Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development

Elizabeth Bonawitz, Stephanie Denison, Thomas L. Griffiths, Alison Gopnik

Research output: Contribution to journalComment/debate

39 Scopus citations

Abstract

Although probabilistic models of cognitive development have become increasingly prevalent, one challenge is to account for how children might cope with a potentially vast number of possible hypotheses. We propose that children might address this problem by 'sampling' hypotheses from a probability distribution. We discuss empirical results demonstrating signatures of sampling, which offer an explanation for the variability of children's responses. The sampling hypothesis provides an algorithmic account of how children might address computationally intractable problems and suggests a way to make sense of their 'noisy' behavior.

Original languageEnglish (US)
Pages (from-to)497-500
Number of pages4
JournalTrends in Cognitive Sciences
Volume18
Issue number10
DOIs
StatePublished - Oct 1 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

Keywords

  • Approximate bayesian inference
  • Causal learning
  • Cognitive development
  • Sampling hypothesis

Fingerprint Dive into the research topics of 'Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development'. Together they form a unique fingerprint.

  • Cite this