Meta-analytic evidence for a core problem solving network across multiple representational domains
Jessica E. Bartley, Emily R. Boeving, Michael C. Riedel, Katherine L. Bottenhorn, Taylor Salo, Simon B. Eickhoff, Eric Brewe, Matthew T. Sutherland and Angela R. Laird
Neuroscience and biobehavioral reviews, v 92, pp 318-337
Activation likelihood estimation (ALE) Cognitive control Domain-generality Domain-specificity Functional neuroimaging Meta-analysis Problem solving Reasoning
•Identified meta-analytic brain networks associated with diverse problem solving tasks.•A shared managerial and attentional network supports generalized problem solving.•Problem solving within content areas engages representationally specific sub-networks.•Problem solving relies on cooperation between sub-network and whole-brain systems.
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.