Google and the mind - Predicting fluency with PageRank
Google and the mind - Predicting fluency with PageRank
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DOI：
10.1111/j.1467-9280.2007.02027.x
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发表时间：
2007-12-01
影响因子：
8.2
通讯作者：
Firl, Alana
中科院分区：
文献类型：
--
作者：
Griffiths, Thomas L.;Steyvers, Mark;Firl, Alana
人类记忆和互联网搜索引擎面临着一个共同的计算问题，需要检索存储的信息片段以响应查询。我们探索了他们是否采用了类似的解决方案，测试我们是否可以使用谷歌搜索引擎的一个组件PageRank来预测人类在流畅性任务中的表现。在这项任务中，研究人员向受试者展示了一个字母，并要求他们说出脑海中出现的第一个以该字母开头的单词。我们发现，PageRank，计算从词关联数据构建的语义网络，优于词频和单词的数量，其中一个词被命名为关联作为预测的话，人们在这项任务中产生。我们确定了两个简单的过程模型，可以支持人类记忆和互联网搜索之间的这种明显的对应关系，并将我们的研究结果与以前的理性记忆模型。
Human memory and Internet search engines face a shared computational problem, needing to retrieve stored pieces of information in response to a query. We explored whether they employ similar solutions, testing whether we could predict human performance on a fluency task using PageRank, a component of the Google search engine. In this task, people were shown a letter of the alphabet and asked to name the first word beginning with that letter that came to mind. We show that PageRank, computed on a semantic network constructed from word-association data, outperformed word frequency and the number of words for which a word is named as an associate as a predictor of the words that people produced in this task. We identify two simple process models that could support this apparent correspondence between human memory and Internet search, and relate our results to previous rational models of memory.