The analysis of speech in different temporal integration windows: Cerebral lateralization as 'asymmetric sampling in time'

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The 'asymmetric sampling in time' (AST) hypothesis developed here provides a framework for understanding a range of psychophysical and neuropsychological data on speech perception in the context of a revised cortical functional anatomic model. The AST model is motivated by observations from psychophysics and cognitive neuroscience that speak to the fractionation of auditory processing, in general, and speech perception, in particular. Building on the observations (1) that the speech signal contains more than one time scale relevant to auditory cognition (e.g. time scales commensurate with processing formant transitions versus scales commensurate with syllabicity and intonation contours), and (2) that speech perception is mediated by both left and right auditory cortices, AST suggests a time-based perspective that maintains anatomic symmetry while permitting functional asymmetry. AST proposes that the input speech signal has a neural representation that is bilaterally symmetric at an early representational level. Beyond the initial representation, however, the signal is elaborated asymmetrically in the time domain: left auditory areas preferentially extract information from short (∼20-40 ms) temporal integration windows. The right hemisphere homologues preferentially extract information from long (∼150-250 ms) integration windows. It is suggested that temporal integration is reflected as oscillatory neuronal activity in different frequency bands (gamma, theta).

Original languageEnglish (US)
Pages (from-to)245-255
Number of pages11
JournalSpeech Communication
Issue number1
StatePublished - Aug 2003


  • Auditory cortex
  • Gamma band
  • Hemispheric asymmetry
  • Neural basis of speech
  • Oscillations
  • Temporal integration
  • Theta band
  • Timing

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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