Encoding multielement scenes: Statistical learning of visual feature hierarchies
Encoding multielement scenes: Statistical learning of visual feature hierarchies
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DOI：
10.1037/0096-3445.134.4.521
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发表时间：
2005-11-01
影响因子：
4.1
通讯作者：
Aslin, RN
中科院分区：
文献类型：
--
作者：
Fiser, J;Aslin, RN
作者研究了人类成年人如何编码和记忆由递归嵌入的视觉形状组合组成的多元素场景的部分。作者发现，作为较大配置的一部分的形状组合比没有嵌入的同类形状组合更难记住。结合统计学习的基本机制，这种嵌入性约束使得能够开发复杂的新特征，以有效地获取内部表示，而不会在计算上难以处理。由此产生的表示也编码部分和整体通过组块的视觉输入到组件根据其成分的统计一致性。这些结果表明，自举方法的约束统计学习提供了一个统一的框架，调查形成不同的内部表示模式和场景感知。
The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.