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  Compositional Inductive Biases in Function Learning

Schulz, E., Tenenbaum, J., Duvenaud, D., Speekenbrink, M., & Gershman, S. (2017). Compositional Inductive Biases in Function Learning. Cognitive Neuroscience, 99, 44-79. doi:10.1016/j.cogpsych.2017.11.002.

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Schulz, E1, Author           
Tenenbaum, JB, Author
Duvenaud, D, Author
Speekenbrink , M, Author
Gershman, SJ, Author
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1External Organizations, ou_persistent22              

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 Abstract: How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved 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, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional.

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 Dates: 2017-12
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.cogpsych.2017.11.002
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Title: Cognitive Neuroscience
  Abbreviation : Cogn Neurosci
Source Genre: Journal
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Publ. Info: Hove : Psychology Press
Pages: - Volume / Issue: 99 Sequence Number: - Start / End Page: 44 - 79 Identifier: ISSN: 1758-8928
CoNE: https://pure.mpg.de/cone/journals/resource/1758-8928