Sandoval Michelle, Patterson Dianne, Dai Huanping, Vance Christopher J, Plante Elena
Department of Speech, Language, and Hearing Sciences, University of Arizona, TucsonAZ, United States.
Front Psychol. 2017 Jul 27;8:1234. doi: 10.3389/fpsyg.2017.01234. eCollection 2017.
The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.
利用从自然语言(俄语名词)中提取的刺激,探索了随着时间推移发生的统计学习的神经基础。输入反映了标记有性别的名词类别的“规则”,而没有让参与者明确意识到他们要学习的内容的性质。在四次连续扫描期间,参与者在听一系列带有性标记的名词时接受扫描,并在每次扫描后立即测试他们的学习情况。尽管没有告知参与者学习任务的性质,但他们在初次接触刺激后就表现出了学习。对大脑数据的独立成分分析揭示了五个与任务相关的子网络。与先前关于单词分割的统计学习研究不同,这项形态学学习任务在学习期间强烈激活了额下回。该区域在多个独立成分中都有体现,表明它作为这种类型学习的网络枢纽发挥作用。此外,结果表明,由统计学习激活的子网络是由输入的性质驱动的,而不是反映一个通用的统计学习系统。