Compositional inductive biases in function learning.
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 o...
| Publicado en: | Cognitive Psychology Vol. 99; pp. 44 - 80 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Dec2017
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=126364398&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 126364398 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00100285 COP jtl: Cognitive Psychology issn: 00100285 maglogo: N pubinfo: dt: Dec2017 vid: 99 pid: 735 pub: Academic Press Inc. artinfo: ui: 126364398 10.1016/j.cogpsych.2017.11.002 ppf: 44 ppct: 36 formats: tig: atl: Compositional inductive biases in function learning. aug: au: Schulz, Eric Tenenbaum, Joshua B. Duvenaud, David Speekenbrink, Maarten Gershman, Samuel J. affil: Harvard University, United States Massachusetts Institute of Technology, United States University of Toronto, Canada University College London, United Kingdom su: Cognitive science Pattern perception Gaussian processes Compositionality (Linguistics) Extrapolation sug: subj: Cognitive science Pattern perception Gaussian processes Compositionality (Linguistics) Extrapolation keyword: Compositionality Function learning Gaussian process Pattern recognition Structure search Compositionality Function learning Gaussian process Pattern recognition Structure search ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|