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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Word recognition reflects dimension-based statistical learning.

Kaori Idemaru1, Lori L Holt

  • 1Department of East Asian Languages and Literatures, University of Oregon, Eugene, OR 97403, USA. idemaru@uoregon.edu

Journal of Experimental Psychology. Human Perception and Performance
|October 19, 2011
PubMed
Summary
This summary is machine-generated.

Listeners rapidly adjust speech perception to new accents by learning correlations between acoustic cues. This statistical learning dynamically reshapes how we perceive speech, balancing native language patterns with local variations.

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Area of Science:

  • Psycholinguistics
  • Auditory Perception
  • Speech Processing

Background:

  • Speech perception relies on both long-term native language knowledge and short-term adaptation to talker variations like nonnative accents.
  • Listeners must flexibly adjust to acoustic perturbations while maintaining accurate speech recognition.

Purpose of the Study:

  • Investigate dimension-based statistical learning of acoustic correlations in speech perception.
  • Determine if listeners adapt to altered correlations between voice-onset time (VOT) and fundamental frequency (F0) onset in an artificial accent.
  • Examine how this adaptation interacts with pre-existing knowledge of native language regularities.

Main Methods:

  • Participants completed a word recognition task using voice-onset time (VOT) cues.
  • Incidental exposure to an artificial accent manipulated the correlation between F0 onset and VOT.
  • Four experiments assessed changes in reliance on acoustic dimensions post-exposure.

Main Results:

  • Listeners demonstrated rapid, dimension-based statistical learning, down-weighting the F0 dimension when its correlation with VOT was perturbed.
  • Adaptation was not a simple mirroring of input statistics; long-term English language regularities persisted.
  • Perceptual space dimensions are dynamically adjusted based on local acoustic experience.

Conclusions:

  • Speech perception involves rapid, online statistical learning of acoustic regularities within speech segments.
  • The acoustic dimensions defining speech perception are flexible and can be rapidly adjusted to local talker idiosyncrasies.
  • Findings extend statistical learning from cross-segment to within-segment speech processing, impacting understanding of accent adaptation.