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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: Apr 5, 2026

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

Prospects for usage-based computational models of grammatical development: argument structure and semantic roles.

Stewart M McCauley¹, Morten H Christiansen¹

¹Department of Psychology, Cornell University, Ithaca, NY, USA.

Wiley Interdisciplinary Reviews. Cognitive Science

|August 27, 2015

Summary

Computational models of language acquisition need to integrate semantics beyond word meaning. This review surveys models incorporating semantic information for grammatical development, arguing for richer environmental input.

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Last Updated: Apr 5, 2026

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

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Published on: September 5, 2019

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Published on: January 29, 2020

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Published on: October 13, 2018

Area of Science:

Cognitive Science
Computational Linguistics
Developmental Psychology

Background:

Current computational models of language development primarily focus on distributional information.
This focus limits understanding of how children learn word meanings beyond single words.
This gap challenges usage-based acquisition models linking grammar and meaning.

Purpose of the Study:

To review computational models of grammatical development that incorporate semantic information.
To address the challenge of modeling semantic acquisition in language development.
To propose future directions for computational accounts of language acquisition.

Main Methods:

Survey of existing computational models.
Analysis of models assigning thematic roles and argument structure.
Review of models incorporating semantic information.

Main Results:

Limited progress in computational models of word meaning acquisition.
Existing models show potential for integrating semantics into grammatical development.
The need for models to utilize environmental and action-based semantic information.

Conclusions:

Computational models must move beyond distributional data to include semantics.
Integrating observable environmental information is crucial for usage-based models.
Future research should explore novel computational approaches to semantics in language acquisition.