A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Emergence of analogy from relation learning
2019
Proceedings of the National Academy of Sciences of the United States of America
By middle childhood, humans are able to learn abstract semantic relations (e.g., antonym, synonym, category membership) and use them to reason by analogy. A deep theoretical challenge is to show how such abstract relations can arise from nonrelational inputs, thereby providing key elements of a protosymbolic representation system. We have developed a computational model that exploits the potential synergy between deep learning from "big data" (to create semantic features for individual words)
doi:10.1073/pnas.1814779116
pmid:30770443
pmcid:PMC6410800
fatcat:iz73e4gvgrhwhomomuqnkdsvnu