Chambers Claire, Sokhey Taegh, Gaebler-Spira Deborah, Kording Konrad Paul
Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
J Vis. 2018 Nov 1;18(12):8. doi: 10.1167/18.12.8.
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.
研究发展对于解决贝叶斯原理是否在大脑中被硬编码的问题很重要。如果大脑本质上是贝叶斯式的，那么行为应该从生命早期就表现出贝叶斯计算的特征。儿童应该整合来自先验分布和似然分布的概率信息来做出决策，并且在考虑个体可靠性时，应该和成年人一样具有统计效率。为了验证这一想法，我们在一项简单的位置估计任务中研究了6至11岁儿童和成年人对先验信息和似然信息的整合情况。在测试的最年幼样本（6至8岁）中存在某种先验和似然的组合，并且在大多数参与者中，贝叶斯模型比简单的基线模型能更好地拟合数据。然而，较年幼的受试者往往具有离最优值更远的参数，并且所有组都表现出相当大的偏差。我们的研究结果支持所有年龄组都存在一定程度的贝叶斯整合，有证据表明在感觉运动估计过程中，儿童比成年人更不有效地使用概率量。
