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Related Experiment Video

Updated: Nov 10, 2025

Infant Auditory Processing and Event-related Brain Oscillations

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

Prediction and error in early infant speech learning: A speech acquisition model.

Jessie S Nixon¹, Fabian Tomaschek¹

¹Quantitative Linguistics Group, Eberhard Karls University of Tübingen, Germany.

|April 2, 2021

Summary

Infants learn speech sounds through error-driven learning, not just statistical clustering. This new model successfully predicts infant speech perception, suggesting a viable approach for understanding early language acquisition.

Keywords:

Delta rule Discriminative learning Error-driven learning Rescorla-Wagner equations Speech acquisition Speech perception Statistical learning

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Last Updated: Nov 10, 2025

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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

Area of Science:

Cognitive Science
Developmental Psychology
Computational Linguistics

Background:

Statistical clustering models are dominant in explaining infant speech sound acquisition.
Recent evidence suggests purely statistical methods are insufficient for modeling early speech perception.
A need exists for alternative computational models of infant language learning.

Purpose of the Study:

To develop and evaluate a computational model of early speech perception using discriminative, error-driven learning.
To investigate if a network without pre-defined linguistic units can learn speech sound expectations.
To simulate infant responses in the high-amplitude sucking paradigm.

Main Methods:

A two-layer neural network trained with Rescorla-Wagner learning equations was employed.
The model processed spectral components from child-directed speech without a priori linguistic units.
Simulations mimicked infant responses to vowel and fricative pairs and continua.

Main Results:

The model successfully discriminated vowel and consonant pairs, aligning with infant data.
Simulated infant responses in the high-amplitude sucking paradigm were accurately predicted.
Discrimination was strongest in the spectrally predicted frequencies.

Conclusions:

Discriminative, error-driven learning offers a viable computational approach to modeling infant speech sound acquisition.
Learned expectations about the acoustic speech signal are crucial for early speech perception.
This model provides a new framework for understanding the mechanisms of infant language development.