Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning

Jason M. Chein , Walter Schneider
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引用次数: 410

Abstract

Functional magnetic resonance imaging and a meta-analysis of prior neuroimaging studies were used to characterize cortical changes resulting from extensive practice and to evaluate a dual-processing account of the neural mechanisms underlying human learning. Three core predictions of the dual processing theory are evaluated: 1) that practice elicits generalized reductions in regional activity by reducing the load on the cognitive control mechanisms that scaffold early learning; 2) that these control mechanisms are domain-general; and 3) that no separate processing pathway emerges as skill develops. To evaluate these predictions, a meta-analysis of prior neuroimaging studies and a within-subjects fMRI experiment contrasting unpracticed to practiced performance in a paired-associate task were conducted. The principal effect of practice was found to be a reduction in the extent and magnitude of activity in a cortical network spanning bilateral dorsal prefrontal, left ventral prefrontal, medial frontal (anterior cingulate), left insular, bilateral parietal, and occipito-temporal (fusiform) areas. These activity reductions are shown to occur in common regions across prior neuroimaging studies and for both verbal and nonverbal paired-associate learning in the present fMRI experiment. The implicated network of brain regions is interpreted as a domain-general system engaged specifically to support novice, but not practiced, performance.

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实践相关变化的神经影像学研究:学习领域通用控制网络的功能磁共振成像和元分析证据
功能性磁共振成像和对先前神经成像研究的荟萃分析被用于描述广泛实践导致的皮层变化,并评估人类学习背后的神经机制的双重处理。本文评估了双重加工理论的三个核心预测:1)实践通过减少支撑早期学习的认知控制机制的负荷,导致区域活动的普遍减少;2)这些控制机制是域通用的;3)随着技能的发展,没有单独的加工途径出现。为了评估这些预测,对先前的神经影像学研究进行了荟萃分析,并进行了受试者内部的fMRI实验,对比了配对联想任务中未练习和练习的表现。研究发现,练习的主要效果是减少了横跨双侧前额叶背侧、左前额叶腹侧、额叶内侧(前扣带)、左岛叶、双侧顶叶和枕颞(梭状体)区域的皮质网络活动的范围和强度。在之前的神经成像研究中,这些活动减少出现在共同区域,在目前的fMRI实验中,语言和非语言配对学习也出现在共同区域。涉及的大脑区域网络被解释为一个专门用于支持新手(而不是熟练的)表现的领域一般系统。
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