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Compositional inductive biases in function learning.

Eric Schulz1, Joshua B Tenenbaum2, David Duvenaud3

  • 1Harvard University, United States.

Cognitive Psychology
|November 21, 2017
PubMed
Summary
This summary is machine-generated.

Humans intuitively grasp complex functions by breaking them down into simpler components, a concept known as compositionality. This cognitive strategy aids in understanding, predicting, and remembering functional patterns.

Keywords:
CompositionalityFunction learningGaussian processPattern recognitionStructure search

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Area of Science:

  • Cognitive Science
  • Machine Learning
  • Functional Analysis

Background:

  • Understanding how humans learn complex functional structures is a key question in cognitive science.
  • Compositionality, the principle of combining simpler elements to form complex ones, is a fundamental concept in various fields.

Purpose of the Study:

  • To investigate whether humans utilize compositionality for learning and recognizing complex functional structures.
  • To formalize and test a computational model of compositional structure learning.

Main Methods:

  • Developed a Bayesian regression model using a grammar over Gaussian process kernels to represent compositional functions.
  • Compared the proposed compositional model with other structure learning approaches.
  • Conducted experiments to assess human participants' preferences and inductive biases regarding functional patterns.

Main Results:

  • Participants consistently preferred compositional extrapolations and interpolations of functions over non-compositional ones.
  • Experimental results indicated a strong inductive bias towards compositional structure in human cognition.
  • Compositional functions were rated as more predictable, memorable, and less numerous.

Conclusions:

  • Human intuitive understanding of functions is fundamentally compositional.
  • The proposed Bayesian grammar framework provides a viable model for human functional structure learning.
  • Compositionality serves as a core principle underlying human perception and cognition of complex patterns.