There’s a paper in the December 2007 issue of Psychological Science titled “Google and the Mind: Predicting Fluency With PageRank.” Here’s the abstract:
Griffiths, T.L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18(12), 1069-1076.
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.
I don’t really have anything to say, and you can probably gleam everything you need to about the methodology from the abstract, though if you want to delve deeper you can read the paper for free here. If you’re wondering what they conclude about rational models of memory (ala John Anderson), it’s pretty simple. Rational models of memory posit that retrieval is a statistical, and specifically, Bayesian process, and so is an internet search, so human memory and internet search may work in similar ways. I don’t really know enough about internet searches to venture a guess as to how similar they might be to human memory, but I suspect there are important differences even in the statistical processes involved. Anyway, here’s the meat of the paper’s conclusion:
The relationship between PageRank and fluency reported in this article suggests that the analogy between computer-based solutions to information retrieval problems and human memory may be worth pursuing further. In particular, our approach indicates how one can obtain novel models of human memory by studying the properties of successful information-retrieval systems, such as Internet search engines. Establishing this correspondence is important not just for the hypotheses about human cognition that may result, but as a path toward developing better search engines.