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Claire Chambers, Taegh Sokhey, Deborah Gaebler-Spira, Konrad Paul Kording; The development of Bayesian integration in sensorimotor estimation. Journal of Vision 2018;18(12):8. doi: https://doi.org/10.1167/18.12.8.
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© ARVO (1962-2015); The Authors (2016-present)
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.
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