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How to Grow a Mind: Statistics, Structure, and Abstraction
2011
Science
In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address
doi:10.1126/science.1192788
pmid:21393536
fatcat:sh4diud5l5g6hkdw7u2eptlpou