Statistical learning of new visual feature combinations by infants
Statistical learning of new visual feature combinations by infants
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DOI：
10.1073/pnas.232472899
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发表时间：
2002-11-26
影响因子：
11.1
通讯作者：
Aslin, RN
中科院分区：
文献类型：
--
作者：
Fiser, J;Aslin, RN
人类识别几乎无限数量的独特视觉对象的能力必须基于从环境中提取复杂视觉特征的强大而有效的学习机制。为了确定在早期发展过程中是否形成了场景的统计最佳表征，我们使用了9个月大的婴儿的习惯化范式，发现仅仅通过观察多元素场景，他们对这些场景的潜在统计结构变得敏感。在接触大量场景后，婴儿不仅更加关注那些在场景中作为嵌入元素而出现的元素对，而且还关注那些在元素对之间具有更高可预测性（条件概率）的元素对。这些研究结果表明，类似于较低层次的视觉表征，婴儿学习高阶视觉功能的基础上的场景内的元素的统计一致性，从而使他们能够开发一个有效的表示进一步的联想学习。
The ability of humans to recognize a nearly unlimited number of unique visual objects must be based on a robust and efficient learning mechanism that extracts complex visual features from the environment. To determine whether statistically optimal representations of scenes are formed during early development, we used a habituation paradigm with 9-month-old infants and found that, by mere observation of multielement scenes, they become sensitive to the underlying statistical structure of those scenes. After exposure to a large number of scenes, infants paid more attention not only to element pairs that cooccurred more often as embedded elements in the scenes than other pairs, but also to pairs that had higher predictability (conditional probability) between the elements of the pair. These findings suggest that, similar to lower-level visual representations, infants learn higher-order visual features based on the statistical coherence of elements within the scenes, thereby allowing them to develop an efficient representation for further associative learning.