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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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在单语和双语儿童的发育语言障碍分类认知措施:机器学习方法.

Jade Plym1,2, Federico Màlato3, Pekka Lahti-Nuuttila1,2

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机器学习模型使用认知评估电池在单语和双语儿童中准确识别了发育性语言障碍 (DLD). 然而,在单语数据上训练的模型在双语儿童中表现不佳,突出了语言发展的差异.

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科学领域:

  • 儿童语言获取 儿童语言获取
  • 发展心理学 发展心理学
  • 计算语言学 计算语言学

背景情况:

  • 发育性语言障碍 (DLD) 影响儿童的语言技能.
  • 在单语和双语儿童中区分DLD与典型发育 (TD) 提出了独特的挑战.
  • 机器学习 (ML) 提供了对DLD的客观分类的潜力.

研究的目的:

  • 用ML评估认知评估电池的准确性,以区分单语和双语儿童的DLD与TD.
  • 评估在单语数据上训练的ML模型的可转移性,以分类双语儿童.
  • 为了确定语言和非语言任务在DLD分类中的相对重要性.

主要方法:

  • 使用随机森林ML分类模型.
  • 分析了4至7岁的单语言和顺序双语儿童的DLD (n=167) 或TD (n=127) 的数据.
  • 采用认知评估电池测量语言和认知领域.

主要成果:

  • 认知评估电池在单语者中获得了DLD/TD分类的高准确性 (91.3%),在双语者中获得了良好的准确性 (84.7%).
  • 在单语数据上训练的ML模型在应用于双语儿童时显示出更低的准确性 (66.0%).
  • 语言处理和口头推理任务对分类至关重要;非语言任务提供最小的改进.

结论:

  • 机理学习模型可以有效地使用特定的认知评估对单语和双语儿童进行DLD分类.
  • 将双语儿童的表现与单语儿童的表现直接比较是不合适的.
  • 需要进一步的研究来澄清非语言函数在DLD检测中的作用.