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The Time Course of Spoken Word Learning and Recognition: Studies With Artificial Lexicons
 

Summary: The Time Course of Spoken Word Learning and Recognition:
Studies With Artificial Lexicons
James S. Magnuson
Columbia University
Michael K. Tanenhaus and Richard N. Aslin
University of Rochester
Delphine Dahan
Max Planck Institute for Psycholinguistics
The time course of spoken word recognition depends largely on the frequencies of a word and its
competitors, or neighbors (similar-sounding words). However, variability in natural lexicons makes
systematic analysis of frequency and neighbor similarity difficult. Artificial lexicons were used to
achieve precise control over word frequency and phonological similarity. Eye tracking provided time
course measures of lexical activation and competition (during spoken instructions to perform visually
guided tasks) both during and after word learning, as a function of word frequency, neighbor type, and
neighbor frequency. Apparent shifts from holistic to incremental competitor effects were observed in
adults and neural network simulations, suggesting such shifts reflect general properties of learning rather
than changes in the nature of lexical representations.
Current models of spoken word recognition share a set of core
assumptions that correspond to what Marslen-Wilson (1993)
called the macrostructure of spoken word recognition: As speech

  

Source: Aslin, Richard N. - Department of Brain and Cognitive Sciences, University of Rochester
DeAngelis, Gregory - Department of Brain and Cognitive Sciences, University of Rochester

 

Collections: Biology and Medicine