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- W4318219818 abstract "Abstract Despite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational neural model with only localist learning laws that is able to find solutions to arbitrary problems. The model and humans perform a multi-step task with arbitrary and changing starting and desired ending states. Using a combination of computational neural modeling, human fMRI, and representational similarity analysis, we show here that the roles of a number of brain regions can be reinterpreted as interacting mechanisms of a control theoretic system. The results suggest a new set of functional perspectives on the orbitofrontal cortex, hippocampus, basal ganglia, anterior temporal lobe, lateral prefrontal cortex, and visual cortex, as well as a new path toward artificial general intelligence." @default.
- W4318219818 created "2023-01-27" @default.
- W4318219818 creator A5001298940 @default.
- W4318219818 creator A5039287542 @default.
- W4318219818 date "2023-01-27" @default.
- W4318219818 modified "2023-10-18" @default.
- W4318219818 title "Foundations of human spatial problem solving" @default.
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- W4318219818 doi "https://doi.org/10.1038/s41598-023-28834-3" @default.
- W4318219818 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36707649" @default.
- W4318219818 hasPublicationYear "2023" @default.
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