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Related Experiment Video

Updated: Mar 7, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Mathematical Modeling of Learning from an Inconsistent Source: A Nonlinear Approach.

Timmy Ma¹, Natalia L Komarova²

¹Department of Mathematics, University of California Irvine, Irvine, CA, 92697, USA.

Bulletin of Mathematical Biology

|February 15, 2017

Summary

Children learning language can become more fluent than their sources using a new nonlinear model. This frequency boosting property explains how learners regularize inconsistent linguistic inputs, enhancing language acquisition.

Keywords:

Boosting property Frequency boosting Frequency matching Language regularization Reinforcement algorithms

Related Experiment Videos

Last Updated: Mar 7, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Cognitive Science
Computational Linguistics
Developmental Psychology

Background:

Children learning language often encounter inconsistent input from adult speakers.
Existing models struggle to fully explain how learners regularize this input.
Understanding this process is key to modeling language acquisition.

Purpose of the Study:

To present a new interpretation of algorithms for modeling learning from inconsistent sources.
To investigate the process of a learner modifying and regularizing linguistic inputs.
To provide a theoretical explanation for frequency boosting in language learning.

Main Methods:

Developed a nonlinear model generalizing the Bush-Mosteller algorithm.
Utilized an update function relating learner state to input increments.
Derived analytical expressions for learner frequency.
Identified update functions exhibiting frequency boosting.

Main Results:

The model explains the frequency boosting property, where learners exceed source fluency.
Learners increase the frequency of the most common input, surpassing the source.
Analytical expressions quantify learner frequency dynamics.
A class of update functions demonstrating frequency boosting was identified.

Conclusions:

The proposed nonlinear model provides a robust framework for understanding language acquisition from inconsistent sources.
Frequency boosting is a key mechanism explaining how learners regularize input and enhance fluency.
The model has potential applications in understanding learning effects like the Feature-Label-Order effect.