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相关概念视频

Language Development01:22

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.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Learning Disabilities01:25

Learning Disabilities

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
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相关实验视频

Updated: Jan 7, 2026

Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism
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用人工智能增强发育性语言障碍识别:使用真实和合成数据开发可解释的查应用程序.

Georgios P Georgiou1,2

  • 1Department of Languages and Literature, University of Nicosia, Nicosia, Cyprus. georgiou.georg@unic.ac.cy.

Journal of autism and developmental disorders
|December 13, 2025
PubMed
概括

这项研究确定了在儿童中检测发育性语言障碍 (DLD) 的关键语言标志物. 一个由人工智能驱动的应用程序被开发和验证,在识别DLD方面显示出高准确性,有助于早期诊断.

关键词:
发育性语言障碍 发育性语言障碍标识 识别 识别 识别机器学习 机器学习选应用程序 选应用程序

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科学领域:

  • 语言学的语言学.
  • 发展心理学 发展心理学
  • 人工智能的人工智能

背景情况:

  • 发育性语言障碍 (DLD) 影响儿童的语言学习.
  • 准确及时诊断DLD对于干预至关重要.
  • 现有的诊断方法可能是耗时和主观的.

研究的目的:

  • 确定区分DLD儿童与典型发育 (TD) 同龄人的语言标记.
  • 为DLD开发一个可解释的人工智能 (AI) 选应用程序.
  • 评估AI选应用程序的可靠性和可行性.

主要方法:

  • 一组30名儿童 (15名DLD,15名TD,年龄在7-10岁) 接受了口头评估.
  • 语言测量包括词汇,形态语法和句子重复.
  • 一个随机的森林分类器被训练在合成数据创建AI选应用程序.

主要成果:

  • 在所有措施中,在DLD和TD群体之间发现了显著的语言差异.
  • 人工智能查应用程序与专家临床诊断有很高的一致性.
  • 该应用程序在识别患有DLD的儿童方面被证明是可靠的.

结论:

  • 特定的语言指标对DLD具有诊断价值.
  • 由人工智能驱动的选解决方案是可行的,并提供实际优势.
  • 该应用程序的可解释性和可扩展性可以改善DLD检测,特别是在资源不足的地区.