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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Related Experiment Video

Updated: Sep 8, 2025

Infant Auditory Processing and Event-related Brain Oscillations
06:34

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Published on: July 1, 2015

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Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning.

Joseph C Y Lau1,2,3, Alona Fyshe4, Sandra R Waxman1,2

  • 1Department of Psychology, Northwestern University, Evanston, IL, United States.

Frontiers in Psychology
|June 13, 2022
PubMed
Summary

Infants link early language to cognition through rhythm. Machine learning shows rhythmic and spectral acoustic features in vocalizations guide infants in identifying sounds that support cognitive tasks like object categorization.

Keywords:
infant cognitionlanguagemachine learningnon-human vocalizationsrhythm

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Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Bioacoustics

Background:

  • Rhythm is crucial for language acquisition, with sensitivity present from birth.
  • Infants as young as 4 months link listening to their native language (English) with object categorization.
  • This cognitive advantage extends to non-native languages and primate vocalizations with similar rhythmic properties, but not to bird vocalizations.

Purpose of the Study:

  • To investigate if machine learning can identify acoustic properties in vocalizations that enable infants to link them to cognitive tasks.
  • To determine which acoustic features (rhythmic, spectral, pitch) are most effective in classifying vocalizations that support or do not support infant cognition.

Main Methods:

  • A supervised machine learning approach was used to classify vocalizations.
  • The model was trained on vocalizations previously shown to support (English, German, lemur) or not support (Cantonese, zebra-finch) object categorization in 4-month-old infants.
  • Classification performance was assessed based on different acoustic feature sets.

Main Results:

  • Machine learning models successfully distinguished vocalizations supporting cognition from those that do not.
  • Rhythm-relevant acoustic features, particularly principal components derived from them, were highly effective in classification.
  • Temporal envelope components also robustly supported classification.

Conclusions:

  • Infants' early links between vocalizations and cognition are likely supported by perceptual sensitivity to surface acoustic properties.
  • Rhythmic and spectral elements in vocalizations may guide infants in identifying sounds that serve as candidate links to cognition.
  • This suggests a foundational role for acoustic pattern recognition in early language-cognition development.