Ellwood-Lowe Monica E, Sacchet Matthew D, Gotlib Ian H
Department of Psychology, Stanford University, Stanford, CA 94305, USA.
Department of Psychology, Stanford University, Stanford, CA 94305, USA; Neurosciences Program, Stanford University, Stanford, CA 94305, USA.
Dev Cogn Neurosci. 2016 Dec;22:1-8. doi: 10.1016/j.dcn.2016.10.001. Epub 2016 Oct 3.
In the nascent field of the cognitive neuroscience of socioeconomic status (SES), researchers are using neuroimaging to examine how growing up in poverty affects children's neurocognitive development, particularly their language abilities. In this review we highlight difficulties inherent in the frequent use of reverse inference to interpret SES-related abnormalities in brain regions that support language. While there is growing evidence suggesting that SES moderates children's developing brain structure and function, no studies to date have elucidated explicitly how these neural findings are related to variations in children's language abilities, or precisely what it is about SES that underlies or contributes to these differences. This issue is complicated by the fact that SES is confounded with such linguistic factors as cultural language use, first language, and bilingualism. Thus, SES-associated differences in brain regions that support language may not necessarily indicate differences in neurocognitive abilities. In this review we consider the multidimensionality of SES, discuss studies that have found SES-related differences in structure and function in brain regions that support language, and suggest future directions for studies in the area of cognitive neuroscience of SES that are less reliant on reverse inference.
在社会经济地位(SES)认知神经科学这个新兴领域,研究人员正利用神经成像技术来探究贫困环境下成长如何影响儿童的神经认知发展,尤其是他们的语言能力。在本综述中,我们强调频繁使用反向推理来解释支持语言的脑区中与SES相关的异常情况所固有的困难。虽然越来越多的证据表明SES会调节儿童发育中的脑结构和功能,但迄今为止尚无研究明确阐明这些神经学发现如何与儿童语言能力的差异相关,或者SES究竟是什么构成或导致了这些差异的基础。由于SES与文化语言使用、第一语言和双语等语言因素相互混淆,这个问题变得更加复杂。因此,支持语言的脑区中与SES相关的差异不一定表明神经认知能力存在差异。在本综述中,我们考虑了SES的多维度性,讨论了在支持语言的脑区结构和功能方面发现与SES相关差异的研究,并为SES认知神经科学领域中较少依赖反向推理的研究提出了未来方向。