Bayesian learning of visual chunks by human observers
Bayesian learning of visual chunks by human observers
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DOI：
10.1073/pnas.0708424105
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发表时间：
2008-02-19
影响因子：
11.1
通讯作者：
Lengyel, Mate
中科院分区：
文献类型：
--
作者：
Orban, Gergoe;Fiser, Jozsef;Lengyel, Mate
任何层次结构化信息的高效和通用处理都需要一种学习机制，将较低级别的特征组合成较高级别的块。我们研究了这种组块机制在人类的视觉模式学习范式。我们开发了一个基于贝叶斯模型比较的理想学习器，它只提取和存储那些最低限度足以编码一组视觉场景的信息块。我们理想的贝叶斯组块学习器不仅再现了人类模式学习领域中大量先前经验发现的结果，而且还做出了我们实验证实的关键预测。根据贝叶斯学习，但与联想学习相反，当样本中的成对统计数据不包含相关信息时，人类的表现远远高于机会。因此，人类通过生成准确而经济的表示来从复杂的视觉模式中提取块，而不是通过对输入的完整相关结构进行编码。
Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input.