Google and the mind: Predicting fluency with pagerank

Thomas L. Griffiths, Mark Steyvers, Alana Firl

Research output: Contribution to journalArticlepeer-review

144 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1069-1076
Number of pages8
JournalPsychological Science
Volume18
Issue number12
DOIs
StatePublished - Dec 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Psychology

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