Computational models of development, social influences

Elizabeth Bonawitz, Patrick Shafto

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations


In the article we argue that past Bayesian approaches that model children's learning from data are missing an important element - the role of other people in generating that data. We propose that children take the origin of data into account when learning, which can be understood through ideal observer analyses of the social situation. Moreover, when observing evidence, children are not just learning from others, but also about others. We review recent literature suggesting that children can make inferences about the knowledge and goals of the individual selecting the data and use this knowledge to bolster learning from this evidence.

Original languageEnglish (US)
Pages (from-to)95-100
Number of pages6
JournalCurrent Opinion in Behavioral Sciences
StatePublished - Feb 1 2016

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience


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