing boundaries to untrained continua with the same onset as the trained continua, but generalization to continua with different onsets depends on previous experience with other continua sharing this different onset. (3) Listeners can learn different voicing boundaries for continua with the same CV onset, which suggests that boundaries are lexically-specific. (4) Listeners can learn different voicing boundaries for multiple talkers even when they are not given instructions about talkers and their task does not require talker identification. (5) Listeners retain talker-specific boundaries after training on a new boundary for a second talker, but generalize boundaries across talkers when they have no previous experience with a talker. These results were obtained using a new paradigm for unsupervised perceptual learning in speech. They suggest that models of speech perception must be highly flexible in order to accommodate both specificity and generalization of perceptually learned categorization boundaries.

Keywords

Adjustment, Distributional learning, Perceptual learning, Speech perception, Unsupervised learning, Variability

Pages

xii, 196 pages

Bibliography

Includes bibliographical references (pages 191-196).

Copyright

Copyright 2011 Cheyenne Michele Munson

Included in

Psychology Commons

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