Journal
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
Volume 31, Issue 2, Pages 124-130Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/09637214211049233
Keywords
brain networks; computational models; deep neural networks; knowledge transfer; variable binding
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A hallmark of human intelligence is the ability to adapt to new situations by applying learned rules to new content. The human brain achieves this through pathways in the parietal cortex that encode the abstract structure of space, events, and tasks, and pathways in the temporal cortex that encode information about specific people, places, and things. Recent neural network models demonstrate how the separation of structure and content can lead to significant improvements in capturing systematic, generative behavior.
A hallmark of human intelligence is the ability to adapt to new situations by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of inferences and actions (generativity). Here, we propose that the human brain accomplishes these feats through pathways in the parietal cortex that encode the abstract structure of space, events, and tasks and pathways in the temporal cortex that encode information about specific people, places, and things (content). Recent neural network models show how the separation of structure and content might emerge through a combination of architectural biases and learning, and these networks show dramatic improvements over previous models in the ability to capture systematic, generative behavior. We close by considering how the hippocampal formation may form integrative memories that enable rapid learning of new structure and content representations.
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