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Bootstrapping01:24

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Statistical learning and prosodic bootstrapping differentially affect neural synchronization during speech

Stefan Elmer1, Seyed Abolfazl Valizadeh2, Toni Cunillera3

  • 1Auditory Research Group Zurich (ARGZ), Division Neuropsychology, Institute of Psychology, University of Zurich, Binzmühlestrasse 14/25, Zurich 8050, Switzerland; Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute, L'Hospitalet de Llobregat, Barcelona 08097, Spain.

Neuroimage
|April 13, 2021
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Summary
This summary is machine-generated.

Neural oscillations track speech at syllable and word rates concurrently for segmentation. This brain-to-stimulus alignment aids word learning using statistical cues and lexical stress.

Keywords:
Event-related potentialsFlat speechInter-trial coherenceProsodyWord learning

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Area of Science:

  • Neuroscience
  • Psycholinguistics
  • Cognitive Science

Background:

  • Neural oscillations are key to brain function, aiding speech segmentation and word learning.
  • Current debate exists on whether neural synchronization transitions between syllable and word rates or tracks them concurrently.
  • The role of syllable transition probability in speech segmentation, especially with lexical stress cues, remains unclear.

Purpose of the Study:

  • To investigate whether neural synchronization tracks syllables and words concurrently during speech segmentation.
  • To determine if syllable transition probability influences segmentation when lexical stress is available.
  • To explore the relationship between neural synchronization at different timescales and event-related potentials (ERPs).

Main Methods:

  • Utilized Inter-Trial Coherence (ITC) analyses to measure neural synchronization.
  • Employed Event-Related Potentials (ERPs) to analyze brain responses.
  • Compared neural responses to structured vs. random speech sequences with varying stress patterns.

Main Results:

  • Concurrent neural synchronization to both syllable and word rates was observed during speech segmentation.
  • Word-rate ITC was higher for structured sequences, especially in the 'flat' condition (no stress).
  • Syllable-rate ITC increased over time in the 'flat' condition but not the 'stressed' condition.
  • ITC at both rates correlated in the 'flat' condition, linked to P200 and N400 ERP component changes.

Conclusions:

  • Speech segmentation involves concurrent neural tracking of syllables and words.
  • Distinct computational principles govern neural synchronization based on listening conditions (statistical vs. stress cues).
  • Interactions between neural synchronization and ERPs reveal dynamic speech processing mechanisms.