Some inconsistencies and misnomers in probabilistic information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Information processing in the context of medical care
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval
ACM Computing Surveys (CSUR)
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Journal of the American Society for Information Science and Technology
SACR: Scheduling-Aware Cache Reconfiguration for Real-Time Embedded Systems
VLSID '09 Proceedings of the 2009 22nd International Conference on VLSI Design
Research issues of Internet-integrated cognitive style
Computers in Human Behavior
| Hi-index | 0.00 |
This study aimed to develop a model for predicting the impact of information access using Web searches, on human decision making. Models were constructed using a database of search behaviors and decisions of 75 clinicians, who answered questions about eight scenarios within 80 minutes in a controlled setting at a university computer laboratory. Bayesian models were developed with and without bias factors to account for anchoring, primacy, recency, exposure, and reinforcement decision biases. Prior probabilities were estimated from the population prior, from a personal prior calculated from presearch answers and confidence ratings provided by the participants, from an overall measure of willingness to switch belief before and after searching, and from a willingness to switch belief calculated in each individual scenario. The optimal Bayes model predicted user answers in 73.3% (95% CI: 68.71 to 77.35%) of cases, and incorporated participants' willingness to switch belief before and after searching for each scenario, as well as the decision biases they encounter during the search journey. In most cases, it is possible to predict the impact of a sequence of documents retrieved by a Web search engine on a decision task without reference to the content or structure of the documents, but relying solely on a simple Bayesian model of belief revision. © 2006 Wiley Periodicals, Inc.