Concepts in a Probabilistic Language of Thought
Name
CBMM-Memo-010.pdf
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902.53 KB
Format
Adobe PDF
Checksum (MD5)
4a532021ac7467c084ae5a742137f97d
Author(s)
Goodman, Noah D.
Tenenbaum, Joshua B.
Gerstenberg, Tobias
Date Issued
June 14, 2014
Publisher
Center for Brains, Minds and Machines (CBMM)
Series/Report no.
CBMM Memo Series;010
Abstract
Knowledge organizes our understanding of the world, determining what we expect given what we have already seen. Our predictive representations have two key properties: they are productive, and they are graded. Productive generalization is possible because our knowledge decomposes into concepts—elements of knowledge that are combined and recombined to describe particular situations. Gradedness is the observable effect of accounting for uncertainty—our knowledge encodes degrees of belief that lead to graded probabilistic predictions. To put this a different way, concepts form a combinatorial system that enables description of many different situations; each such situation specifies a distribution over what we expect to see in the world, given what we have seen. We may think of this system as a probabilistic language of thought (PLoT) in which representations are built from language-like composition of concepts and the content of those representations is a probability distribution on world states. The purpose of this chapter is to formalize these ideas in computational terms, to illustrate key properties of the PLoT approach with a concrete example, and to draw connections with other views of conceptual structure.
Description
Note: The book chapter is reprinted courtesy of The MIT Press, from the forthcoming edited collection “The Conceptual Mind: New Directions in the Study of Concepts” edited by Eric Margolis and Stephen Laurence, print date Spring 2015.
Subjects
Human Knowledge
Development of Intelligence
Probabilistic Language of Thought (PLoT)
Computational Neuroscience
Terms of Use
Attribution-NonCommercial 3.0 United States
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