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Related Experiment Video

Updated: Jun 1, 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

A computational model of early argument structure acquisition.

Afra Alishahi¹, Suzanne Stevenson

¹Department of Computer Science, University of Toronto.

Cognitive Science

|June 4, 2011

Summary

This study presents a computational model for language acquisition, demonstrating how children learn language rules and exceptions. The model successfully mimics children's learning patterns in verb argument structure.

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Last Updated: Jun 1, 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

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

Cognitive Science of Language
Computational Linguistics
Developmental Psychology

Background:

Understanding how children acquire language, including rules and exceptions, is a key challenge.
Verb argument structure learning is complex due to interacting linguistic factors.
Computational modeling offers a valuable approach to investigate language acquisition mechanisms.

Purpose of the Study:

To develop and present a computational model for representing, acquiring, and using verbs and constructions.
To explore learning mechanisms that can generalize from specific language examples.
To investigate how exceptions to linguistic rules are learned and maintained.

Main Methods:

Utilized a Bayesian framework for computational modeling.
Proposed a novel view of constructions as probabilistic associations between syntactic and semantic features.
Conducted computational experiments using verb usages as input data.

Main Results:

Demonstrated the feasibility of learning general linguistic constructions and their exceptions from individual verb usages.
The model's learning progression over time mirrored key stages observed in child language acquisition.
Successfully represented and acquired verb argument structures computationally.

Conclusions:

The proposed computational model offers insights into the mechanisms underlying linguistic generalization and exception learning in children.
The Bayesian framework provides a robust method for modeling the probabilistic nature of language acquisition.
This work contributes to understanding the cognitive processes involved in mastering complex linguistic structures.