IngentaConnect Google and the Mind: Predicting Fluency With PageRank

Authors: Griffiths, Thomas L.¹; Steyvers, Mark²; Firl, Alana³

Source: Psychological Science, Volume 18, Number 12, December 2007 , pp. 1069-1076(8)

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.

Document Type: Research article

DOI: 10.1111/j.1467-9280.2007.02027.x

Affiliations: 1: Department of Psychology, University of California, Berkeley; 2: Department of Cognitive Sciences, University of California, Irvine; and 3: Department of Cognitive and Linguistic Sciences, Brown University

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