He was playing with one of this year’s hot Christmas gifts, a digital photo frame from Kodak. It had a wondrous list of features — it could display your pictures, send them to a printer, put on a slide show, play your music — and there was probably no consumer on earth better prepared to put it through its paces.
Dr. Norman, a cognitive scientist who is a professor at Northwestern, has been the maestro of gizmos since publishing “The Design of Everyday Things,” his 1988 critique of VCRs no one could program, doors that couldn’t be opened without instructions and other technologies that seemed designed to drive humans crazy.
Besides writing scholarly analyses of gadgets, Dr. Norman has also been testing and building them for companies like Apple and Hewlett-Packard. One of his consulting gigs involved an early version of this very technology on the shelf at Best Buy: a digital photo frame developed for a startup company that was later acquired by Kodak.
“This is not the frame I designed,” Dr. Norman muttered as he tried to navigate the menu on the screen. “It’s bizarre. You have to look at the front while pushing buttons on the back that you can’t see, but there’s a long row of buttons that all feel the same. Are you expected to memorize them?”
A recent article in Psychological Science on using Google’s PageRank algorithim to explore fluency effects in memory.
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.
Volume 18 (2), December 2007, pages 1069-1076.