Transparent Queries: investigation users' mental models of search engines

By Jack Muramatsu, Wanda Pratt. 2001

121 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, methodology): “…and measure or evaluate mental models (Besnard, Greathead and Baxter 2004; Borgman, 1986; deGreef and Neerincx, 1995; Dodge et al., 2018; Doyle, Radzicki, and Trees, 2008; Kim, et al., 2016; Klein and Hoffman, 2008; Muramatsu and Pratt, 2001; Schaffernicht and Groesser, 2011; Tullio et al., 2007).”
How it works: a field study of non-technical users interacting with an intelligent system (2007, background): “More recently, Muramatsu and Pratt investigated how users’ mental models of search queries could be improved to correctly use concepts such as logical operators and stop words [21].”
How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface (2016, background): “Increased transparency is also associated with fewer misconceptions [16] and higher confidence in system recommendations [29].”
The perfect search engine is not enough: a study of orienteering behavior in directed search (2004, background): “Recent literature suggests that people are bad at understanding the models that search engines use [19], and this finding could suggest why teleporting, in contrast to orienteering, might”
The role of transparency in recommender systems (2002, background): “Muramatsu & Pratt (2001) investigated user mental models for typical query transformations done by search engines during the retrieval process (e.g., stop word removal, term suffix expansion).”
Faceted metadata for image search and browsing (2003, background): “Stemming is not used, both because of the confusion it can cause [11], and because Google does not use it.”