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Sex differences in early sensorimotor processing for speech discrimination.

David Thornton1, Ashley W Harkrider2, David E Jenson3

  • 1Gallaudet University, Washington, DC, 20002, USA. David.Thornton@Gallaudet.edu.

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Summary
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Males and females exhibit distinct sensorimotor processing during speech discrimination, with females showing early inhibition and males showing predictive activation. Both sexes adapt to noise, but males show greater neural changes.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Speech Perception

Background:

  • Sensorimotor activity is crucial for speech perception and influenced by context, load, and ability.
  • Listener sex has not been extensively studied as a variable in sensorimotor responses during speech tasks.

Purpose of the Study:

  • To investigate sex differences in sensorimotor activity during speech sound discrimination.
  • To explore how sensorimotor responses differ between sexes in quiet versus noisy environments.

Main Methods:

  • Collected raw electroencephalography (EEG) data from 21 males and 21 females discriminating /ba/ and /da/ sounds.
  • Utilized independent component analysis (ICA) and time-frequency decomposition to analyze sensorimotor mu components.
  • Focused analysis on accurately discriminated trials to identify neural patterns.

Main Results:

  • Females showed stronger early mu-alpha synchronization in quiet, while males exhibited stronger mu-beta desynchronization.
  • Both sexes demonstrated increased early sensorimotor inhibition in noise, indicating sensory gating.
  • Sex differences in neural activation patterns were more pronounced in the difference between quiet and noisy conditions for males.

Conclusions:

  • Early attentional mechanisms for speech discrimination involve sex-specific sensorimotor processing: inhibition in females and predictive activation in males.
  • While both sexes adapt sensorimotor processing to noise, the underlying neural mechanisms differ.
  • These sex-based differences in sensorimotor processing may contribute to variability in neuroimaging data and understanding neurodevelopmental disorders.