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从时间处理到发育性语言障碍:注意差距。

From temporal processing to developmental language disorders: mind the gap.

机构信息

Department of Philosophy and History of Science, University of Athens, , Ano Ilissia Campus, Zografos 157 71, Greece.

出版信息

Philos Trans R Soc Lond B Biol Sci. 2013 Dec 9;369(1634):20130090. doi: 10.1098/rstb.2013.0090. Print 2014.

Abstract

The 'rapid temporal processing' and the 'temporal sampling framework' hypotheses have been proposed to account for the deficits in language and literacy development seen in specific language impairment and dyslexia. This paper reviews these hypotheses and concludes that the proposed causal chains between the presumed auditory processing deficits and the observed behavioural manifestation of the disorders are vague and not well established empirically. Several problems and limitations are identified. Most data concern correlations between distantly related tasks, and there is considerable heterogeneity and variability in performance as well as concerns about reliability and validity. Little attention is paid to the distinction between ostensibly perceptual and metalinguistic tasks or between implicit and explicit modes of performance, yet measures are assumed to be pure indicators of underlying processes or representations. The possibility that diagnostic categories do not refer to causally and behaviourally homogeneous groups needs to be taken seriously, taking into account genetic and neurodevelopmental studies to construct multiple-risk models. To make progress in the field, cognitive models of each task must be specified, including performance domains that are predicted to be deficient versus intact, testing multiple indicators of latent constructs and demonstrating construct reliability and validity.

摘要

“快速时间处理”和“时间采样框架”假说被提出,以解释特定语言损伤和阅读障碍中语言和读写能力发展的缺陷。本文综述了这些假说,并得出结论,所提出的听觉处理缺陷与所观察到的疾病行为表现之间的因果关系链是模糊的,并且在经验上没有得到很好的证实。还确定了几个问题和局限性。大多数数据涉及相关任务之间的相关性,并且在表现上存在相当大的异质性和可变性,以及对可靠性和有效性的关注。很少关注表面上的感知任务和元语言任务之间或内隐和外显表现模式之间的区别,尽管假设这些测量方法是潜在过程或表示的纯指标。需要认真对待诊断类别是否不指因果和行为上同质的群体的可能性,同时考虑遗传和神经发育研究,以构建多种风险模型。为了在该领域取得进展,必须指定每个任务的认知模型,包括预测有缺陷和完整的表现领域,测试潜在结构的多个指标,并证明结构可靠性和有效性。

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