Compositional inductive biases in function learning.
Cogn Psychol
; 99: 44-79, 2017 12.
Article
em En
| MEDLINE
| ID: mdl-29154187
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Visual de Modelos
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Pensamento
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Aprendizagem
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Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Limite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Cogn Psychol
Ano de publicação:
2017
Tipo de documento:
Article