The development of Bayesian integration in sensorimotor estimation

Claire Chambers, Taegh Sokhey, Deborah Gaebler-Spira, Konrad Paul Kording

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Examining development is important in addressing questions about whether Bayesian principles are hard coded in the brain. If the brain is inherently Bayesian, then behavior should show the signatures of Bayesian computation from an early stage in life. Children should integrate probabilistic information from prior and likelihood distributions to reach decisions and should be as statistically efficient as adults, when individual reliabilities are taken into account. To test this idea, we examined the integration of prior and likelihood information in a simple position-estimation task comparing children ages 6-11 years and adults. Some combination of prior and likelihood was present in the youngest sample tested (6-8 years old), and in most participants a Bayesian model fit the data better than simple baseline models. However, younger subjects tended to have parameters further from the optimal values, and all groups showed considerable biases. Our findings support some level of Bayesian integration in all age groups, with evidence that children use probabilistic quantities less efficiently than adults do during sensorimotor estimation.

Original languageEnglish (US)
Article number8
Pages (from-to)1-16
Number of pages16
JournalJournal of Vision
Volume18
Issue number12
DOIs
StatePublished - Nov 1 2018

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems

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