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Language Development

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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: Jun 4, 2025

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

Simulating prenatal language exposure in computational models: An exploration study.

María Andrea Cruz Blandón¹, Nayeli Gonzalez-Gomez², Marvin Lavechin³

¹Unit of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland.

|December 19, 2024

Summary

This summary is machine-generated.

Prenatal language exposure (PLE) may aid infant language learning, but previous models lacked realism. This study introduces a realistic framework for modeling PLE, revealing its impact on infant language acquisition models.

Keywords:

Child language development Computational modeling Language acquisition Prenatal language exposure

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Last Updated: Jun 4, 2025

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Area of Science:

Developmental Psychology
Computational Linguistics
Auditory Neuroscience

Background:

Infant language acquisition is hypothesized to begin prenatally.
Studies show fetuses and newborns can discriminate native languages.
Previous computational models of prenatal language exposure (PLE) lacked ecological representativeness.

Purpose of the Study:

To develop an ecologically representative framework for modeling PLE.
To simulate language learning with computational models using this framework.
To compare the effects of PLE versus postnatal-only input on infant language phenomena.

Main Methods:

Developed a framework modeling prenatal speech input quantity and quality.
Incorporated empirical estimates of prenatal speech exposure.
Modeled speech signal attenuation to the fetal auditory system.
Conducted unsupervised learning simulations with computational models.

Main Results:

Incorporating PLE affects computational models' language learning outcomes.
Differences observed between full-term and preterm infant models.
Duration of PLE influences model behavior depending on the linguistic task.
PLE inclusion did not enhance model compatibility with empirical infant data.

Conclusions:

Prenatal language exposure is a relevant factor for computational modeling of infant language acquisition.
The developed framework provides a basis for future computational studies on the prenatal period.
Further research is needed to refine models and their alignment with empirical infant data.