期刊论文详细信息
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS 卷:92
Meta-analytic evidence for a core problem solving network across multiple representational domains
Review
Bartley, Jessica E.1  Boeving, Emily R.2  Riedel, Michael C.1  Bottenhorn, Katherine L.2  Salo, Taylor2  Eickhoff, Simon B.3,4  Brewe, Eric5,6,7  Sutherland, Matthew T.2  Laird, Angela R.1 
[1] Florida Int Univ, Dept Phys, AHC4 310,Modesto Maidique Campus, Miami, FL 33199 USA
[2] Florida Int Univ, Dept Psychol, Miami, FL 33199 USA
[3] Heinrich Heine Univ Dusseldorf, Med Fac, Inst Syst Neurosci, Dusseldorf, Germany
[4] Res Ctr Julich, Inst Neurosci & Med, Brain & Behav INM 7, Julich, Germany
[5] Florida Int Univ, Dept Teaching & Learning, Miami, FL 33199 USA
[6] Drexel Univ, Dept Phys, Philadelphia, PA 19104 USA
[7] Drexel Univ, Dept Educ, Philadelphia, PA 19104 USA
关键词: Problem solving;    Reasoning;    Cognitive control;    Functional neuroimaging;    Meta-analysis;    Activation likelihood estimation (ALE);    Domain-generality;    Domain-specificity;   
DOI  :  10.1016/j.neubiorev.2018.06.009
来源: Elsevier
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【 摘 要 】

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.

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