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https://doi.org/10.1101/136267...
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https://doi.org/10.1167/18.12....
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Journal of Vision
Article . 2018 . Peer-reviewed
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Journal of Vision
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PubMed Central
Article . 2018
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The development of Bayesian integration in sensorimotor estimation

Authors: Chambers, Claire; Sokhey, Taegh; Gaebler-Spira, Deborah; Kording, Konrad;

The development of Bayesian integration in sensorimotor estimation

Abstract

If the brain is inherently Bayesian, then behavior should show the signatures of Bayesian computation from an early stage in life without the need for learning. Children should integrate probabilistic information from prior and likelihood distributions to reach decisions and should be as statistically efficient as adults. To test this idea, we examined the integration of prior and likelihood information in a simple position estimation task comparing children aged 6-11 years and adults. During development, estimation performance became closer to the statistical optimum. Children use likelihood information as well as adults but are limited in their use of priors. This finding suggests that Bayesian behavior is not inherent but learnt over the course of development.

Keywords

Male, Adolescent, Decision Making, Bayes Theorem, Article, Young Adult, Humans, Female, Sensorimotor Cortex, Child, Psychomotor Performance, Probability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Top 10%
Top 10%
Top 10%
Green
gold