当前位置: X-MOL 学术Neurosci Biobehav Rev › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Meta-analytic evidence for a core problem solving network across multiple representational domains.
Neuroscience & Biobehavioral Reviews ( IF 8.2 ) Pub Date : 2018-06-23 , DOI: 10.1016/j.neubiorev.2018.06.009
Jessica E Bartley 1 , Emily R Boeving 2 , Michael C Riedel 1 , Katherine L Bottenhorn 2 , Taylor Salo 2 , Simon B Eickhoff 3 , Eric Brewe 4 , Matthew T Sutherland 2 , Angela R Laird 1
Affiliation  

Problem solving is a complex skill engaging multi-stepped reasoning processes to find unknown solutions. The breadth of real-world contexts requiring problem solving is mirrored by a similarly broad, yet unfocused neuroimaging literature, and the domain-general or context-specific brain networks associated with problem solving are not well understood. To more fully characterize those brain networks, we performed activation likelihood estimation meta-analysis on 280 neuroimaging problem solving experiments reporting 3166 foci from 1919 individuals across 131 papers. The general map of problem solving revealed broad fronto-cingulo-parietal convergence, regions similarly identified when considering separate mathematical, verbal, and visuospatial problem solving domain-specific analyses. Conjunction analysis revealed a common network supporting problem solving across diverse contexts, and difference maps distinguished functionally-selective sub-networks specific to task type. Our results suggest cooperation between representationally specialized sub-network and whole-brain systems provide a neural basis for problem solving, with the core network contributing general purpose resources to perform cognitive operations and manage problem demand. Further characterization of cross-network dynamics could inform neuroeducational studies on problem solving skill development.

中文翻译:

跨多个代表性领域的核心问题解决网络的元分析证据。

解决问题是一项复杂的技能,需要多步推理过程才能找到未知的解决方案。需要解决问题的现实环境的广度被类似广泛但尚未聚焦的神经影像文献所反映,并且与问题解决相关的领域通用或特定于上下文的大脑网络还没有得到很好的理解。为了更全面地表征这些大脑网络,我们对280项神经影像问题解决实验进行了活化可能性估计元分析,该实验报告了131篇论文中来自1919名个体的3166个病灶。一般的问题解决方案显示出宽泛的顶峰-顶峰-顶峰收敛,在考虑单独的数学,语言和视觉空间问题解决特定领域的分析时,也可以相似地确定区域。合并分析揭示了一个通用的网络,该网络支持跨不同上下文的问题解决,并且差异图区分了特定于任务类型的功能选择性子网。我们的研究结果表明,具有代表性的专用子网和全脑系统之间的合作为解决问题提供了神经基础,而核心网络贡献了通用资源来执行认知操作和管理问题需求。跨网络动力学的进一步表征可以为解决问题技能发展的神经教育研究提供信息。我们的研究结果表明,具有代表性的专用子网和全脑系统之间的合作为解决问题提供了神经基础,而核心网络贡献了通用资源来执行认知操作和管理问题需求。跨网络动力学的进一步表征可以为解决问题技能发展的神经教育研究提供信息。我们的研究结果表明,具有代表性的专用子网和全脑系统之间的合作为解决问题提供了神经基础,而核心网络贡献了通用资源来执行认知操作和管理问题需求。跨网络动力学的进一步表征可以为解决问题技能发展的神经教育研究提供信息。
更新日期:2018-06-23
down
wechat
bug