Probing the compositionality of intuitive functions
Name
CBMM-Memo-048.pdf
Size
815.72 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Schulz, Eric
Tenenbaum, Joshua B.
Duvenaud, David
Speekenbrink, Maarten
Gershman, Samuel J.
Date Issued
May 26, 2016
Publisher
Center for Brains, Minds and Machines (CBMM)
Series/Report no.
CBMM Memo Series;048
Abstract
How do people learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is accomplished by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels. We show that participants prefer compositional over non-compositional function extrapolations, that samples from the human prior over functions are best described by a compositional model, and that people perceive compositional functions as more predictable than their non-compositional but otherwise similar counterparts. We argue that the compositional nature of intuitive functions is consistent with broad principles of human cognition.
Subjects
cognitive science
Development of Intelligence
human cognition
Terms of Use
Attribution-NonCommercial-ShareAlike 3.0 United States
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