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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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Statistical learning for speech segmentation: Age-related changes and underlying mechanisms.

Shekeila D Palmer1, James Hutson1, Sven L Mattys1

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This summary is machine-generated.

Statistical learning (SL) for speech segmentation remains robust with aging. However, challenging conditions reveal age-related declines linked to memory updating and working memory capacity.

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Area of Science:

  • Cognitive Psychology
  • Developmental Psychology
  • Neuroscience

Background:

  • Statistical learning (SL) is crucial for language acquisition and word segmentation.
  • Limited understanding exists regarding how SL changes across the lifespan and interacts with cognitive aging.

Purpose of the Study:

  • To investigate the impact of aging on speech segmentation via SL.
  • To explore the underlying cognitive mechanisms of SL in different age groups.

Main Methods:

  • Young, middle-aged, and older adults were tested on speech segmentation using SL under varying speech rates and cognitive loads.
  • Learning was assessed via a forced-choice task comparing words against part-words or nonwords.
  • Cognitive tests evaluated working memory and executive functions.

Main Results:

  • Speech segmentation by SL demonstrated resilience to aging, with age effects emerging under demanding conditions (part-word discrimination, cognitive load).
  • Performance against part-words correlated with memory updating, while nonword discrimination relied on working memory storage.
  • Age-related declines in memory updating were linked to reduced SL performance in challenging scenarios.

Conclusions:

  • SL involves both implicit and explicit cognitive skills.
  • Age-related changes in SL are associated with selective declines in memory updating and working memory capacity.
  • These findings offer insights into the lifelong development of language processing mechanisms.