Cerebral lateralization and early speech acquisition: A developmental scenario

Yasuyo Minagawa-Kawai, Alejandrina Cristià, Emmanuel Dupoux

Research output: Contribution to journalReview article

85 Citations (Scopus)

Abstract

During the past ten years, research using Near-infrared Spectroscopy (NIRS) to study the developing brain has provided groundbreaking evidence of brain functions in infants. This paper presents a theoretically oriented review of this wealth of evidence, summarizing recent NIRS data on language processing, without neglecting other neuroimaging or behavioral studies in infancy and adulthood. We review three competing classes of hypotheses (i.e. signal-driven, domain-driven, and learning biases hypotheses) regarding the causes of hemispheric specialization for speech processing. We assess the fit between each of these hypotheses and neuroimaging evidence in speech perception and show that none of the three hypotheses can account for the entire set of observations on its own. However, we argue that they provide a good fit when combined within a developmental perspective. According to our proposed scenario, lateralization for language emerges out of the interaction between pre-existing left-right biases in generic auditory processing (signal-driven hypothesis), and a left-hemisphere predominance of particular learning mechanisms (learning-biases hypothesis). As a result of this completed developmental process, the native language is represented in the left hemisphere predominantly. The integrated scenario enables to link infant and adult data, and points to many empirical avenues that need to be explored more systematically.

Original languageEnglish
Pages (from-to)217-232
Number of pages16
JournalDevelopmental Cognitive Neuroscience
Volume1
Issue number3
DOIs
Publication statusPublished - 2011 Jul 1

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Keywords

  • Developmental cerebral lateralization
  • Functional specialization
  • Infancy
  • Near-infrared Spectroscopy (NIRS)
  • Speech perception
  • Temporal cortex

ASJC Scopus subject areas

  • Cognitive Neuroscience

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