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Related Experiment Videos

Google and the mind: predicting fluency with PageRank.

Thomas L Griffiths1, Mark Steyvers, Alana Firl

  • 1Department of Psychology, University of California, Berkeley, Berkeley, CA 94720-1650, USA. tom_griffiths@berkeley.edu

Psychological Science
|November 23, 2007
PubMed
Summary
This summary is machine-generated.

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Human memory and search engines share information retrieval challenges. Researchers found that PageRank, a search engine algorithm, could predict human word recall in a fluency task, suggesting similar cognitive processes.

Area of Science:

  • Cognitive Psychology
  • Computer Science
  • Information Retrieval

Background:

  • Human memory and internet search engines both solve the problem of retrieving stored information.
  • The computational mechanisms underlying these retrieval processes are not fully understood.

Purpose of the Study:

  • To investigate if internet search engine algorithms can predict human memory performance.
  • To compare the predictive power of PageRank against other metrics like word frequency.

Main Methods:

  • A word-fluency task was designed where participants recalled the first word that came to mind starting with a given letter.
  • PageRank algorithm was computed on a semantic network derived from word-association data.
  • Human performance was analyzed against predictions from PageRank, word frequency, and associate counts.

Related Experiment Videos

Main Results:

  • PageRank significantly outperformed word frequency and associate counts in predicting the words participants recalled.
  • This suggests a shared computational principle between human memory retrieval and search engine ranking.

Conclusions:

  • The findings support the hypothesis that human memory and search engines may employ similar strategies for information retrieval.
  • This research opens avenues for developing computational models of human memory based on search engine principles.