Jenkins Gavin W, Samuelson Larissa K, Smith Jodi R, Spencer John P
Department of Psychology and DeLTA Center, The University of Iowa.
Cogn Sci. 2015 Mar;39(2):268-306. doi: 10.1111/cogs.12135. Epub 2014 Jun 24.
It is unclear how children learn labels for multiple overlapping categories such as "Labrador," "dog," and "animal." Xu and Tenenbaum (2007a) suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and adults. Here, we report data testing a developmental prediction of the Bayesian model-that more knowledge should lead to narrower category inferences when presented with multiple subordinate exemplars. Two experiments did not support this prediction. Children with more category knowledge showed broader generalization when presented with multiple subordinate exemplars, compared to less knowledgeable children and adults. This implies a U-shaped developmental trend. The Bayesian model was not able to account for these data, even with inputs that reflected the similarity judgments of children. We discuss implications for the Bayesian model, including a combined Bayesian/morphological knowledge account that could explain the demonstrated U-shaped trend.
目前尚不清楚儿童是如何学习诸如“拉布拉多犬”“狗”和“动物”等多个重叠类别的标签的。徐和特南鲍姆(2007a)认为,学习者借助贝叶斯推理来推断正确的含义。他们在一个贝叶斯模型中阐述了这些观点,并用学龄前儿童和成年人进行了测试。在此,我们报告了对贝叶斯模型的一个发展性预测进行测试的数据——当呈现多个从属范例时,更多的知识应该会导致更窄的类别推断。两项实验均不支持这一预测。与知识较少的儿童和成年人相比,拥有更多类别知识的儿童在面对多个从属范例时表现出更广泛的泛化。这意味着一种U形的发展趋势。即使输入反映了儿童的相似性判断,贝叶斯模型也无法解释这些数据。我们讨论了对贝叶斯模型的影响,包括一个可以解释所展示的U形趋势的贝叶斯/形态学知识综合解释。