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Related Experiment Video

Updated: Jun 24, 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

The evolution of frequency distributions: relating regularization to inductive biases through iterated learning.

Florencia Reali¹, Thomas L Griffiths

¹University of California, Department of Psychology, Berkeley, CA 94720-1650, USA.

|March 31, 2009

Summary

Language learners regularize linguistic structures, supporting innate constraints on language acquisition. This study models iterated learning to show how regularization emerges from weak biases, impacting language evolution.

Related Experiment Videos

Last Updated: Jun 24, 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
Linguistics
Evolutionary Biology

Background:

Learner regularization is crucial for innate language acquisition theories and language evolution.
Formal models are needed to link learner inductive biases to observed regularization behavior.
Iterated learning provides a framework for studying language evolution through successive generations.

Purpose of the Study:

To explore how regular linguistic structures emerge through iterated learning.
To investigate the role of Bayesian agents and inductive biases in regularization.
To experimentally demonstrate how simulated language evolution reveals regularization biases.

Main Methods:

Developed a computational model of iterated learning with Bayesian agents.
Simulated language evolution by passing linguistic output from one agent to another as input.
Conducted three laboratory experiments to observe human regularization behavior in controlled settings.

Main Results:

The iterated learning model demonstrated that regularization occurs with appropriate inductive biases.
Experimental simulations revealed that weak biases can have significant effects on regularization.
Participants showed a tendency to regularize inconsistent word-meaning mappings.

Conclusions:

Iterated learning provides a viable mechanism for the emergence of linguistic regularity.
Even subtle biases towards regularization can drive the evolution of regular languages.
Understanding learner biases is key to explaining language acquisition and evolution.